Traversal Algorithm Research Articles

Voxel-based variable width continuous spiral path planning for 3D printing

Discontinuous printing paths in 3D printing lead to wasted time in fabricating products. This paper proposes a voxel-based continuous spiral path planning algorithm for 3D printing parts with less time consumption. Interestingly, we found our method can also improve the surface qualities and mechanical properties of the printed parts. The proposed path planning strategy in this paper is based on voxels which is different from traditional path planning strategies in literature. Firstly, our proposed algorithm determines the voxel size by considering the printable width of the printer nozzle. It then generates discrete elements by voxelizing the model and creates spirals and line segments by restricting the print direction of the connectable discrete elements. Next, the algorithm employs the Euler circuit to generate continuous sub-paths for the spirals and line segments. It utilizes the depth-first traversal algorithm to traverse and open the continuous sub-paths, resulting in printable continuous paths. To maintain surface accuracy for complex models, contour lines are introduced on the model's exterior and connected internally to ensure global path continuity. Lastly, the algorithm controls the path width in different directions by adjusting the extrusion rate of the printhead, preventing path overlap. Experimental results demonstrate that the proposed algorithm outperforms traditional path planning algorithms and the Connected Fermat spirals algorithm. The proposed strategy effectively mitigates the negative effects of path discontinuity, reduces printing time, and improves printed accuracy, and mechanical properties.

Space efficient algorithm for solving reachability using tree decomposition and separators

To solve reachability is to determine whether there is a path from one vertex to the other in a graph. Standard graph traversal algorithms such as DFS and BFS take linear time to solve reachability; however, their space complexity is also linear. On the other hand, Savitch's algorithm takes quasipolynomial time, although the space-bound is O(log2⁡n). In this paper, we study space-efficient algorithms for deciding reachability that runs in polynomial time.We show a polynomial-time algorithm that solves reachability in directed graphs using O(wlog⁡n) space. Our algorithm requires access to a tree decomposition of width w for the underlying undirected graph of the input. This requirement can be waived for graphs for which recursive balanced vertex separators can be computed space-efficiently.

Quantitively evaluation of fillers' alignment for targeted manufacturing of functional composite materials

AbstractFiller‐reinforced composite materials are widely used in numerous domains such as energy, optoelectronics, chemistry, and so forth, owing to their versatile microstructures, excellent properties, and low fabrication cost. By finely regulating the distribution and orientation of fillers in the base material, composite materials with fantastic thermal, chemical, and mechanical properties have been demonstrated, which are also known as “metamaterials”. For the anisotropic fillers, the orientation of fillers is crucial for designing and predicting the properties of composite materials. However, accurate and efficient evaluation of fillers' orientation remains a challenge, which severely hinders the microstructural design and topological optimization of composite materials. To solve this problem, a bidirectional pixel‐traversal method which combines with microscopic image processing was developed to evaluate the orientation of fillers in composite materials. Specifically, a vertical orientation maximization algorithm was designed to evaluate the oriented degree and oriented direction of the whole fillers, and a crossing traversal algorithm was designed to evaluate the oriented angle of individual filler. Further, this method was applied to the evaluation of microscopic images from different researches. The evaluated results show a high accuracy (average error is less than 2%) and wide applicability in the evaluation of different types of fillers. This method reveals the underlying relationship between microscopic filler orientation and macroscopic composites properties, which paves a new way for the direct optimization and processing of composite materials.

Cost Effective Guided Routing (CEGR) Technique – A bi-directional searching method towards the solution of land acquisition problem

In this paper a two-way searching methodology has been proposed, where simultaneously two searching is being carried out, one from Start node to Destination node and other in reverse direction. The proposed methodology is optimal, complete and runs with a complexity of O(nm/2) . The proposed algorithm has successfully been implemented for solution of land acquisition problem, a burning issue of the country now-a-days, where while selection of path in addition to distance, many other influencing parameters been judged. This methodology takes as input a digitized land bank. To evaluate the efficiency, the methodology been compared with many popular graph traversal algorithms. Key Words: Two-way searching, influencing parameters, land acquisition problem, digitized map, optimal, complete

