Depth-first Search Algorithm Research Articles

Face-Gaze Biometric Fusion for Enhanced Personal Authentication

Objectives: To develop a robust authentication system by combining two authentication modals. The system aims to enhance the security by integrating the gaze-based biometrics in the authentication process, which is difficult to perform spoofing attacks. Methods: The system uses gaze-tracking and face recognition modals to authenticate the user. The user will enter his/her credentials, then the first step of authentication will start where the face recognition modal will authenticate the user by detecting the user’s face, if one is not authenticated in this phase then the user will be omitted. After that the second phase will starts where the system will generate a random 10X10 maze using Depth-First Search algorithm, and then it will extract the x, y coordinates of the maze. The system will capture the user’s real-time eye movement data, and extracts the x, y coordinates of the pupil through a webcam, and compares those coordinates with the maze coordinates if both are matched then the system will provide access to the user else it will omit the user. Findings: Findings show that this approach improves the security of traditional biometric systems, offering a unique form of user authentication. The system tested for accuracy in different environments, and its performance was evaluated based on how well the user’s gaze matches the maze path. Tests across various environments show that the system achieves an accuracy of 92% in matching user gaze to the maze coordinates, also achieved 0% FTE (Failure To Enroll) and 0% FTA (Failure To Acquire). Novelty: The novelty of this project lies in combining biometric eye gaze tracking with real-time maze-based navigation for user authentication. Unlike the traditional biometrics that rely on static features like fingerprints or facial recognition, this system incorporates dynamic tracking of user behavior, specifically gaze patterns, to enhance security. This makes it more resilient to spoofing attacks. Keywords: Biometric Authentication, Deepfake Resistance, Authorization Mechanism, Operational Environments, Human-Centric Security

Research on Path Planning of Agricultural UAV Based on Improved Deep Reinforcement Learning

Traditional manual or semi-mechanized pesticide spraying methods often suffer from issues such as redundant coverage and cumbersome operational steps, which fail to meet current pest and disease control requirements. Therefore, there is an urgent need to develop an efficient pest control technology system. This paper builds upon the Deep Q-Network algorithm by integrating the Bi-directional Long Short-Term Memory structure to propose the BL-DQN algorithm. Based on this, a path planning framework for pest and disease control using agricultural drones is designed. This framework comprises four modules: remote sensing image acquisition via the Google Earth platform, task area segmentation using a deep learning U-Net model, rasterized environmental map creation, and coverage path planning. The goal is to enhance the efficiency and safety of pesticide application by drones in complex agricultural environments. Through simulation experiments, the BL-DQN algorithm achieved a 41.68% improvement in coverage compared with the traditional DQN algorithm. The repeat coverage rate for BL-DQN was 5.56%, which is lower than the 9.78% achieved by the DQN algorithm and the 31.29% of the Depth-First Search (DFS) algorithm. Additionally, the number of steps required by BL-DQN was only 80.1% of that of the DFS algorithm. In terms of target point guidance, the BL-DQN algorithm also outperformed both DQN and DFS, demonstrating superior performance.

Integrating hybrid deep learning and path allocation for real-time inbound passenger flow prediction and anomaly detection in urban rail transit

This paper study the problem of real-time prediction of inbound passenger flow and the detection and alerting of abnormal passenger flows in urban rail transit (URT) networks. We propose a fused framework that combines a hybrid deep learning model and an evaluation strategy. Specifically, the learning model incorporates Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and attention mechanisms to effectively capture spatial and temporal correlations in passenger flow data. The evaluation strategy utilizes a depth-first search algorithm to determine the optimal travel paths for each individual passenger. And based on the paths, we develop a real-time method for estimating the origin–destination (OD) matrix that utilizes both long-term and short-term historical destination trend vectors to reduce dimensions while improving predictive accuracy. Through extensive testing using data from the Shanghai rail transit system, we demonstrate that this fused framework achieves high prediction accuracy for inbound passenger flow at various stations while efficiently identifying and warning sudden large-scale events involving significant increases in passenger flow volume. This research contributes towards improving overall passenger experience as well as operational resilience within urban rail systems when dealing with large-scale influxes of passengers.

Machine learning-assisted design of flow fields for proton exchange membrane fuel cells

Optimizing the flow field is a key approach to enhancing the performance of proton exchange membrane fuel cells. Most previous research on flow field design relies on physical intuitions, lacking systematic exploration of the complex geometries of flow fields. To address these issues, we first generate a comprehensive flow field library containing 28,348 geometries using the Depth-First Search algorithm. Subsequently, we randomly select 480 flow fields from this library and characterize their corresponding fuel cell performance using computational fluid dynamics. These 480 flow fields serve as the dataset for machine learning training. Using the trained neural network, we rapidly predict fuel cell performance and identify high-performance flow fields. Simulation results demonstrate that the predicted high-performance flow fields effectively improve mass transfer and current density distribution, thereby enhancing current density and maximum power density. Experimental validation shows a 10.37 % increase in maximum power density for our optimized flow field design compared to traditional serpentine channels. Additionally, our geometric analysis identifies key features of high-performance flow fields, guiding future designs.

