Three-dimensional Coordinates Research Articles

FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.

The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubility and permeability, still requires improvement, limiting high-throughput applications of BCS, which is essential for evaluating drug candidate developability and guiding formulation decisions in the early stages of drug development. In recent years, advancements in machine learning (ML) and molecular characterization have revealed the potential of quantitative structure-performance relationships (QSPR) for rapid and accurate in silico BCS classification. The present study aims to develop a web platform for high-throughput BCS classification based on high-performance ML models. Initially, four data sets of BCS-related molecular properties: log S, log P, log D, and log Papp were curated. Subsequently, 6 ML algorithms or deep learning frameworks were employed to construct models, with diverse molecular representations ranging from one-dimensional molecular fingerprints, descriptors, and molecular graphs to three-dimensional molecular spatial coordinates. By comparing different combinations of molecular representations and learning algorithms, LightGBM exhibited excellent performance in solubility prediction, with an R2 of 0.84; AttentiveFP outperformed others in permeability prediction, with R2 values of 0.96 and 0.76 for log P and log D, respectively; and XGBoost was the most accurate for log Papp prediction, with an R2 of 0.71. When externally validated on a marketed drug BCS category data set, the best-performing models achieved classification accuracies of over 77 and 73% for solubility and permeability, respectively. Finally, the well-trained models were embedded into the first ML-based BCS class prediction web platform (x f), enabling pharmaceutical scientists to quickly determine the BCS category of candidate drugs, which will aid in the high-throughput BCS assessment for candidate drugs during the preformulation stage, thereby promoting reduced risk and enhanced efficiency in drug development and regulation.

Research on a Method for Measuring the Pile Height of Materials in Agricultural Product Transport Vehicles Based on Binocular Vision.

The advancement of unloading technology in combine harvesting is crucial for the intelligent development of agricultural machinery. Accurately measuring material pile height in transport vehicles is essential, as uneven accumulation can lead to spillage and voids, reducing loading efficiency. Relying solely on manual observation for measuring stack height can decrease harvesting efficiency and pose safety risks due to driver distraction. This research applies binocular vision to agricultural harvesting, proposing a novel method that uses a stereo matching algorithm to measure material pile height during harvesting. By comparing distance measurements taken in both empty and loaded states, the method determines stack height. A linear regression model processes the stack height data, enhancing measurement accuracy. A binocular vision system was established, applying Zhang's calibration method on the MATLAB (R2019a) platform to correct camera parameters, achieving a calibration error of 0.15 pixels. The study implemented block matching (BM) and semi-global block matching (SGBM) algorithms using the OpenCV (4.8.1) library on the PyCharm (2020.3.5) platform for stereo matching, generating disparity, and pseudo-color maps. Three-dimensional coordinates of key points on the piled material were calculated to measure distances from the vehicle container bottom and material surface to the binocular camera, allowing for the calculation of material pile height. Furthermore, a linear regression model was applied to correct the data, enhancing the accuracy of the measured pile height. The results indicate that by employing binocular stereo vision and stereo matching algorithms, followed by linear regression, this method can accurately calculate material pile height. The average relative error for the BM algorithm was 3.70%, and for the SGBM algorithm, it was 3.35%, both within the acceptable precision range. While the SGBM algorithm was, on average, 46 ms slower than the BM algorithm, both maintained errors under 7% and computation times under 100 ms, meeting the real-time measurement requirements for combine harvesting. In practical operations, this method can effectively measure material pile height in transport vehicles. The choice of matching algorithm should consider container size, material properties, and the balance between measurement time, accuracy, and disparity map completeness. This approach aids in manual adjustment of machinery posture and provides data support for future autonomous master-slave collaborative operations in combine harvesting.

Key point trajectory prediction method of human stochastic posture falls

ABSTRACT The human body will show very complex and diversified posture changes in the process of falling, including body posture, limb position, angle and movement trajectory, etc. The coordinates of the key points of the model are mapped to the three-dimensional space to form a three-dimensional model and obtain the three-dimensional coordinates of the key points; The construction decomposition method is used to calculate the rotation matrix of each key point, and the rotation matrix is solved to obtain the angular displacement data of the key points on different degrees of freedom. The method of curve fitting combined with the weight distribution kernel function based on self-organizing mapping theory is used to obtain the motion trajectory prediction equation of the human body falling in different degrees of freedom at random positions in three-dimensional space, determine the key point trajectory of human random fall behaviour. The experimental results show that the mapped 3D model is consistent with the real human body structure. This method can accurately determine whether the human body falls or squats randomly, and the prediction results of the key points of the human fall are consistent with the actions of the human body after the fall.