A BOM model transformation method for hierarchical production planning management process of complex products

The Bill of Materials (BOM) is the core data in the hierarchical production planning management of complex products. In the current competitive market environment and increasing demand for individual customization, complex product manufacturing enterprises are exploring methods that fully use common data and efficiently organize the BOM according to order requirements. However, due to the lack of a unified data modeling method for hierarchical production planning, BOM data at different levels cannot be interpenetrated. Moreover, the single-tree BOM lacks flexibility in describing the dynamic change process of data in different planning management stages (i.e., order receiving, variant design, and manufacturing execution).This paper proposes a BOM model transformation method for hierarchical production planning management to efficiently and accurately organize the data in different planning management stages of complex products. First, the Hierarchical Bill of Materials (HBOMs) model is constructed to integrate BOM data of different levels of planning. Subsequently, to describe the data requirements in HBOMs for different plan management stages, a composite HBOMs containing three types was designed. The Generic-HBOM (G-HBOM) aims to manage the common data of the same product type. The Instance-HBOM before-manufacturing (I-HBOM (BM)) and Instance-HBOM after-manufacturing (I-HBOM (AM)) manage the personality data of single product variant design and manufacturing execution stages, respectively. The transformation process models of HBOMs are constructed by analyzing the transformation logic between three types of HBOMs. To support the transformation process of HBOMs, five transformation operators are summarized, and the node traversal algorithm logic is proposed. Finally, a steam turbine generator product is taken as an example to verify the feasibility and effectiveness of this method. This method can effectively improve the efficiency of BOM construction and reduce data redundancy while helping to ensure the integrity, consistency, and accuracy of BOM data.

Key deviation source diagnosis of complex thin-walled structures based on complex networks and weighted transfer entropy

Abstract There are many deviation sources in the assembly process of aircraft complex thin-walled structures. To get important factors that affect quality, it is crucial to diagnose the key deviation resources. The deviation transfer between deviation sources and assembly parts has the characteristics of small sample size, nonlinearity, and strong coupling, so it is difficult to diagnose the key deviation sources by constructing assembly dimension chains. Therefore, based on the deviation detection data, transfer entropy and complex network theory are introduced. Integrating the depth-first traversal algorithm with degree centrality theory, a key deviation diagnosis method for complex thin-walled structures is proposed based on weighted transfer entropy and complex networks. The application shows that key deviation sources that affect assembly quality can be accurately identified by the key deviation source diagnosis method based on complex networks and weighted transfer entropy.

Prediction of minimum ignition energy for combustible dust using graph neural networks and SMILES data augmentation

Dust explosions are among the most hazardous incidents in industrial production. The inherent dangers of dust make its testing not only risky but also costly. Consequently, it is crucial to identify a method for rapidly determining dust explosion hazards. Previous quantitative structure-property relationship (QSPR) methods have been computationally demanding, time-consuming, and inefficient. In this study, the minimum ignition energy is predicted using the MPNN model, a graph neural network that extracts molecular data features to make accurate predictions based on chemical structure. The input data is transformed into a compact representation, facilitating more efficient analysis and learning by machine learning algorithms. By converting the molecular structure into SMILES for training, employing molecular enhancement via the SMILES molecular traversal algorithm, and optimizing the model through hyperparameter optimization, the MPNN was compared with the GCN and GAT models. The confusion matrix, ROC curves, and PR curves were evaluated for all three models. The MPNN model outperformed the other two models in predicting dust explosion hazards. After data augmentation and hyperparameter optimization, the performance of all three models improved significantly: the MPNN model enhanced its performance by 4% on the ROC curve and 4% on the PR curve; the GCN model by 6% on the ROC curve and 7% on the PR curve; and the GAT model by 2% on the ROC curve and 3% on the PR curve. Nevertheless, among the optimized models, the MPNN model still demonstrated the best performance on the ROC and PR curves, achieving an accuracy of 98%. By visualizing the classification results using the t-SNE algorithm, it was found that the MPNN model was better at capturing the relationships between nodes on the molecular graph, resulting in superior performance in the classification task. In contrast, the GCN and GAT models underperformed in utilizing graph node information, leading to less efficient predictions. Analysis of the molecular structures in the prediction results indicates that the minimum ignition energy is >30 mJ for chain compounds and below 30 mJ for cyclic compounds, influenced by both the conjugation system and molecular weight. The MPNN model has demonstrated high accuracy and robustness in predicting dust explosion hazards, providing an effective solution to reduce experimental risks and costs. In conclusion, the study presents a method for rapidly determining dust explosion hazards using the MPNN model, a graph neural network that accurately predicts minimum ignition energy based on chemical structure. The MPNN model outperformed other models in terms of accuracy and efficiency, providing an effective solution to reduce experimental risks and costs. These findings have significant implications for industrial production and can be applied at large-scale to enhance safety measures in real-world conditions.

