Search Results (103)

Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation solutions in manufacturing, serving safety-critical sectors such as automotive and aerospace industries. However, this method of component fabrication is very susceptible to generating manufacturing flaws, hence necessitating adequate non-destructive testing (NDT) to ascertain the fitness for use of such components. Machine learning has taken the center stage in recent years as a tool for developing automated solutions for detecting and classifying flaws in digital X-ray radiography. These machine learning-based solutions have increasingly been developed and deployed for component inspection, to keep pace with the high production throughput in manufacturing industries. This work focuses on the development of a defect grading algorithm that assesses detected flaws to ascertain if they constitute a defect that could render a component unfit for use. Guided by ASTM 2973-15; Standard Digital Reference Images for Inspection of Aluminum and Magnesium Die Castings, a grading pipeline utilizing K-D (k-dimensional) trees was developed to effectively structure detected flaws, enabling the system to make decisions based on acceptable grading terms. This solution is dynamic in terms of its conformity to different grading criteria and offers the possibility to achieve automated decision making (Accept/Reject) in digital X-ray radiography applications. Full article

In linear elastic fracture mechanics, the stress intensity factor describes the magnitude of the stress singularity near a crack tip caused by remote stress and is related to the rate of fatigue crack growth. The literature lacks SIF solutions for cracks emanating from a three-dimensional semi-ellipsoidal pit. This study undertakes a comprehensive parametric investigation of the Mode I stress intensity factor ${(K}_I)$ concerning cracks originating from a semi-ellipsoidal pit in a plate. This work utilizes finite element analysis, controlled by Python scripts, to conduct an extensive study on the effect of various pit dimensions and crack lengths on $K_I$. Two cracks in the shape of a circular arc are introduced at the pit mouth perpendicular to the loading direction. The $K_I$ values are calculated using the displacement extrapolation method. The effect of normalized geometric parameters pit-depth-to-pit-width $(a/2c$), pit-depth-to-plate-thickness $(a/t)$, and crack-radius-to-pit-depth $(R/a)$ are investigated. The crack-radius-to-pit-depth $(R/a)$ is found to be the dominating parameter based on correlation analysis. The data obtained from 216 FEA simulations are incorporated into a predictive model using a k-dimensional (k-d) tree and k-Nearest Neighbour (k-NN) algorithm. Full article

Greenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, Türkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predict future patterns (2036 and 2050) via the Markov–FLUS model. Performances of random forest (RF), k-nearest neighborhood (KNN), and k-dimensional trees k-nearest neighborhood (KD-KNN) algorithms were compared for GH discrimination. Accordingly, the RF algorithm gave the highest accuracies of over 90%. GH areas were found to increase by 73% between 2008 and 2022. The majority of new areas were converted from agricultural lands. Markov-based predictions showed that GHs are likely to increase by 43% and 54% before 2036 and 2050, respectively, whereby reliable simulations were generated with the FLUS model. This study is believed to serve as a baseline for future research by providing the first attempt at the visualization of future GH conditions in the Turkish Mediterranean region. Full article

Traditionally, ceramic design began with sketches on rough paper and later evolved into using CAD software for more complex designs and simulations. With technological advancements, optimization algorithms have gradually been introduced into ceramic design to enhance design efficiency and creative diversity. The use of Interactive Genetic Algorithms (IGAs) for ceramic design is a new approach, but an IGA requires a significant amount of user evaluation, which can result in user fatigue. To overcome this problem, this paper introduces the LightGBM algorithm and the CatBoost algorithm to improve the IGA because they have excellent predictive capabilities that can assist users in evaluations. The algorithms are also applied to a vase design platform for validation. First, bicubic Bézier surfaces are used for modeling, and the genetic encoding of the vase is designed with appropriate evolutionary operators selected. Second, user data from the online platform are collected to train and optimize the LightGBM and CatBoost algorithms. Finally, LightGBM and CatBoost are combined with an IGA and applied to the vase design platform to verify their effectiveness. Comparing the improved algorithm to traditional IGAs, KD trees, Random Forest, and XGBoost, it is found that IGAs improve with LightGBM, and CatBoost performs better overall, requiring fewer evaluations and less time. Its R² is higher than other proxy models, achieving 0.816 and 0.839, respectively. The improved method proposed in this paper can effectively alleviate user fatigue and enhance the user experience in product design participation. Full article