Implementation of an indoor optical camera communication and localization fusion system using infrared LED markers.

Recently, with the development of communication and positioning technologies, progressively higher demands have been placed on the timeliness of indoor communication and the effectiveness of indoor positioning. This paper proposes a low-cost and full-time domain coverage indoor communication and localization fusion system that uses an infrared camera to identify mobile LEDs based on region of interest optical camera communication (RoI-OCC) and calculate three-dimensional (3D) world coordinates based on perspective-n-point (PnP) algorithm. An intermediate delimiter modulation-demodulation method is designed to achieve asynchronous OCC. In processing infrared images, a region-growth-based absolute-directional-mean-difference (RG-ADMD) algorithm is developed to detect infrared targets and recover the scale. Additionally, a local-contrast-method-based Lucas-Kanade (LCM-LK) optical flow estimation algorithm is designed to track infrared targets and determine the centroid coordinates of pixels. In a 5×5 m experimental area, the established system can achieve a target detection recall rate of over 85%, the asynchronous OCC with error-free transmission and an average 3D positioning accuracy of 2.01 cm under a LCM-LK tracking algorithm.

Multi-Beam Surveying Ocean Exploration Model and Applications

With the development of artificial intelligence, fiber Bragg grating (FBG) sensing technology has garnered increasing attention. This paper first proposes a surface reconstruction algorithm based on curvature information, which is applicable to two different sensor structures: implanted FBG sensors and whisker array sensors. The design and analysis of these sensors are based on a pure bending model, where the corresponding bending curvature is obtained by measuring the wavelength shift of the fiber Bragg grating at the measurement points. Next, the paper elaborates on the surface reconstruction algorithm for the implanted FBG sensor. This sensor contains FBG sensing points that are evenly distributed. Curvature information corresponding to the position can be obtained based on the direction and magnitude of the wavelength shift. The fiber bending is considered as a connection of multiple arc segments, and the coordinates of the sensing points are calculated in the Cartesian coordinate system using the properties of the tangents. The fiber bending curve is then reconstructed by connecting the arc segments in MATLAB. Finally, the paper provides a detailed introduction to the surface reconstruction algorithm for the whisker array sensor. When the whiskers bend, the FBGs fixed on them act as curvature sensors. The curvature is determined by the FBG wavelength shift, and the three-dimensional coordinates of the whisker tip relative to the base are calculated based on a geometric model. MATLAB is then used to connect the whisker tip coordinates, completing the construction of the surface.

High-Resolution Infrared Reflectance Distribution Measurement Under Variable Temperature Conditions.

The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data.

Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots.

To address the issue of accurately recognizing and locating picking points for tea-picking robots in unstructured environments, a visual positioning method based on RGB-D information fusion is proposed. First, an improved T-YOLOv8n model is proposed, which improves detection and segmentation performance across multi-scale scenes through network architecture and loss function optimizations. In the far-view test set, the detection accuracy of tea buds reached 80.8%; for the near-view test set, the mAP0.5 values for tea stem detection in bounding boxes and masks reached 93.6% and 93.7%, respectively, showing improvements of 9.1% and 14.1% over the baseline model. Secondly, a layered visual servoing strategy for near and far views was designed, integrating the RealSense depth sensor with robotic arm cooperation. This strategy identifies the region of interest (ROI) of the tea bud in the far view and fuses the stem mask information with depth data to calculate the three-dimensional coordinates of the picking point. The experiments show that this method achieved a picking point localization success rate of 86.4%, with a mean depth measurement error of 1.43 mm. The proposed method improves the accuracy of picking point recognition and reduces depth information fluctuations, providing technical support for the intelligent and rapid picking of premium tea.

