K-dimensional Tree Research Articles

Comparison of Collision Avoidance Algorithms for Unmanned Surface Vehicle Through Free-Running Test: Collision Risk Index, Artificial Potential Field, and Safety Zone

This paper details the development of a collision avoidance algorithm for unmanned surface vehicles (USVs) and its validation using free-running tests. The USV, designed as a catamaran, incorporates a variety of sensors for its guidance, navigation, and control system. It performs turning maneuvers using thrusters positioned on the port and starboard sides. The robot operating system is used to streamline communication, transmitting data such as position, orientation, and situational information from diverse sensors. Using the collision risk index (CRI) method, the algorithm calculates risk based on the distance to obstacles and the angle to the desired waypoint, directing the USV on a path with minimized risk. Noise within the data captured by the two-dimensional light detection and ranging system is filtered out using the k-dimensional tree and Euclidean distance methods, ensuring single obstacles are distinctly identified. To assess the efficacy of the CRI-based collision avoidance algorithm, it was benchmarked against other algorithms rooted in the artificial potential field and safety zone methods within an artificial tank setting. The results highlight the CRI method’s superior time efficiency and optimality in comparison to its counterparts.

Position Normalization of Propellant Grain Point Clouds

Point cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’s point cloud. This paper proposes a normalization algorithm for propellant grain point clouds, consisting of two stages, coarse normalization and fine normalization, to achieve high-precision transformations of the point clouds. In the coarse normalization stage, a layer-by-layer feature points detection scheme based on k-dimensional trees (KD-tree) and k-means clustering (k-means) is designed to extract feature points from the propellant grain point cloud. In the fine normalization stage, a rotation angle compensation scheme is proposed to align the fitted symmetry axis of the propellant grain point cloud with the coordinate axes. Finally, comparative experiments with iterative closest point (ICP) and random sample consensus (RANSAC) validate the efficiency of the proposed normalization algorithm.

Research on 3D laser SLAM algorithm based on graph optimization

Abstract Aiming at the problems that the positioning accuracy of laser Simultaneous Localization and Mapping (SLAM) is affected by the misclassification of close feature points and incorrect feature matching in the environment with redundant features, a 3D laser SLAM algorithm based on graph optimization was proposed. Firstly, to reduce the feature extraction error problem, the distance threshold is used to filter the close point cloud and improve the accuracy of feature classification, enhanced reliability of optoelectronic measurement data. Secondly, to improve the consistency of feature matching, based on the rough matching based on k-dimensional tree (KD-Tree), a two-stage fine matching based on geometric consistency is introduced to improved laser point alignment accuracy. The proposed method is compared with the mainstream localization algorithms based on KITTI dataset. Compared with Lidar-IMU Odometry with Sparse Adaptive Mapping and Fast LiDAR-Inertial Odometry, the positioning error of the proposed method is reduced by 21.88%and 24.43%on average, which showsthe superiority of the present algorithm in the application of optoelectronics and optical technology, and provides new ideas and methods for the development of high-precision laser navigation technology.

Prediction of Greenhouse Area Expansion in an Agricultural Hotspot Using Landsat Imagery, Machine Learning and the Markov–FLUS Model

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.

Step feature line extraction from large-scale point clouds of open-pit mine based on structural characteristics

High-precision step feature lines play a crucial role in open-pit mine design, production scheduling, mining volume calculations, road network planning, and slope maintenance. Compared with the feature lines of the geometric model, step feature lines are more irregular, complex, higher in density, and richer in detail. In this study, a novel technique for extracting step feature line from large-scale point clouds of open-pit mine by leveraging structural attributes, that is, SFLE_OPM (Step Feature Line Extraction for Open-Pit Mine), is proposed. First, we adopt the k-dimensional tree (KD-tree) resampling method to reduce the point-cloud density while retaining point-cloud features and utilize bilateral filtering for denoising. Second, we use Point Cloud Properties Network (PCPNET) to estimate the normal, calculate the slope and aspect, and then filter them. We then apply morphological operations to the step surface and obtain more continuous and smoother slope lines. In addition, we construct an Open-Pit Mine Step Feature Line (OPMSFL) dataset and benchmarked SFLE_OPM, achieving an accuracy score of 89.31% and true positive rate score of 80.18%. The results demonstrate that our method yields a higher extraction accuracy and precision than most of the existing methods. Our dataset is available at https://github.com/OPMDataSets/OPMSFL.