To address the issue of accuracy in Simultaneous Localization and Mapping (SLAM) for forested areas, a novel point cloud adaptive filtering algorithm is proposed in the paper, based on point cloud data obtained by backpack Light Detection and Ranging (LiDAR). The algorithm employs a K-D tree to construct the spatial position information of the 3D point cloud, deriving a linear model that is the guidance information based on both the original and filtered point cloud data. The parameters of the linear model are determined by minimizing the cost function using an optimization strategy, and a guidance point cloud filter is subsequently constructed based on these parameters. The results demonstrate that, comparing the diameter at breast height (DBH) and tree height before and after filtering with the measured true values, the accuracy of SLAM mapping is significantly improved after filtering. The Mean Absolute Error (MAE) of DBH before and after filtering are 2.20 cm and 1.16 cm; the Root Mean Square Error (RMSE) values are 4.78 cm and 1.40 cm; and the relative RMSE values are 29.30% and 8.59%. For tree height, the MAE before and after filtering are 0.76 m and 0.40 m; the RMSE values are 1.01 m and 0.50 m; the relative RMSE values are 7.33% and 3.65%. The experimental results validate that the proposed adaptive point cloud filtering method based on guided information is an effective point cloud preprocessing method for enhancing the accuracy of SLAM mapping in forested areas. Full article

Canopy volume is a crucial biological parameter for assessing tree growth, accurately estimating forest Above-Ground Biomass (AGB), and evaluating ecosystem stability. Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) are advanced precision mapping technologies that capture highly accurate point clouds for forest digitization studies. Despite advances in calculating canopy volume, challenges remain in accurately extracting the canopy and removing gaps. This study proposes a canopy volume extraction method based on an improved PointNeXt model, fusing ALS and TLS point cloud data. In this work, improved PointNeXt is first utilized to extract the canopy, enhancing extraction accuracy and mitigating under-segmentation and over-segmentation issues. To effectively calculate canopy volume, the canopy is divided into multiple levels, each projected into the xOy plane. Then, an improved Mean Shift algorithm, combined with KdTree, is employed to remove gaps and obtain parts of the real canopy. Subsequently, a convex hull algorithm is utilized to calculate the area of each part, and the sum of the areas of all parts multiplied by their heights yields the canopy volume. The proposed method’s performance is tested on a dataset comprising poplar, willow, and cherry trees. As a result, the improved PointNeXt model achieves a mean intersection over union (mIoU) of 98.19% on the test set, outperforming the original PointNeXt by 1%. Regarding canopy volume, the algorithm’s Root Mean Square Error (RMSE) is 0.18 m³, and a high correlation is observed between predicted canopy volumes, with an R-Square (R²) value of 0.92. Therefore, the proposed method effectively and efficiently acquires canopy volume, providing a stable and accurate technical reference for forest biomass statistics. Full article

Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%. Full article

To overcome incomplete point cloud data obtained from laser scanners scanning complex surfaces, multi-viewpoint cloud data needs to be aligned for use. A hybrid improved SAC-IA with a KD-ICP algorithm is proposed for local point cloud alignment optimization. The scanned point cloud data is preprocessed with statistical filtering, as well as uniform down-sampling. The sampling consistency initial alignment (SAC-IA) algorithm is improved by introducing a dissimilarity vector for point cloud initial alignment. In addition, the iterative closest point (ICP) algorithm is improved by incorporating bidirectional KD-tree to form the KD-ICP algorithm for fine point cloud alignment. Finally, the algorithms are compared in terms of runtime and alignment accuracy. The implementation of the algorithms is based on the Visual Studio 2013 software configurating point cloud library environment for testing experiments and practical experiments. The overall alignment method can be 40%~50% faster in terms of running speed. The improved SAC-IA algorithm provides better transformed poses, combined with the KD-ICP algorithm to select the corresponding nearest neighbor pairs, which improves the accuracy, as well as the applicability of the alignment. Full article

This paper proposes a point-by-point weighted fusion algorithm based on an improved random sample consensus (RANSAC) and inverse distance weighting to address the issue of low-resolution point cloud data obtained from light detection and ranging (LiDAR) sensors and single technologies. By fusing low-resolution point clouds with higher-resolution point clouds at the data level, the algorithm generates high-resolution point clouds, achieving the super-resolution reconstruction of lidar point clouds. This method effectively reduces noise in the higher-resolution point clouds while preserving the structure of the low-resolution point clouds, ensuring that the semantic information of the generated high-resolution point clouds remains consistent with that of the low-resolution point clouds. Specifically, the algorithm constructs a K-d tree using the low-resolution point cloud to perform a nearest neighbor search, establishing the correspondence between the low-resolution and higher-resolution point clouds. Next, the improved RANSAC algorithm is employed for point cloud alignment, and inverse distance weighting is used for point-by-point weighted fusion, ultimately yielding the high-resolution point cloud. The experimental results demonstrate that the proposed point cloud super-resolution reconstruction method outperforms other methods across various metrics. Notably, it reduces the Chamfer Distance (CD) metric by 0.49 and 0.29 and improves the Precision metric by 7.75% and 4.47%, respectively, compared to two other methods. Full article

