Traversal Algorithm Research Articles

Research on intelligent three-dimensional anchor position detection method for ships utilizing Traversal and Monte Carlo algorithms

As intelligent ship technology advances, the importance of intelligent anchor position detection, as one of the key technologies, can ensure the safe anchoring of ships and enhance the efficiency of port operation. At present, most of the anchor position selection and detection algorithms are mainly based on two-dimensional planes, and there is a lack of research on the intelligent detection of safe water depth for ship anchoring in three-dimensional space. It not only restricts the full utilization of anchorage resources but also affects the safety and environmental adaptability of anchoring operations. To address these issues, this study proposes a three-dimension anchor position detection method. Firstly, based on the establishment of a three-dimensional ocean model, the possible anchor positions selected by the ship are simulated using the Monte Carlo algorithm. Secondly, the simulated anchor positions are optimized using a Traversal algorithm to filter out the optimal anchoring position that meets the requirements, the safety distance between each point and the existing ship is calculated, and the anchor position is determined according to the corresponding required safety spacing. Finally, to verify the applicability and effectiveness of the method under different sea conditions and different ship types, this study conducts a series of simulation experiments with 5000 random samples. These experiments compare the demand of anchor position selection for anchoring ships with changing water depths in the case of empty and full load drafts, and visualize the impact of varying water depth parameters on the selection of anchor positions for anchoring ships in various ship types. The outcomes of the experiment indicate that the algorithm’s detection area encompasses the whole anchorage area, ensuring both the anchorage area’s usage rate and the accuracy of anchor position detection. This study demonstrates that the Traversal and Monte Carlo Algorithms effectively improve the accuracy of the selection of anchoring position of the ship, makes full use of the resources of anchorage, and further improves the safety and efficiency of the anchoring operation.

Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning

AbstractDiverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library . Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${\mathcal{O}}(N)$$ O ( N ) , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.

Safety evaluation for the dismantling of long-span spatial lattice structures based on deep learning and graph traversal

The demolition issue of long-span spatial lattice structures has become increasingly prominent with the rapid urban development and building renewal. Compared with traditional demolition techniques, building deconstruction offers superior economic and social benefits. Dismantling in blocks is one of the primary ways to realize the deconstruction of long-span spatial lattice structures, yet the safety is rarely studied. To fill this gap, a safety evaluation framework was established based on deep learning and graph traversal algorithm in this study. Hazardous configuration of the remaining structure is the basis of dismantling safety evaluation. To collect the hazardous configurations, an identification method in conjunction with deep neural network (DNN) and depth-first search (DFS) algorithm was proposed. The dismantling safety is measured by the improved structural well-formedness whose allowable range is determined by statistical analysis of hazardous configurations. The applicability of the framework was illustrated by the safety evaluation for the dismantling of single-layer reticulated domes. Results indicated that the dismantling safety is significantly affected by the configuration quality of the remaining structure, and the DNN-based prediction model is effective to classify the configuration state. Additionally, a large number of hazardous configurations were obtained through the identification method, and the allowable value of the safety evaluation index was determined to be 0.7. The configuration quality mainly depends on the geometry and boundary conditions. This novel framework provides scientific and effective methodological guidance for engineers, promoting the further application of building deconstruction.

SecureQwen: Leveraging LLMs for vulnerability detection in python codebases

Identifying vulnerabilities in software code is crucial for ensuring the security of modern systems. However, manual detection requires expert knowledge and is time-consuming, underscoring the need for automated techniques. In this paper, we present SecureQwen, a novel vulnerability detection tool leveraging large language models (LLMs) with a context length of 64K tokens to identify potential security threats in large-scale Python codebases. Utilizing a decoder-only transformer architecture, SecureQwen captures complex relationships between code tokens, enabling accurate classification of vulnerable code sequences across 14 common weakness enumerations (CWEs), including OS Command Injection, SQL Injection, Improper Check or Handling of Exceptional Conditions, Path Traversal, Broken or Risky Cryptographic Algorithm, Deserialization of Untrusted Data, and Cleartext Transmission of Sensitive Information. Therefore, we evaluate SecureQwen on a large Python dataset with over 1.875 million function-level code snippets from different sources, including GitHub repositories, Codeparrot’s dataset, and synthetic data generated by GPT4-o. The experimental evaluation demonstrates high accuracy, with F1 scores ranging from 84% to 99%. The results indicate that SecureQwen effectively detects vulnerabilities in human-written and AI-generated code.