VISIR-2: ship weather routing in Python

Abstract. Ship weather routing, which involves suggesting low-emission routes, holds potential for contributing to the decarbonisation of maritime transport. However, including because of a lack of readily deployable open-source and open-language computational models, its quantitative impact has been explored only to a limited extent. As a response, the graph-search VISIR (discoVerIng Safe and effIcient Routes) model has been refactored in Python, incorporating novel features. For motor vessels, the angle of attack of waves has been considered, while for sailboats the combined effects of wind and sea currents are now accounted for. The velocity composition with currents has been refined, now encompassing leeway as well. Provided that the performance curve is available, no restrictions are imposed on the vessel type. A cartographic projection has been introduced. The graph edges are quickly screened for coast intersection via a K-dimensional tree. A least-CO2 algorithm in the presence of dynamic graph edge weights has been implemented and validated, proving a quasi-linear computational performance. The software suite's modularity has been significantly improved, alongside a thorough validation against various benchmarks. For the visualisation of the dynamic environmental fields along the route, isochrone-bounded sectors have been introduced. The resulting VISIR-2 model has been employed in numerical experiments within the Mediterranean Sea for the entirety of 2022, utilising meteo-oceanographic analysis fields. For a 125 m long ferry, the percentage saving of overall CO2 expenditure follows a bi-exponential distribution. Routes with a carbon dioxide saving of at least 2 % with respect to the least-distance route were found for prevailing beam or head seas. Two-digit savings, up to 49 %, were possible for about 10 d in a year. In the case of an 11 m sailboat, time savings increased with the extent of path elongation, particularly during upwind sailing. The sailboat's routes were made approximately 2.4 % faster due to optimisation, with the potential for an additional 0.8 % in savings by factoring in currents. VISIR-2 serves as an integrative model, uniting expertise from meteorology, oceanography, ocean engineering, and computer science, to evaluate the influence of ship routing on decarbonisation efforts within the shipping industry.

LeGO-LOAM-FN: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM, Faster_GICP and NDT in Complex Orchard Environments.

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.

Intelligent Algorithms for Detecting Attacks in the Web Environment

The article is devoted to the analysis of the use of machine learning algorithms to detect attacks using a custom web environment or the functionality of user applications. Learning with a teacher and clustering algorithms are considered. The dataset uses a sample of online shopping transactions collected by an e-commerce retailer. The dataset contains 39,221 transactions. To detect attacks in the web environment, the most optimal implementations of machine learning algorithms were selected after their review and comparative analysis. The most effective algorithm for detecting fraudulent transactions has been determined. We use the accuracy and running time of the algorithm as criteria. The accuracy of detecting fraudulent transactions for Random Forest, GB (Scikit-learn), GB (CatBoost) algorithms is 100%, and the KD-trees algorithm is 99,9%. The gradient boosting algorithm in the CatBoos implementation is 4,2 times faster than Random Forest, 2,4 times faster than GB Scikit-learn, 1,2 times faster than GB without using the cat_features parameter, 41,9 times faster than k-dimensional trees, 66,8 times faster than DBSCAN. The data obtained for each method is presented in the form of tables. Within the framework of this work, the parameters for evaluating the effectiveness of the algorithms under study are learning time indicators, as well as characteristics from the Confusion matrix and Classification Report for classification algorithms, and fowlkes_mallows_score, rand_score, adjusted_rand_score, Homogeneity, Completeness, V-measure for clustering algorithms.

Efficient method for finding nearest neighbors in flocking behaviors using k-dimensional trees

<p>Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensional trees (KD trees). To evaluate the proposed approach, we apply it using Unity-3D game engine, together with other conventional nearest neighbor methods. The Unity-3D game simulation engine allows users to utilize interaction design tools for programming and animating flocking behaviors. Results showed that the proposed approach outperform other conventional nearest neighbor approaches. The proposed approach can be used to enhance digital games quality and simulations.</p>

Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR

BackgroundThe metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm.ResultsThe results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m2, rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m2, rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m2, rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field.ConclusionsThe algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype.