Station Arrangement Optimization of Photoelectric Theodolites Based on Efficient Traversing Discrete Points

Station arrangement optimization of photoelectric theodolites in shooting ranges presents a non-convex and non-linear problem, and the method required to seek the global optimal solution remains an open question. This paper proposes an efficient traversal algorithm that could solve this problem by utilizing discretization of regions with a finite length of mesh, in which both the terrain of the station arrangement region and the observation airspace region are discretized through triangulation. To enhance the computational efficiency of the traversal algorithm, two strategies are employed to speed up the calculation: reducing the dimension of the observation airspace and using the Euclidean distance matrix to compute the intersection angle. After the global optimal solution with discrete finite precision was obtained through the traversal algorithm, it was then used as the initial points for local mesh refinement and to implement gradient-based optimization in order to further improve the precision of the solution. The proposed approach is demonstrated to be practical through application to numerical examples used for the optimization of station arrangements that involve two to four stations.

Route Planning for Emergency Evacuation Using Graph Traversal Algorithms

The automatic identification of various design elements in a floor-plan image has gained increasing attention in recent research. Emergency-evacuation applications can benefit greatly from automated floor-plan solutions, as they allow for the development of horizontal solutions instead of vertical solutions targeting a specific audience. In addition to that, current evacuation plans rely on static signs without taking into account the dynamic characteristics of each emergency case. This work aims to extract information from a floor-plan image and transform it into a graph that is used for pathfinding in an emergency evacuation. First, the basic elements of the floor-plan image, i.e., walls, rooms and doors, are identified. This is achieved using Panoptic-DeepLab, which is a state-of-the-art deep neural network for the panoptic segmentation of images, and it is available from DeepLab2, an image segmentation library. The neural network was trained using CubiCasa5K, a large-scale floor-plan image dataset containing 5000 samples, annotated into over 80 floor-plan object categories. Then, using the prediction of each pixel, a graph that shows how rooms and doors are connected is created. An application that presents this information in a user-friendly manner and provides graph editing capabilities was developed. Finally, the exits are set, and the optimal path for evacuation is calculated from each node using Dijkstra’s algorithm.

Case study on a novel approach to determining initial values for 3D water vapor tomography in the a priori water vapor field