An efficient nonlocal integral method based on the octree algorithm

The nonlocal integral method typically requires a very high computing cost to search neighborhood integration points for calculating the nonlocal variable, which limits its application in large-scale problems. This paper proposes an efficient nonlocal integral method based on the octree algorithm, in which the integration point information is stored in the tree data structure to accelerate the search task. Firstly, the fundamental principles and implementation details of using the octree algorithm to search neighborhood integration points are described in detail. Subsequently, a Mohr-Coulomb nonlocal damage plastic model is presented as the application object of the proposed method. The model is implemented further in the ABAQUS using the octree-based nonlocal method and the return mapping algorithm enhanced by a line search method. Finally, two typical boundary value problems are simulated to verify the effectiveness and to assess the computational efficiency of the proposed nonlocal method. For the given test environment, the octree algorithm can achieve up to 100 times speedup at the integration point level compared to the traversal algorithm, and the developed efficient nonlocal method can achieve up to 7.9 times speedup at the boundary value problem level compared to the original nonlocal method.

Automated compound speciation, cluster analysis, and quantification of organic vapors and aerosols using comprehensive two-dimensional gas chromatography and mass spectrometry

Abstract. The advancement of analytical techniques, such as comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC–MS), enables the efficient separation of complex organics. Developing innovative methods for data processing and analysis is crucial to unlock the full potential of GC×GC–MS in understanding intricate chemical mixtures. In this study, we proposed an innovative method for the semi-automated identification and quantification of complex organic mixtures using GC×GC–MS. The method was formulated based on self-constructed mass spectrum patterns and the traversal algorithms and was applied to organic vapor and aerosol samples collected from the tailpipe emissions of heavy-duty diesel vehicles and the ambient atmosphere. Thousands of compounds were filtered, speciated, and clustered into 26 categories, including aliphatic and cyclic hydrocarbons, aromatic hydrocarbons, aliphatic oxygenated species, phenols and alkylphenols, and heteroatom-containing species. The identified species accounted for over 80 % of all the eluted chromatographic peaks at the molecular level. A comprehensive analysis of quantification uncertainty was undertaken. Using representative compounds, quantification uncertainties were found to be less than 37.67 %, 22.54 %, and 12.74 % for alkanes, polycyclic aromatic hydrocarbons (PAHs), and alkyl-substituted benzenes, respectively, across the GC×GC space, excluding the first and the last time intervals. From a source apportionment perspective, adamantane was clearly isolated as a potential tracer for heavy-duty diesel vehicle (HDDV) emissions. The systematic distribution of nitrogen-containing compounds in oxidized and reduced valences was discussed, and many of them served as critical tracers for secondary nitrate formation processes. The results highlighted the benefits of developing self-constructed models for the enhanced peak identification, automated cluster analysis, robust uncertainty estimation, and source apportionment and achieving the full potential of GC×GC–MS in atmospheric chemistry.

Research on Path Planning for Industrial Cleaning Robots in Complex Environments: An Enhanced Immunogenetic Algorithm Approach

As industrial automation advances, cleaning robots in complex environments have demonstrated significant value in enhancing efficiency and reducing labor costs. This paper presents a path planning method based on an improved Immune Genetic Algorithm (IGA) for comprehensive coverage path planning in complex industrial settings. Initially, the Boustrophedon Cellular Decomposition (BCD) method is employed to effectively segment the complex scene, identifying key areas for cleaning tasks. Subsequently, the path planning efficiency and adaptability are enhanced through an optimized genetic algorithm that integrates immune memory, Inver-Over operators, and 3-opt operators to refine the traversal sequence between regions. Moreover, this study incorporates the A* algorithm and the Biologically Inspired Neural Network (BINN) algorithm for efficient inter-regional path transitions and precise intra-regional traversal, respectively. A series of simulation experiments validate the superiority of the proposed algorithm in terms of path length, coverage rate, and computational time. The results demonstrate that this method not only effectively addresses the challenges of path planning in complex industrial environments but also significantly improves cleaning efficiency, offering high practical value and potential for broader application.