A Web3D Rendering Optimization Algorithm for Pipeline BIM Models

BIM (building information modeling) plays a pivotal role in the construction industry. BIM technology tailored for pipelines offers in-depth semantic information and spatial data, bolstering the utility and implementation of digital twin-associated technologies in both architecture and urban planning. This paper introduces a rendering optimization algorithm rooted in the BSP Tree (Binary Space Partitioning Tree). The algorithm is used to address the challenges of slow loading and poor rendering quality of pipeline BIM models when displayed on the web, which stem from large amounts of model data and complex geometric configurations. Initially, the algorithm delves into the geometric distribution traits of the pipeline BIM model from multiple perspectives, pinpointing the spatial division dimension. Subsequently, it employs an adaptive step size technique for spatial segmentation, harmonizing it with real-world application contexts. Concurrently, any superfluous data that emerge are refined to uphold the structural wholeness of the BIM model. This algorithm is adept at systematically arranging and overseeing the BIM model data. Trial outcomes reveal that the AKDT (Adaptive K-Dimensional Tree) algorithm significantly trims the browser’s initial rendering duration while maintaining the model’s accuracy and semantic uniformity. Moreover, it excels in areas such as rendering frame rate, user interaction responsiveness, and data transmission duration. In essence, the algorithm stands out for its efficiency and precision in rendering pipeline BIM models on web platforms, achieving the desired optimization results.

A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components

This paper proposes a stereo-vision-based method that detects and registers the positions and postures of muti-type, randomly placed miniature circuit breaker (MCB) components within scene point clouds acquired by a 3D stereo camera. The method is designed to be utilized in the flexible assembly of MCBs to improve the precision of gripping small-sized and complex-structured components. The proposed method contains the following stages: First, the 3D computer-aided design (CAD) models of the components are converted to surface point cloud models by voxel down-sampling to form matching templates. Second, the scene point cloud is filtered, clustered, and segmented to obtain candidate-matching regions. Third, point cloud features are extracted by Intrinsic Shape Signatures (ISSs) from the templates and the candidate-matching regions and described by Fast Point Feature Histogram (FPFH). We apply Sample Consensus Initial Alignment (SAC-IA) to the extracted features to obtain a rough matching. Fourth, fine registration is performed by employing Iterative Closest Points (ICPs) with a K-dimensional Tree (KD-tree) between the templates and the roughly matched targets. Meanwhile, Random Sample Consensus (RANSAC), which effectively solves the local optimal problem in the classic ICP algorithm, is employed to remove the incorrectly matching point pairs for further precision improvement. The experimental results show that the proposed method achieves spatial positioning errors smaller than 0.2 mm and postural estimation errors smaller than 0.5°. The precision and efficiency meet the requirements of the robotic flexible assembly for MCBs.

A New Algorithm for Large-Scale Geographically Weighted Regression with K-Nearest Neighbors

Geographically weighted regression (GWR) is a classical method for estimating nonstationary relationships. Notwithstanding the great potential of the model for processing geographic data, its large-scale application still faces the challenge of high computational costs. To solve this problem, we proposed a computationally efficient GWR method, called K-Nearest Neighbors Geographically weighted regression (KNN-GWR). First, it utilizes a k-dimensional tree (KD tree) strategy to improve the speed of finding observations around the regression points, and, to optimize the memory complexity, the submatrices of neighbors are extracted from the matrix of the sample dataset. Next, the optimal bandwidth is found by referring to the spatial clustering relationship explained by K-means. Finally, the performance and accuracy of the proposed KNN-GWR method was evaluated using a simulated dataset and a Chinese house price dataset. The results demonstrated that the KNN-GWR method achieved computational efficiency thousands of times faster than existing GWR algorithms, while ensuring accuracy and significantly improving memory optimization. To the best of our knowledge, this method was able to run hundreds of thousands or millions of data on a standard computer, which can inform improvement in the efficiency of local regression models.