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.

Distinguishing between topological isomorphism and topological equivalence of power electronic converters

In the process of deducing the topology of power electronic converters, scholars often use topological equivalence or topological isomorphism to identify topologies with different structures but the same performance to avoid repeated research. However, the connotations of topological equivalence and topological isomorphism are not the same. This paper will clarify the method for accurately identifying topologies with the same performance by distinguishing the differences and connections between the two. First, it is derived that the necessary condition for topological isomorphism is that the determinants of their adjacency matrices are equal; then, it is derived that the necessary and sufficient conditions for topological equivalence are that their component composition is the same and their simple circuits correspond one to one; finally, the above two conditions are analyzed from the perspective of topological subgraphs, and it is found that topological isomorphism is a sufficient but not necessary condition for topological equivalence, and topological equivalence is a necessary and sufficient condition for the same topological performance. Therefore, in practice, equivalence rather than isomorphism should be used to identify topologies with the same performance. This paper verifies the correctness of the theoretical analysis through a case analysis. In addition, this paper also introduces a method for automatically determining equivalent topologies based on the depth-first search algorithm to help quickly and accurately identify converter topologies with the same performance.

Analysis, Generation, and Validation of New Boards for the Game Micro Robots

This document presents a study of the board game Micro Robots and the development of a pipeline for the generation and validation of new boards of variable sizes. The implementation was carried out in the C# language, using graph theory and a depth-first search algorithm to explore various board configurations and their possible solutions. The main objective is the creation of boards that are different from the original ones, offering greater variety in the gaming experience. The impact of the modifications on the game’s dynamics and complexity was evaluated by comparing the generated boards with those from the commercial game. The results show that no major differences were found between the new boards’ structure and the original ones, maintaining an average complexity across all configurations. This work not only contributes to the study of board game design, but also opens new opportunities for innovation in this field.

Intelligent toy tracking trajectory design based on mobile cloud terminal deployment and depth-first search algorithm.

The popularization of intelligent toys enriches the lives of the general public. To provide the public with a better toy experience, we propose the intelligent toy tracking method by the mobile cloud terminal deployment and depth-first search algorithm. Firstly, we construct a toy detection model via Transformer, which realizes the positioning of toys in the image through the refined region adaptive boundary representation. Then, using these detected continuous frames, we improve the toy tracking based on a depth-first search. Long-short-term memory (LSTM) constructs the continuous frame tracking structure, and the depth-first search mechanism is embedded to realize the accurate tracking of multiple targets in continuous frames. Finally, to realize the terminal marginalization of the proposed method, this chapter proposes a lightweight model deployment method based on mobile cloud terminals to realize the maintenance of the optimal machine state of intelligent toys. The experiment proves that our proposed target method can reach the world-leading level and obtain the mAP value of 0.858. Our tracking method can also perform excellently with a MOTA value of 0.916.

Smart product service resources composition optimization for smart product service system in context of industrial IoT platform

Smart product service resources composition (SPS-RC) is a core issue of smart product service system (PSS) development in the emerging context of industrial internet of things (IIoT) platform, aiming to organize distributed resources for smart product service operation tasks. Existing research mainly focuses on the traditional physical PSS, while contains scant exploration of the new features emerging in context of IIoT platform, leading to an urgent updating need for SPS-RC approaches. Therefore, this study proposes a systematic framework for SPS-RC in context of IIoT platform based on cyber physical system (CPS)-based SPS blueprint, graph theory and adaptive genetic algorithm. In the proposed framework, the SPS-RC problem is formulated in accordance with the realistic smart product service operation process using the CPS-based SPS blueprint. The graph theory and depth-first search algorithm are employed to calculate the composition performance under the environment of heterogenous connection structures between SPS tasks. In addition, an optimization model considering heterogenous composition structures is established with aggregating various quality of service into one comprehensive objective and solved by an adaptive genetic algorithm. Finally, an application of this systematic framework for smart port container crane service demonstrates the feasibility and effectiveness of the proposed approach.

Optimizing the Implementation of the BFS and DFS algorithms using the web crawler method on the kumparan site

Efficient access to timely information is critical in today's digital era. Web crawlers, automated programs that navigate the Internet, play an important role in collecting data from websites such as Kumparan, a leading news site in Indonesia. This research shows the effectiveness of the Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms in indexing Kumparan content. The results of the research show that BFS consistently indexes more files comprehensively but with longer execution times compared to DFS, which provides faster initial results but with fewer files. For example, at depth 4 BFS indexed 949 files in 886.94 seconds, while DFS indexed 470 files in 233.02 seconds. These findings highlight the balance between precision and speed when selecting a crawling algorithm tailored to the needs of a particular website. This research provides insights into optimizing web crawler technology for complex websites such as Coil and suggests avenues for further research to improve permission efficiency and adaptability across a variety of crawling scenarios.