Dynamic Fault Tree Generation and Quantitative Analysis of System Reliability for Embedded Systems Based on SysML Models.

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an "anti-semantic" approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method.

Space‐efficient data structures for the inference of subsumption and disjointness relations

AbstractConventional database systems function as static data repositories, storing vast amounts of facts and offering efficient query processing capabilities. The sheer volume of data these systems store has a direct impact on their scalability, both in terms of storage space and query processing time. Deductive database systems, on the other hand, require far less storage space since they derive new knowledge by applying inference rules. The challenge is how to efficiently obtain the required derivations, compared to having them in explicit form. In this study, we concentrate on a set of predefined inference rules for subsumption and disjointness relations, including their negations. We use compact data structures to store the facts and provide algorithms to support each type of relation, minimizing even further the storage space requirements. Our experimental findings demonstrate the feasibility of this approach, which not only saves space but is often faster than a baseline that uses well‐known graph traversal algorithms implemented on top of a traditional adjacency list representation to derive the relations.

Tetrahedral grids in Monte Carlo radiative transfer

Context. To understand the structures of complex astrophysical objects, 3D numerical simulations of radiative transfer processes are invaluable. For Monte Carlo radiative transfer, the most common radiative transfer method in 3D, the design of a spatial grid is important and non-trivial. Common choices include hierarchical octree and unstructured Voronoi grids, each of which has advantages and limitations. Tetrahedral grids, commonly used in ray-tracing computer graphics, can be an interesting alternative option. Aims. We aim to investigate the possibilities, advantages, and limitations of tetrahedral grids in the context of Monte Carlo radiative transfer. In particular, we want to compare the performance of tetrahedral grids to other commonly used grid structures. Methods. We implemented a tetrahedral grid structure, based on the open-source library TetGen, in the generic Monte Carlo radiative transfer code SKIRT. Tetrahedral grids can be imported from external applications or they can be constructed and adaptively refined within SKIRT. We implemented an efficient grid traversal method based on Plücker coordinates and Plücker products. Results. The correct implementation of the tetrahedral grid construction and the grid traversal algorithm in SKIRT were validated using 2D radiative transfer benchmark problems. Using a simple 3D model, we compared the performance of tetrahedral, octree, and Voronoi grids. With a constant cell count, the octree grid outperforms the tetrahedral and Voronoi grids in terms of traversal speed, whereas the tetrahedral grid is poorer than the other grids in terms of grid quality. All told, we find that the performance of tetrahedral grids is relatively poor compared to octree and Voronoi grids. Conclusions. Although the adaptively constructed tetrahedral grids might not be favourable in most media representative of astrophysical simulation models, they still form an interesting unstructured alternative to Voronoi grids for specific applications. In particular, they might prove useful for radiative transfer post-processing of hydrodynamical simulations run on tetrahedral or unstructured grids.

Research on Safety Control Technology for Dual Robots Based on Kinematic Models

The industrial robots play an important role in modern industrial manufacturing processes, and their applications are becoming increasingly widespread, greatly improving manufacturing efficiency and significantly enhancing the digitalization level of the manufacturing industry. Therefore, achieving safe control of multi robot collaborative work is of great significance for the stable and efficient operation of robot systems. This article proposes a dual robot safety control technology based on the kinematic model of industrial robots. Firstly, a mathematical model of the kinematic space of the dual robots is constructed using D-H parameters; Then, use recursive traversal algorithm to calculate the minimum distance between each joint arm of the two robots in real time; Finally, the minimum distance between each joint arm will be calculated and compared with the set safety threshold to achieve real-time assessment and control of collision risk during the movement of the dual robots. A dual robot safety control testing platform was built using two KUKA industrial robots, integrated control software was developed, and relevant algorithms were validated. Through experiments, it was verified that this method can evaluate the collision risk of dual robots in real time during motion and effectively control the collision risk during the motion of dual robots.