Insufficient prior information in GNSS water vapor tomographic models can lead to instability in the structure of the design matrix. The addition of high-precision initial values for water vapor density can effectively solve this problem, but common methods for obtaining water vapor density are relatively crude and not fully utilizing the original accuracy of the a priori water vapor field. To address this issue, a new method is proposed that takes into account the influence of the a priori information weight ratio in the tomographic model on tomographic analysis results, based on the negative exponential decrease of atmospheric water vapor density in the vertical direction with height. Moreover, the 3D water vapor tomographic technique based on the ray-tracing fast voxel traversal algorithm has been improved and implemented in the C++ programming language. To verify the feasibility, accuracy, and stability of the improved 3D water vapor tomographic technique and the proposed method, the experiment using actual data from the CORS station in Hong Kong in January and May and the corresponding grid data provided by ERA5 was conducted. As benchmarks, radio-sounding data from the experimental area was utilized. In May, the proposed method exhibited an average daily improvement in the root mean square error of 0.88% (UTC0) and 0.97% (UTC12), and an improvement in the interquartile range of the difference between the water vapor density values at each altitude layer (excluding the top and bottom layers) and the corresponding sounding data of 5.08% (UTC0) and 3.37% (UTC12). In January, the average daily improvement in the root mean square error was −13.78% (UTC0) and −4.84% (UTC12), and the improvement in the interquartile range of the difference between the water vapor density values at each altitude level and those calculated from the corresponding sounding data was −14.49% (UTC0) and −6.31% (UTC12). The results show that the proposed method and the commonly used method maintain high consistency in the accuracy of the water vapor density values solved by the system of tomographic equations and improve stability, especially in May when the water vapor density near the surface is high. The feasibility of the improved 3D water vapor tomographic technique based on the ray-tracing fast voxel traversal algorithm is also demonstrated.

GPU-parallelisation of Haar wavelet-based grid resolution adaptation for fast finite volume modelling: application to shallow water flows

Abstract Wavelet-based grid resolution adaptation driven by the ‘multiresolution analysis’ (MRA) of the Haar wavelet (HW) allows to devise an adaptive first-order finite volume (FV1) model (HWFV1) that can readily preserve the modelling fidelity of its reference uniform-grid FV1 counterpart. However, the MRA entails an enormous computational effort as it involves ‘encoding’ (coarsening), ‘decoding’ (refining), analysing and traversing modelled data across a deep hierarchy of nested, uniform grids. GPU-parallelisation of the MRA is needed to handle its computational effort, but its algorithmic structure (1) hinders coalesced memory access on the GPU and (2) involves an inherently sequential tree traversal problem. This work redesigns the algorithmic structure of the MRA in order to parallelise it on the GPU, addressing (1) by applying Z-order space-filling curves and (2) by adopting a parallel tree traversal algorithm. This results in a GPU-parallelised HWFV1 model (GPU-HWFV1). GPU-HWFV1 is verified against its CPU predecessor (CPU-HWFV1) and its GPU-parallelised reference uniform-grid counterpart (GPU-FV1) over five shallow water flow test cases. GPU-HWFV1 preserves the modelling fidelity of GPU-FV1 while being up to 30 times faster. Compared to CPU-HWFV1, it is up to 200 times faster, suggesting that the GPU-parallelised MRA could be used to speed up other FV1 models.

MITra: A Framework for Multi-Instance Graph Traversal

This paper presents MITra, a framework for composing multi-instance graph algorithms that traverse from multiple source vertices simultaneously over a single thread. Underlying MITra is a model of multi-instance traversal that uniformly captures traversal sharing across instances. Based on this, MITra provides a programming model that allows users to express traversals by declaring vertex ranks and specify computation logic via an edge function. It synthesizes multi-instance traversal algorithms from declared vertex ranks and edge functions adopted from classic single-instance algorithms, automatically sharing computation across instances and benefiting from SIMD. We show that MITra can generate multi-instance algorithms provably better than existing ones, while being more expressive than traditional frameworks. In addition to the ease of programming, we experimentally verify that MITra is on average an order of magnitude faster than approaches based on existing frameworks for common graph algorithms, and is comparable to the state-of-the-art highly optimized one-off algorithms.

Graph-Based Interdependent Cyber-Physical Risk Analysis of Power Distribution Networks

This paper proposes a graph-based model for interdependent cyber-physical risk analysis of power distribution networks. The proposed methodology evaluates device criticality in the interdependent cyber and physical networks that comprise modern power distribution networks. A network traversal algorithm is developed to identify latent interdependent support and dependency structures as network subgraphs. Identifying these subgraphs provides a context-aware approach to model feasible cyber-attack vectors, define metrics for device criticality, and measure overall network resilience to cyber-attacks. The proposed methodology is demonstrated in a cyber-attack detection study. The results indicate that the proposed context-aware criticality metrics, based on the dependency subgraph framework, better identify the risks of cascading dependencies in power distribution networks. Further, several characteristics of the power distribution network such as interdependencies, device vulnerabilities, and attack target criticalities detailed in the proposed methodology have significant impacts on the performance of attack detection strategies.