The generalized STAR modelling with three-dimensional of spatial weight matrix in predicting the Indonesia peatland’s water level

The release rate of CO2 gas can be influenced by peatlands’ physical properties, such as water level and soil moisture, and rainfall. To anticipate the unstable condition which is when the peatland emit more carbon, we developed the Generalized Space Time Autoregressive (GSTAR) model in predicting these physical properties for the following weeks. As the innovation in modelling, the spatial weight matrix was based on three-dimensional coordinates with a modification on the height factor. The data we used are real-time data of water level on the peatlands in Pulang Pisau Regency, Central Kalimantan Province from 20 February 2021 to 18 March 2023. We then used Ordinary Kriging interpolation on the prediction results to create contour maps on different dates. There were empty data on several dates, especially from 24 March until 3 August 2022. To fill the empty data, we used linear interpolation and then we added white noise to the interpolation results. From the data, the water level has a downward trend pattern from around November to September and an upward trend pattern from October to November. Furthermore, we found that the best model for water level was GSTAR (2;0.1) with a modified matrix a=0.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$a=0.1$$\\end{document} and b=1.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b=1.1$$\\end{document}. Based on the predicted water level, there is a risk of changes in the properties of the peatlands in several areas in Pulang Pisau Regency.

A virtual scene reconstruction algorithm for 3D digital images based on machine learning

In order to obtain a better quality image of virtual scene reconstruction of three-dimensional digital image, a virtual scene reconstruction algorithm of three-dimensional digital image based on machine learning is proposed. Firstly, the state information and presentation instructions of the scene are analyzed to obtain the distribution position of the vertices of the mesh model of the image reconstruction. The global image is approximately calculated by the local coordinate method, and the local details are corrected to complete the rendering of the three-dimensional digital image. Then, the space and scale are used as feature points to build a window detection template on the image, and the classifier is applied to suppress discrete feature points and remove redundant features. Finally, the smoothed three-dimensional coordinates are obtained according to the fitting function method to reconstruct the three-dimensional surface, and the local two-dimensional triangle is segmented and mapped to the three-dimensional space to realize the reconstruction of the virtual scene of the three-dimensional digital image. The experimental results show that the algorithm has a fast convergence speed, the reconstructed image details and edge contours are complete, and the overall effect is good.

A Calculation Method of Bearing Balls Rotational Vectors Based on Binocular Vision Three-Dimensional Coordinates Measurement

The rotational speed vectors of the bearing balls affect their service life and running performance. Observing the actual rotational speed of the ball is a prerequisite for revealing its true motion law and conducting sliding behavior simulation analysis. To address the need for accuracy and real-time measurement of spin angular velocity, which is also under high-frequency and high-speed ball motion conditions, a new measurement method of ball rotation vectors based on a binocular vision system is proposed. Firstly, marker points are laid on the balls, and their three-dimensional (3D) coordinates in the camera coordinate system are calculated in real time using the triangulation principle. Secondly, based on the 3D coordinates before and after the movement of the marker point and the trajectory of the ball, the mathematical model of the spin motion of the ball was established. Finally, based on the ball spin motion model, the three-dimensional vision measurement technology was first applied to the measurement of the bearing ball rotation vector through formula derivation, achieving the analysis of bearing ball rolling and sliding characteristics. Experimental results demonstrate that the visual measurement system with the frame rate of 100 FPS (frames per second) yields a measurement error within ±0.2% over a speed range from 5 to 50 RPM (revolutions per minute), and the maximum measurement errors of spin angular velocity and linear velocity are 0.25°/s and 0.028 mm/s, respectively. The experimental results show that this method has good accuracy and stability in measuring the rotation vector of the ball, providing a reference for bearing balls' rotational speed monitoring and the analysis of the sliding behavior of bearing balls.

Soft Tissue Facial Morphology in Growing Patients with Different Occlusal Classes.