Investigation of the error averaging effect on design and assembly of external slider closed hydrostatic guideways

Hydrostatic guideways are widely used in ultra-precision machine tools. However, in most cases, assemblers must rely on their experience to adjust the manufacturing errors of the guide rail and slider, which indirectly controls the error motion. To ensure assembly accuracy and improve efficiency, an error motion calculation model is established based on the external slider closed hydrostatic guideway. The accuracy of the model is verified through experiments, and the main factors that affect the error motion are analysed. Finally, a traversal algorithm is used to calculate the distribution of the error averaging coefficient. Once the error motion index is clarified, the design and assembly process of the guideway can be constrained by the error averaging coefficient.

Research on Parameter Correction of Distribution Network Model Based on CIM Model and Measurement Data

The construction of an energy distribution network can improve the system’s ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network needs accurate distribution network model parameters as support. At present, the installation of PMU equipment in China’s distribution network synchronous phase angle measurement unit is limited, which brings challenges to the parameter correction of the distribution network. In this paper, an automatic correction algorithm of distribution network parameters based on the CIM model and measurement data is proposed for the distribution network system without PMU. Firstly, the distribution network topology construction technology based on XML files and key fields of the distribution network is proposed, and the non/small impedance devices (such as switches) are merged and reduced. The breadth-first traversal algorithm is used to verify the connectivity of the constructed topology. Then, based on the topology construction and the least squares method, an iterative parameter correction technique is constructed. Finally, the accuracy and effectiveness of the proposed algorithm are verified in a standard IEEE 33-bus system distribution network and an example of the China Southern Power Grid. The topology connections constructed based on the CIM model have significantly enhanced the efficiency of parameter correction.

Considering the Optimization Design of Urban Bus Network Scheduling

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%.

Enhancing routing and quality of service using AI-driven technique for internet of vehicles contexts

This paper introduces an Enhancing Routing and Quality of Service (QoS) Using AI-driven Technique for Internet of Vehicles (IoV) Contexts. The proposed approach aims to enhance QoS and overall network performance in connected vehicle networks. Vehicles utilize Road-Side Units and the Internet to aggregate data from neighboring vehicles, optimizing routing and data management. The proposed approach utilizes a graph traversal algorithm, a widely adopted technique in Artificial Intelligence for graph search, which facilitates traversal in routing. The proposed approach integrates a mobility score based on metrics such as velocity, acceleration, and neighboring information, ensuring optimal routing for dynamic vehicular networks. Objectives include improving QoS and network performance by reducing overheads, optimizing load balancing, and extending network lifetime. Implemented in NS-3 and MOVE simulators, results demonstrate significant performance enhancements compared to existing approaches. This approach addresses the challenges of extensive vehicle mobility and frequent topology changes in connected environments. Overall, the paper presents a comprehensive solution for IoV networks, combining AI-driven routing with mobility-aware strategies to advance network efficiency and QoS.

Parameter Design of a Photovoltaic Storage Battery Integrated System for Detached Houses Based on Nondominated Sorting Genetic Algorithm-II

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building’s energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house.

Optimization of robotic path planning and navigation point configuration based on convolutional neural networks.

This study introduces a novel approach for enhancing robotic path planning and navigation by optimizing point configuration through convolutional neural networks (CNNs). Faced with the challenge of precise area coverage and the inefficiency of traditional traversal and intelligent algorithms (e.g., genetic algorithms, particle swarm optimization) in point layout, we proposed a CNN-based optimization model. This model not only tackles the issues of speed and accuracy in point configuration with Gaussian distribution characteristics but also significantly improves the robot's capability to efficiently navigate and cover designated areas with high precision. Our methodology begins with defining a coverage index, followed by an optimization model that integrates polygon image features with the variability of Gaussian distribution. The proposed CNN model is trained with datasets generated from systematic point configurations, which then predicts optimal layouts for enhanced navigation. Our method achieves an experimental result error of <8% on the test dataset. The results validate effectiveness of the proposed model in achieving efficient and accurate path planning for robotic systems.