Unified chip hardware architecture of KD-tree mean-based trainer and speeding-up classifier with repeat-point searching for various applications

In this paper, we propose a total ASIC solution of unified low-latency K-dimensional tree (KD-tree) chip hardware architecture, including online mean-based trainer and speeding-up classifier. As compared with the other works in the current literature, we also develop a first KD-tree chip implementation to simultaneously perform training and classification tasks. Instead of supporting only one specific application, our work can be widely applied for various applications and well-verified with different datasets. Meanwhile, for different purposes, we also develop three useful design techniques, such as efficient trainer with mean-based splitting (ET-MBS), speeding-up classifier with repeat-point searching (SC-RPS), and double-leaf tree structure (DLTS). With TSMC 40-nm CMOS technology, the post-APR ASIC chip layout is well-verified with 7 representative datasets for various applications. The total core area only occupies 0.357 mm2, operating at a maximum frequency of 474 MHz and consuming power of 71.6 mW. Regarding online training, the worst-case training latency is only 1.9 μs for dealing with 1 K data. As for classification performance, the decision throughput varies from 0.51 GBps to 3.56 GBps.

Multi-modal multi-step wind power forecasting based on stacking deep learning model

Wind power is becoming a clean and effective energy source for electric power generation. However, the abnormity, multi-modal, and uncertainty represented in wind power data are commonly undesired. Thus, accurate wind power forecasting is a significant method for keeping the power system operations steady. To solve these issues, a multi-modal multi-step wind power forecasting model is presented. To obtain this, the density-based spatial clustering of applications with noise (DBSCAN) is improved by the k-dimensional tree (kd-tree) for detecting abnormal data. Then, the low-rank matrix fusion method fuses the wind speed, wind direction, and air density modalities for obtaining a unified representation. To further increase model accuracy, we propose a stacking deep learning model (SDLM) for overcoming the uncertainty phenomenon, which contains the bidirectional gated recurrent unit (BGRU) and leaky echo state network (LESN). The final forecasting results are acquired by a meta-learning operator. To validate the accuracy and stability of the presented approach, the inland and offshore wind farm datasets are used for forecasting. The contrastive results demonstrate that the presented model outperforms satisfactory performance in multi-step wind power prediction.

Robust automatic modulation classification under noise mismatch

Automatic modulation classification plays a critical role in the intelligent reception of unknown wireless signals. In practice, the dynamic wireless environment brings a great challenge, and the actual test model is inconsistent with the training model. Therefore, aiming at the problem of noise mismatch, this paper proposes a new modulation classification method based on KD-GoogLeNet and Squeeze-Excitation (KD-GSENet). Using the k-dimensional tree, the complex wireless signals are converted into color images rather than normal constellations, which can enhance the classification features. Considering the attention block has the inherent advantage of assigning more weights to important features, this paper further uses it to improve the GoogLeNet. Finally, extensive experiments are presented including Gaussian noise, non-Gaussian noise, and the scenarios of noise mismatch. Numerical results verify the superior classification performance of the proposed KD-GSENet under different scenarios.

A genomic rule-based KNN model for fast flux botnet detection

Fast Flux Botnet (FFB) is an advance method developed by cyber criminals to perpetrate distributed malicious attacks. The major problems of existing FFB detection systems are the vulnerability to evasion mechanisms, long detection time, and high dimensionality of the feature set. In this study, an improved FFB detection architecture called Bot-FFX was developed to address some of these problems. The developed Bot-FFX consists of four modules: extractor, filter, resolver, and detector. The extractor module is responsible for Domain Name System (DNS) queries on domains. The filter module can classify the incoming domains as either blacklist or whitelist and sends the unclassified domains to the resolver. The resolver extracts all IP addresses associated with the domain at its Time-To-Live (TTL) within a time frame of 10 min. The detector module uses a rule-based Genetic Algorithm (GA) and K-Nearest Neighbor (KNN) for botnet detection. The detector computed the Standard Deviation of Round Trip Time (SDRTT), Average Google Hits (AGH) and Genetic Threshold Value (GTV) for all IP addresses associated with the domains. The detector, built on a decision tree rules and the K-Dimensional (KD) tree KNN algorithm, classified the domains using the set of IP addresses, SDRTT, AGH, and GTV. The Bot-FFX was implemented on a dataset of 2,000 benign domains and 1,630 botnet domains. The dataset was split into 50% training and 50% testing sets. The evaluation results on the same datasets showed that Bot-FFX is an effective FFB detection system with accuracy, false positive, and false negative of 99.178%, 0.8%, and 0.8% respectively.