The study of facial profiles in the dental field is very important for the diagnosis and the dental and orthodontic treatment plan. The aim of this study is to analyze the three-dimensional morphology of the faces of 269 growing patients with Class I and II occlusions, focusing on children aged between 6 and 9 years old. The analysis was conducted using a non-invasive computerized system, which allowed for the automatic collection of facial landmarks and the subsequent reconstruction of three-dimensional coordinates. The sample comprised 269 children within the specified age range. Each child's facial features were captured using the non-invasive computerized system, which utilized two infrared CCD cameras, real-time hardware for label recognition, and software for three-dimensional landmark reconstruction. Sixteen cutaneous facial landmarks were automatically collected for each participant. From these landmarks, 10 angular and 15 linear measurements, as well as five direct distance rates, were derived. The mean values for each age class were calculated separately for children with bilateral Angle Class I occlusion and compared with those for children with bilateral Class II occlusion. In all children, the left and right occlusal classes were measured as suggested by Katz. The analysis revealed notable differences, primarily in the three-dimensional angular measurements between children with Class I and II occlusions. Specifically, Class II children exhibited more convex faces in the sagittal plane and a less prominent lower jaw compared to Class I children. However, no significant differences were observed in linear measurements, except for the lower facial height rate, which varied inconsistently across age groups between the two occlusion types. the findings of this research highlight distinct three-dimensional facial morphological differences between children with Class I and II occlusions. While Class II children tended to have more convex facial profiles and less prominent lower jaws, linear measurements showed minimal variation between the two occlusion types. These results underscore the importance of three-dimensional analysis in understanding facial morphology in growing patients with different occlusal patterns.

Unsupervised detection and mapping of sparks in the Electrochemical Discharge Machining (ECDM) process

Material removal in electrochemical discharge machining is caused by sparks generated in a tool immersed in an electrolytic solution. Being the primary machining agent in this non-contact machining process, mapping the locations of microscopic sparks is of great interest. The distribution of sparks around the tool surface could give insights into the machined hole properties like the size, surface finish, and depth as compared to the machining parameters such as applied voltage, tool size, rotation speed, and feed rate. This paper is focused on detecting sparks in photographs of the ECDM process captured using a high-speed camera. A novel approach of using a tri-planar reflective surface for capturing the location of sparks in 3D space using a 2D camera output is attempted. Traditional spark detection methods use neural network classifiers that need labeled data for training. This labeled data often comes from human intervention and contains inherent biases that could lead to misclassification. In this paper, an unsupervised spark detection methodology is demonstrated, which eliminates the need for human intervention and relies on the number of neighboring pixels detected in regions of interest (ROIs). The feasibility of using adaptive background modeling to classify thousands of images and identify the ones with sparks is demonstrated in this work. The masking technique combining effects of erosion followed by dilation is used to determine the exact boundaries of the spark contours in every image. Centroids for each of these contours are then transformed from the skewed coordinate system as observed in camera images, to a three-dimensional orthogonal coordinates system centered around the tool. The same procedure is repeated for various voltages to benchmark the distribution of sparks around a tool tip in an ECDM process.

DEVELOPMENT AND DATA ANALYSIS OF A ROBO-PEN FOR ALZHEIMER’S DISEASE DIAGNOSIS: PRELIMINARY RESULTS

Alzheimer’s Disease (AD) poses a significant challenge in contemporary medicine, necessitating early and accurate diagnostic methods to manage its progression effectively. This study explores the development and application of the Robo-pen, an innovative diagnostic tool designed to detect early signs of cognitive decline through detailed handwriting analysis. The Robo-pen, equipped with an MPU-9250 sensor, captures three-dimensional coordinates, velocity, and acceleration of handwriting movements, crucial for assessing spatial control, movement consistency, speed variations, and the ability to modulate movement speed and force–parameters often disrupted in cognitive impairments like AD. Participants included 20 patients diagnosed with AD and 18 healthy controls, matched in age and educational levels. Data collection involved tasks such as sentence rewriting, figure redrawing, and digit rewriting, processed using CoolTerm software at a sampling rate of 18 Hz. Descriptive statistics revealed that the AD group exhibited lower mean values for gyroscope and acceleration data, indicating slower and less variable movements compared to the control group. T-tests confirmed significant differences (p < 0.001) across all measured parameters between the AD and control groups. The results support the potential of the Robo-pen as a non-invasive, cost-effective diagnostic tool for early detection of AD. By capturing subtle neuromotor changes, the Robo-pen facilitates earlier diagnosis and timely intervention, potentially altering the disease trajectory and improving patient outcomes. This study marks a significant advancement in the early detection of AD, highlighting the Robo-pen’s promise as a transformative tool in neurodegenerative disease diagnosis and management.

Innovative 3D Navigation Module for Precise Unilateral Pedicle Screw Combined with Contralateral Translaminar Facet Screw Placement in Lumbar Spine Surgery.