Adaptive Impulsive Noise Suppression: A Deep Learning-Based Parameters Estimation Approach

In order to obtain favorable communication performance, impulsive noise needs to be suppressed which is a major interference in scenarios such as broadcasting system and power line communication. In this paper, a time-domain impulsive noise suppression method is combined with deep learning, whose parameters are calculated by neural networks. The parameters of the denoising method are of great significance because they have a direct influence on the performance of impulsive noise suppression. By constructing a proper training set, trained neural networks can avoid the huge cost compared with traversal algorithm for determining parameters. In the meanwhile, the networks are able to estimate adaptive parameters under various noise environment avoiding fixed parameters. Furthermore, The computing resources of terminal devices are usually insufficient for a large number of calculations. Therefore, the noise suppression algorithm embedded in terminal equipment is expected to be with low complexity and achieve superior effects. Simulation experiments are carried out concerning signal to interference plus noise ratio and data rate. Simulation results of the deep learning based impulsive noise suppression scheme validate that neural networks have favorable performance in predicting parameters for the denoising algorithm.

Smart contract vulnerability detection based on a semantic code structure and a self-designed neural network

Smart contracts are riddled with vulnerabilities due to flaws in programming languages and the inexperience of developers, causing damage. Nonetheless, the current research on smart contract vulnerability detection is insufficient. In this study, we propose a novel approach, namely, Blass, based on a semantic code structure and a self-designed neural network. Blass constructs program slices with complete semantic structure information (CPSs) and uses an abstract syntax tree and a depth-first traversal algorithm to convert CPSs into code chains during the process of CPS vectorization, which increases its ability to express vulnerability features. Blass also uses a self-designed neural network, Bi-LSTM-Att, as the classification model, which introduces an attention mechanism to capture the key features of vulnerabilities and effectively achieve improved smart contract vulnerability detection performance. The CPSs and the Bi-LSTM-Att can improve the vulnerability detection effectiveness of Blass, and Blass can be applied to malicious contract detection with satisfactory precision, recall, and F1 values.

A Quantitative Framework for Propagation Paths of Natech Domino Effects in Chemical Industrial Parks: Part I—Failure Analysis

Along with global climate change and industrialization, domino effects caused by Natech events occurred frequently in chemical industrial parks over the past decades. Previous research has not yet proposed a reliable method to obtain all possible paths of Natech domino effects, and moreover, a risk assessment and mitigation system has not been established. The present work aims to develop a quantitative framework for propagation paths of Natech domino effects, which can effectively safeguard the sustainable development of chemical industrial parks. The presentation of this work is divided into two parts: Part I (current paper) proposes a path probability calculation method that can simultaneously consider multiple primary accident scenarios and multi-level domino effects triggered by natural disasters. The proposed method transforms the propagation paths of domino effects into the paths of directed graph by constructing the equipment failure state transition matrix and the equipment failure state transition probability matrix. The depth-first traversal algorithm is used to obtain all possible propagation paths and their propagation probabilities, providing data support for the quantitative risk assessment and prevention and control measures presented in the accompanying paper (Part II). The case study shows that the probability of equipment failure caused by multi-level domino effects triggered by Natech accidents is higher than that of conventional accidents. However, the present work only considers the spatial propagation of domino effects, while their spatio-temporal propagation remains as a further direction for this area of inquiry.