CAVE: Concurrency-Aware Graph Processing on SSDs

Large-scale graph analytics has become increasingly common in areas like social networks, physical sciences, transportation networks, and recommendation systems. Since many such practical graphs do not fit in main memory, graph analytics performance depends on efficiently utilizing underlying storage devices. These out-of-core graph processing systems employ sharding and sub-graph partitioning to optimize for storage while relying on efficient sequential access of traditional hard disks. However, today's storage is increasingly based on solid-state drives (SSDs) that exhibit high internal parallelism and efficient random accesses. Yet, state-of-the-art graph processing systems do not explicitly exploit those properties, resulting in subpar performance. In this paper, we develop CAVE, the first graph processing engine that optimally exploits underlying SSD-based storage by harnessing the available storage device parallelism via carefully selecting which I/Os to graph data can be issued concurrently. Thus, CAVE traverses multiple paths and processes multiple nodes and edges concurrently, achieving parallelization at a granular level. We identify two key ways to parallelize graph traversal algorithms based on the graph structure and algorithm: intra-subgraph and inter-subgraph parallelization. The first identifies subgraphs that contain vertices that can be accessed in parallel, while the latter identifies subgraphs that can be processed in their entirety in parallel. To showcase the benefit of our approach, we build within CAVE parallelized versions of five popular graph algorithms (Breadth-First Search, Depth-First Search, Weakly Connected Components, PageRank, Random Walk) that exploit the full bandwidth of the underlying device. CAVE uses a blocked file format based on adjacency lists and employs a concurrent cache pool that is essential to the parallelization of graph algorithms. By experimenting with different types of graphs on three SSD devices, we demonstrate that CAVE utilizes the available parallelism, and scales to diverse real-world graph datasets. CAVE achieves up to one order of magnitude speedup compared to the popular out-of-core systems Mosaic and GridGraph, and up to three orders of magnitude speedup in runtime compared to GraphChi.

Temporal vectorized visibility for direct illumination of animated models

Direct illumination rendering is an important technique in computer graphics. Precomputed radiance transfer algorithms can provide high quality rendering results in real time, but they can only support rigid models. On the other hand, ray tracing algorithms are flexible and can gracefully handle animated models. With NVIDIA RTX and the AI denoiser, we can use ray tracing algorithms to render visually appealing results in real time. Visually appealing though, they can deviate from the actual one considerably. We propose a visibility-boundary edge oriented infinite triangle bounding volume hierarchy (BVH) traversal algorithm to dynamically generate visibility in vector form. Our algorithm utilizes the properties of visibility-boundary edges and infinite triangle BVH traversal to maximize the efficiency of the vector form visibility generation. A novel data structure, temporal vectorized visibility, is proposed, which allows visibility in vector form to be shared across time and further increases the generation efficiency. Our algorithm can efficiently render close-to-reference direct illumination results. With the similar processing time, it provides a visual quality improvement around 10 dB in terms of peak signal-to-noise ratio (PSNR) w.r.t. the ray tracing algorithm reservoir-based spatiotemporal importance resampling (ReSTIR).

Vehicle logistics intermodal route optimization based on Tabu search algorithm

With the development of logistics enterprises and the adjustment of some relevant laws and regulations, the profit space of vehicle logistics enterprises has been further compressed. To reduce vehicle logistics transportation cost and increase the profit space of vehicle logistics, the vehicle logistics multimodal transport network is constructed and the graph traversal algorithm is used to screen the feasible paths in the vehicle logistics multimodal transport network. Then, the Tabu search algorithm can optimize vehicle logistics multimodal transport route model. Results showed that Tabu search performed better than other methods in solving route optimization problems. The cost of Tabu search algorithm after convergence was 1.2 yuan/km × per set. The performance of Tabu search algorithm on NGSIM data set was better than other methods. On this data set, the area under the curve of Tabu search algorithm was much higher than that of other methods. The optimization results of Tabu search for vehicle logistics intermodal routes were effective. Among the 15 routes, only four routes were not optimized, and other routes were optimized. After optimization, the profits have increased, and the profit of Route 9 had the largest increase, which was 18%. The research successfully constructs the optimization model of vehicle logistics intermodal route, and completes the solution to increase the profit space of vehicle logistics enterprises.