The incidence of degenerative diseases of the lumbar spine has increased in recent years. Unilateral pedicle screw combined with contralateral translaminar facet screw fixation offers the advantages of less trauma, better stability, and fewer complications. However, the surgical difficulty and suboptimal pinning accuracy of translaminar facet screw placement in clinical practice limit its use. Therefore, in this study, we designed a novel suspended 3D-printed navigation module to facilitate fast and accurate intraoperative screw placement. The aim of this study is to investigate the digital design, precise implementation, and evaluation methods for placing unilateral pedicle screws in the lumbar spine combined with translaminar facet screw placement using a new suspended 3D navigation module. This retrospective study included 46 patients with single-level lumbar lesions who underwent spine surgery at the Affiliated Hospital of Putian University between June 2022 and December 2023. The suspended navigation module was designed digitally. Preoperative screw placement was simulated using 3D printed models, followed by an intraoperative accurate screw placement facilitated by the navigation module and a postoperative evaluation of the accuracy of screw placement. The absolute difference in three-dimensional coordinates of the inlet and outlet points of the preoperative design and the postoperative screw-nail channel served as the precision index. In a study involving 46 patients, surgery was successful with 92 pedicle screws and 46 translaminar facet screws placed without any penetration of the cortex. The difference in coordinates before and after screw insertion was minimal, with entry points varying between 1.21 to 1.36 mm and exit points between 1.97 to 2.46 mm. When screw accuracy met certain thresholds, there was no significant difference between preoperative design and postoperative coordinates, indicating precise replication of the surgical plan. The new suspended 3D navigation module enables the precise placement of unilateral pedicle screws in the lumbar spine combined with translaminar pedicle screws for precise surgery.

Error Analysis of Non-Time-Synchronized Lightning Positioning Method

Since the non-time-synchronized lightning positioning method does not rely on the time synchronization of the stations in the positioning system, it eliminates the errors arising from the pursuit of time synchronization and potentially achieves higher positioning accuracy. This paper provides a comprehensive overview of the errors present in the three-dimensional lightning positioning system. It compares the results of traditional positioning methods with those of non-time-synchronized lightning positioning algorithms. Subsequently, a simulation analysis of the positioning errors is conducted specifically for the non-time-synchronized lightning positioning method. The results show that (1) the non-time-synchronized lightning positioning method exhibits greater errors when utilizing two randomly positioned radiation sources for location determination. Consequently, the resulting positioning outcomes only provide a general overview of the lightning discharge. (2) The positioning outcomes resemble those of the traditional method when employing a fixed-coordinate beacon point. However, the errors in the three-dimensional positional coordinates of these fixed-coordinate beacon points significantly impact the deviations in the positioning results. This impact is positively correlated with the positional error of the beacon point, considering both the orientation and magnitude. (3) Similarly to the traditional method, the farther away from the center of the positioning network, the larger the radial error. (4) The spatial position of the selected fixed-coordinate beacon point has little influence on the error.

The visual measurement method of the hole diameter and center-to-center distance of the box based on simplified external parameter calibration

Abstract The hole is a significant mechanical structure type. In this paper, two visual measurement models are established for chamfered and non-chamfered holes. A concentric cylinder with a known diameter is used as a calibration object to obtain the external parameters corresponding to the end face of the measured hole. This approach overcomes limitations associated with using calibration plates for external parameter calibration and aligns better with requirements in production sites. By utilizing the obtained external parameters, three-dimensional coordinates of edge points on the measured hole can be determined, and an ellipse fitting algorithm is employed to obtain aperture and center point coordinates. In the experiment, gearbox housing is used to measure hole diameter and center-to-center distance. The measurements are compared with a coordinate measuring machine, demonstrating a measurement accuracy up to 0.1 mm. The experimental results confirm both feasibility and effectiveness of measurement model while maintaining low operational complexity.