Optimizing Photoelectrochemical Photovoltage and Stability of Molecular Interlayer-Modified Halide Perovskite in Water: Insights from Interpretable Machine Learning and Symbolic Regression

Interpretable machine learning models are desired for materials and chemical design processes, while the stable optoelectronic properties of the halide perovskite materials in hostile conditions such as in water are prerequisites for their wider industrial deployment. In this study, we demonstrate an experimentally verified interpretable machine learning pipeline coupled with a symbolic regression method to optimize and understand the stability and photovoltage of the molecular interfacial layer-modified perovskite film in aqueous solution. An accurate machine learning model is achieved via the random forest algorithm; this leads to the successful experimental validation of a champion CH3NH3PbI3/bimolecule/TiO2 system giving a stable photovoltage output in water with two interlayer molecules, basic violet 7 and rhodamine B. The experimental stability and photovoltage outputs of the machine learning-predicted sample in the aqueous solution are enhanced by 4 and 16.7% compared with those of the untreated CH3NH3PbI3 film under the same aqueous conditions; the atomic adsorption structures are then investigated via density functional theory calculations. A t-SR symbolic regression method is developed to design relevant molecular descriptors, which employs a traversal algorithm comparing 8.95 million expressions and obtains a descriptor with 18% improvement. The present study provides a machine learning platform to accelerate the design of stable and high-performance perovskite films in extreme conditions, and the method can be elaborated to other surface systems.

Graph Traversal (GT) on Linear and Quadratic Graphic Processing Units with CPU

Nowadays, Graph Traversal (GT) is a fundamental procedure for developing a measure in computing systems and algorithms. Graph traversal algorithms usually iterate input datasets of a graph to provide a logical convergence in every iteration. Graphic Processing Units (GPUs) have been utilized broadly as accelerators of GT due to their massive parallelism and accessibility of high-transfer speed memory. Furthermore, graph-based computation is pervasive and challenging for memory systems implementation in a specific time period. This paper provides quadratic and single GPUs, allowing developers to extend graph-based GPU computation effortlessly. The paper concentrates on the creditability and validation of sparse graphs in GPUs. Since the benchmark characteristic of Breadth-First Search (BFS) plays a critical role in graphs, this paper considers a primary characteristic benchmark for BFS computation usage. This paper explores the effectiveness and applicability of BFS for analyzing graph systems by utilizing the mentioned characteristics in BFS computation. This paper also utilizes the penalization of GPU BFS during the analysis and simulation results. In addition, using a comparative simulation result, this paper concludes that improved traversal rates speed up the computation and reduce memory usage for the single and quadratic GPU structures.

Augmenting a round-trip path test suite to cover all event-pairs for testing more diverse usage scenarios

Among State-Based Testing (SBT) strategies, the Round-Trip Path (RTP) strategy is known to generate the most cost-effective state-based test suite. However, some studies have pointed out that an RTP suite is not sufficient because it does not test enough realistic usage scenarios, which could lead to poor fault detection. We believe that the traversal algorithm of the RTP strategy, which omits certain event-pairs, is one of the main factors causing the problem. To address this issue, we propose two methods for augmenting an RTP suite to cover all event-pairs. Through three case studies, we confirmed that the RTP suites did not cover 30%, 56.67%, and 37% of all event-pairs, some of which were critical, such as (remove, add) in an ordered set, (brake, brake) in a cruise control system, and (stop, play) in a video cassette recorder. The augmented suites improved the effectiveness of the RTP suites by testing more usage scenarios that include the event-pairs, albeit at a slightly higher cost. Compared to the All Transition-Pair (ATP) suites, the augmented suites provide a more cost-effective improvement to an RTP suite, requiring a lower cost increase rate. We believe that the augmented suites could be cost-effective alternatives between an RTP suite and an ATP suite.

An integrated optimization of lot-sizing and condition-based maintenance policy of a two-stage deteriorating system

In this study, a two-stage deteriorating production system is considered. The initiation time of the deterioration follows an exponential distribution. A homogeneous Gamma process is used to model the deterioration propagation process. The equipment produces a single product with a constant production rate and demand rate. To ensure the efficiency of the production, inspection is carried out at the end of each production run. The lot-sizing and the condition-based maintenance threshold are optimized by minimizing the long-run average cost rate, which is derived in the framework of a semi-Markov decision process. The traversal algorithm is used to obtain optimal results. A numerical example is presented to illustrate the applicability of the proposed model. Some sensitivity analysis are carried out in order to highlight the interest and properties of the model. It may provide a theoretical reference for production and maintenance schedules.