In satellite imagery, clouds obstruct the ground information, directly impacting various downstream applications. Thus, cloud annotation/cloud detection serves as the initial preprocessing step in remote sensing image analysis. Recently, deep learning methods have significantly improved in the field of cloud detection, but training these methods necessitates abundant annotated data, which requires experts with professional domain knowledge. Moreover, the influx of remote sensing data from new satellites has further led to an increase in the cost of cloud annotation. To address the dependence on labeled datasets and professional domain knowledge, this paper proposes an automatic cloud annotation method for satellite remote sensing images, CloudAUE. Unlike traditional approaches, CloudAUE does not rely on labeled training datasets and can be operated by users without domain expertise. To handle the irregular shapes of clouds, CloudAUE firstly employs a convex hull algorithm for selecting cloud and non-cloud regions by polygons. When selecting convex hulls, the cloud region is first selected, and points at the edges of the cloud region are sequentially selected as polygon vertices to form a polygon that includes the cloud region. Then, the same selection is performed on non-cloud regions. Subsequently, the fast KD-Tree algorithm is used for pixel classification. Finally, an uncertainty method is proposed to evaluate the quality of annotation. When the confidence value of the image exceeds a preset threshold, the annotation process terminates and achieves satisfactory results. When the value falls below the threshold, the image needs to undergo a subsequent round of annotation. Through experiments on two labeled datasets, HRC and Landsat 8, CloudAUE demonstrates comparable or superior accuracy to deep learning algorithms, and requires only one to two annotations to obtain ideal results. An unlabeled self-built Google Earth dataset is utilized to validate the effectiveness and generalizability of CloudAUE. To show the extension capabilities in various fields, CloudAUE also achieves desirable results on a forest fire dataset. Finally, some suggestions are provided to improve annotation performance and reduce the number of annotations. Full article

The human–computer interaction attribute of the interactive genetic algorithm (IGA) allows users to participate in the product design process for which the product needs to be evaluated, and requiring a large number of evaluations would lead to user fatigue. To address this issue, this paper utilizes an XGBoost proxy model modified by particle swarm optimization and the graphical interaction mechanism (GIM) to construct an improved interactive genetic algorithm (PXG-IGA), and then the PXG-IGA is applied to 3D vase design. Firstly, the 3D vase shape has been designed by using a bicubic Bézier surface, and the individual genetic code is binary and includes three parts: the vase control points, the vase height, and the texture picture. Secondly, the XGBoost evaluation of the proxy model has been constructed by collecting user online evaluation data, and the particle swarm optimization algorithm has been used to optimize the hyperparameters of XGBoost. Finally, the GIM has been introduced after several generations, allowing users to change product styles independently to better meet users’ expectations. Based on the PXG-IGA, an online 3D vase design platform has been developed and compared to the traditional IGA, KD tree, random forest, and standard XGBoost proxy models. Compared with the traditional IGA, the number of evaluations has been reduced by 58.3% and the evaluation time has been reduced by 46.4%. Compared with other proxy models, the accuracy of predictions has been improved up from 1.3% to 20.2%. To a certain extent, the PXG-IGA reduces users’ operation fatigue and provides new ideas for improving user experience and product design efficiency. Full article

Traditional iterative closest point (ICP) registration algorithms are sensitive to initial positions and easily fall into the trap of locally optimal solutions. To address this problem, a point cloud registration algorithm is put forward in this study based on adaptive neighborhood eigenvalue loading ratios. In the algorithm, the resolution of the point cloud is first calculated and used as an adaptive basis to determine the raster widths and radii of spherical neighborhoods in the raster filtering; then, the adaptive raster filtering is implemented to the point cloud for denoising, while the eigenvalue loading ratios of point neighborhoods are calculated to extract and match the contour feature points; subsequently, sample consensus initial alignment (SAC-IA) is used to carry out coarse registration; and finally, a fine registration is delivered with KD-tree-accelerated ICP. The experimental results of this study demonstrate that the feature points extracted with this method are highly representative while consuming only 35.6% of the time consumed by other feature point extraction algorithms. Additionally, in noisy and low-overlap scenarios, the registration error of this method can be controlled at a level of 0.1 mm, with the registration speed improved by 56% on average over that of other algorithms. Taken together, the method in this study cannot only ensure strong robustness in registration but can also deliver high registration accuracy and efficiency. Full article

An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection. Full article

The study proposes two innovative algorithms in the field of multi-beam echo sounder (MBES) simulation: distance-aided sound ray marching (DASRM) and height-aided sound ray marching (HASRM). These algorithms aim to enhance the efficiency and accuracy of MBES simulations, particularly when dealing with long-distance propagation and real-time processing limitations. DASRM addresses issues related to simulation accuracy by efficiently utilizing the KD-tree for spatial indexing and intersection detection instead of the signed distance field (SDF). Building upon the further analysis of DASRM, HASRM is proposed, which improves the search strategy for ray intersections and utilizes a height field pyramid for sampling and retrieval, thereby reducing memory usage while enhancing indexing efficiency. The experimental results demonstrate that both algorithms significantly outperform traditional methods in terms of simulation time, with HASRM exhibiting particular advantages in parallel computing due to its data structure and improved strategies. Additionally, DASRM is well suited for applications requiring complex scene construction, while HASRM proves especially effective in simulating MBES with a focus on underwater terrain due to its effectiveness in handling large incident angles and long-distance propagation. Full article

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment. Full article