Field Obstacle Detection and Location Method Based on Binocular Vision

When uncrewed agricultural machinery performs autonomous operations in the field, it inevitably encounters obstacles such as persons, livestock, poles, and stones. Therefore, accurate recognition of obstacles in the field environment is an essential function. To ensure the safety and enhance the operational efficiency of autonomous farming equipment, this study proposes an improved YOLOv8-based field obstacle detection model, leveraging depth information obtained from binocular cameras for precise obstacle localization. The improved model incorporates the Large Separable Kernel Attention (LSKA) module to enhance the extraction of field obstacle features. Additionally, the use of a Poly Kernel Inception (PKI) Block reduces model size while improving obstacle detection across various scales. An auxiliary detection head is also added to improve accuracy. Combining the improved model with binocular cameras allows for the detection of obstacles and their three-dimensional coordinates. Experimental results demonstrate that the improved model achieves a mean average precision (mAP) of 91.8%, representing a 3.4% improvement over the original model, while reducing floating-point operations to 7.9 G (Giga). The improved model exhibits significant advantages compared to other algorithms. In localization accuracy tests, the maximum average error and relative error in the 2–10 m range for the distance between the camera and five types of obstacles were 0.16 m and 2.26%. These findings confirm that the designed model meets the requirements for obstacle detection and localization in field environments.

Ground-to-UAV sub-terahertz channel measurement and modeling.

Unmanned aerial vehicle (UAV) assisted wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analysis and modeling specific to terahertz (THz) wireless channels leveraging UAVs remain under-explored. This work delves into a ground-to-UAV channel at 140 GHz, with a specific focus on the influence of UAV hovering behavior on channel performance. Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance. Our findings highlight the minimal impact of UAV orientation adjustments on channel performance and underscore the diminishing necessity for precise alignment between UAVs and ground stations as beamwidth increases.

Rational Polynomial Coefficient Estimation via Adaptive Sparse PCA-Based Method

The Rational Function Model (RFM) is composed of numerous highly correlated Rational Polynomial Coefficients (RPCs), establishing a mathematical relationship between two-dimensional images and three-dimensional spatial coordinates. Due to the existence of ill-posedness and overparameterization, the estimated RPCs are sensitive to any slight perturbations in the observation data, particularly when handling a limited number of Ground Control Points (GCPs). Recently, Principal Component Analysis (PCA) has demonstrated significant performance improvements in the RFM optimization problem. In the PCA-based RFM, each Principal Component (PC) is a linear combination of all variables in the design matrix. However, some original variables are noise related and have very small or almost zero contributions to the construction of PCs, which leads to the overparameterization problem and makes the RPC estimation process ill posed. To address this problem, in this paper, we propose an Adaptive Sparse Principal Component Analysis-based RFM method (ASPCA-RFM) for RPC estimation. In this method, the Elastic Net sparsity constraint is introduced to ensure that each PC contains only a small number of original variables, which automatically eliminates unnecessary variables during PC computation. Since the optimal regularization parameters of the Elastic Net vary significantly in different scenarios, an adaptive regularization parameter approach is proposed to dynamically adjust the regularization parameters according to the explained variance of PCs and degrees of freedom. By adopting the proposed method, the noise and error in the design matrix can be reduced, and the ill-posedness and overparameterization of the RPC estimation can be significantly mitigated. Additionally, we conduct extensive experiments to validate the effectiveness of our method. Compared to existing state-of-the-art methods, the proposed method yields markedly improved or competitive performance.

Initial point positioning of weld seam and robot pose estimation based on binocular vision

Abstract In order to rapidly identify and locate the weld seam initial point in robotic automated welding, we established a binocular vision system and proposed a weld seam initial point localization algorithm named WIPL-Net. Built upon the Fully Convolutional One-Stage object detection network, WIPL-Net introduces a lightweight ResNext as its backbone network and incorporates channel attention and enhanced feature fusion mechanisms to enhance feature detection and extraction capabilities. Subsequently, WIPL-Net is utilized to obtain the weld seam’s initial point, and its three-dimensional coordinates are determined through trigonometric measurements. To further estimate the robot’s posture at the initial point, we performed sparse three-dimensional reconstruction of the local region centered on the weld seam initial point based on You Only Look At Coefficients of Tensors instance segmentation and feature point matching. Finally, we conducted comparative experiments on WIPL-Net and conducted weld seam initial point localization experiments in real welding scenarios. The results demonstrate that our proposed method achieves a positioning error of less than 1.2 mm for the weld seam’s initial point and a pose error of less than 10 degrees for the robot, meeting the requirement for real-time positioning of the weld seam’s initial point.