Development of a hybrid parallelism Monte Carlo transport middleware on mesh geometry

Mesh-based Monte Carlo particle transport is suitable for complex geometry modeling and for multi-physics coupling calculations. Performing large-scale mesh-based Monte Carlo simulations requires a quite large amount of computation and memory space. Therefore, parallel Monte Carlo computing is crucial. In this study, a hybrid parallelism Monte Carlo transport middleware on mesh geometry was developed. The middleware can provide the following algorithms and functions: 1. hybrid parallelism of domain decomposition and particle parallelism; 2. particle data structure, which manages particle information and automatically performs asynchronous communication between decomposed domains; 3. subset data structure, which provides an efficient management strategy for materials and tally data; 4. particle tracking method on structured/unstructured mesh and k-dimensional tree (KD-Tree) acceleration algorithm; 5. condensed application programming interface for the rapid development of Monte Carlo code based on the middleware. We transplanted the fixed source module of the J Monte Carlo transport code (JMCT) which was based on constructive solid geometry (CSG) to the middleware for verification. The results indicated that the serial particle tracking efficiency of an unstructured mesh model was 6.56 times as high as that of MCNP6 and the serial simulation efficiency of the corresponding structured mesh model was basically equivalent to that of JMCT. The parallel efficiency of domain decomposition was higher than that of JMCT. We also calculated the shielding problem of the Qinshan No. 1 reactor through hybrid parallelism. The results were in good agreement with the experimental data, and the hybrid parallel efficiencies were higher than 65% on 4000 CPU cores.

Candidate project selection in cross project defect prediction using hybrid method

Cross Project Defect Prediction (CPDP) is a process that develops a defect prediction model on source projects and then applies the same model to the target project. Day by day, new software projects are being developed, so selecting an appropriate training project from existing projects and from new upcoming projects, to train a predictor model is a challenging task in CPDP. In the present study, we have proposed a hybrid selection method to select a candidate project from existing projects and a probabilistic method to select a candidate project from new projects. The proposed hybrid method is a weighted combination of the Collaborative filtering (CF) method and the Content Based (CB) method. The probabilistic method is based on a Naïve Bayes classifier and is used to predict the relation between the target project and the new target project. In the CF method, a usability score is generated for each project by making use of classification techniques, and the CB method calculates the matching score of candidate projects by using the K-dimensional tree. Finally, both the methods are combined by parallelized hybridization design, and weights for the proposed method are estimated with an empirical bootstrapping method. The score generated by the proposed hybrid technique is then used to identify the most suitable candidate project for the new project. The experimental results show that the suggestion list of the best three candidate projects is consistent when employing different classifiers. The recommendation performance is evaluated in terms of F-score and Mean Average Precision (MAP), and the proposed method has shown improved performance as compared to the existing methods in both terms.

A combination of library search and Levenberg-Marquardt algorithm in optical scatterometry

Optical scatterometry has been widely used for nanostructure metrology in integrated circuit (IC) given the fast, low-cost, non-contact, non-destructive, and in-line technique. Library search is a common solution for optical scatterometry, but the deterministic error in this method is hard to be eliminated due to the limitation of the grid interval in the spectral library. Another solution is the nonlinear regression algorithm, which faces difficulty in practical applications in meeting the in-line real-time requirements as it involves the time-consuming rigorous coupled-wave analysis (RCWA) calculations in the forward modeling. In this paper, we propose a combination of library search and Levenberg-Marquardt (LSLM) algorithm to solve the problem. We apply a presorted K-dimensional tree (KD-tree) algorithm to speed up searching the initial parameters in the spectral library and replace the time-consuming RCWA calculations with the Taylor expansion in the spectral library to compute the Jacobian matrix in the Levenberg-Marquardt (LM) procedure. Simulations and experiments demonstrate that the results in the proposed method are accurate, and in particular, the speed is much faster than that in previous methods.