A Graph-Based Scheme Generation Method for Variable Traffic Organization in Parking Lots

To deal with the traffic congestion issues caused by the imbalance between supply and demand in parking lots, this study proposes a graph-based scheme generation method for variable traffic organization in parking lots. A graph-based methodological framework is developed to dynamically generate feasible traffic organization schemes and adapt the road networks of parking lots based on fluctuating demands. First, we design a parking lot-tailored enhanced primal approach by adding a directedness attribute while maintaining road continuity to ensure correspondence between generated graphs and traffic organization schemes. A graph generation algorithm is then designed to generate all feasible schemes in the scenario, deploying the depth-first search algorithm to check the connectivity of each graph and narrowing down feasible options based on domain knowledge. Finally, the initial parking space distribution and parking demand are used as inputs to calculate the total vehicle cruising time under each scheme, serving as the key indicator to select the optimal organization scheme. A single-level parking lot model is developed to verify the performance of our method under six initial parking space distributions. This model is built using the multi-agent simulation platform AnyLogic version 8.8.6, which enables the quick transformation of organization schemes by customizing the behavior of different agents. The results show that the optimal organization scheme generated by the proposed method can reduce vehicle cruising time by 15–46% compared to conventional traffic organization, varying according to parking space distributions. The significance of this study lies in its potential to mitigate traffic congestion in parking lots, thereby enhancing overall efficiency and user satisfaction. By dynamically adapting to fluctuating parking demands, this method provides a robust solution for urban planners and parking lot operators aiming to optimize traffic flow and reduce unnecessary delays.

Aircraft Taxi Path Optimization Considering Environmental Impacts Based on a Bilevel Spatial–Temporal Optimization Model

Aircraft taxiing emissions are the main source of carbon dioxide and other pollutant gas emissions during airport ground operations. It is crucial to optimize aircraft taxiing from both spatial and temporal perspectives to improve airport operation efficiency and reduce aviation emissions. In this paper, a bilevel spatial and temporal optimization model of aircraft taxiing is constructed. The upper-level model optimizes the aircraft taxiing path, and the lower-level model optimizes the taxiing start time of the aircraft. By the iterative optimization of the upper- and lower-level interactions, the aviation fuel consumption, flight waiting time, and number of taxiing conflicts are reduced. To improve the calculation accuracy, the depth-first search algorithm is utilized to generate the set of available paths for aircraft during the model solution process, and a model solution method based on the genetic algorithm is constructed. Simulation experiments using Tianjin Binhai International Airport as the research object show that adopting the waiting taxiing strategy can effectively avoid taxiing conflicts and reduce aviation fuel consumption by 753.18 kg and 188.84 kg compared to the available path sets generated using Dijkstra’s algorithm and those created manually based on experience, respectively. Conversely, adopting an immediate taxi-out strategy caused 54 taxiing conflicts and increased aviation fuel consumption by 49.44 kg. These results can provide safe and environmentally friendly taxiing strategies for the sustainable development of the air transportation industry.

生鲜农产品冷链集货车辆路径与货物配载联合优化研究

As a bridge connecting agricultural production and consumption, the circulation of agricultural products has the function of connecting supply and demand, guiding production and promoting consumption. However, the development of rural logistics in China is slow, and most logistics centers still rely on experience to plan the pick-up vehicle routings, resulting in long transport time and high cost. In order to improve the efficiency of pick-up and reduce transportation costs, a joint optimization model of cold-chain pick-up vehicle routing and cargo allocation for fresh agricultural products was proposed in this study. Soft time window constraint and three-dimensional loading constraints were considered, and the lowest pick-up cost was used as optimization goals in this model. In addition, adaptive large neighborhood search algorithm (ALNS) and heuristic depth-first search algorithm (HDFS) were combined to solve the model. A case study of Kunming International Flower Auction Center was conducted to compare the schemes of pick-up vehicle routing before and after optimization. Results demonstrate that the pick-up cost after optimization decreases by 9.6 %, the number of vehicles decreases by one, the total volume utilization rate of vehicles increases by 23 %, and the total load utilization rate of vehicles increases by 15 %. This study provides a model reference and solution method for enterprise operators to formulate schemes of pick-up vehicle routing quickly and reasonably.

Situation modeling and evaluation for complex systems: A case study of satellite attitude control system

Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit satellite failure inevitable. Modeling and evaluation of satellite conditions can identify potential problems of key system components in time. Firstly, in order to accurately describe the system components and measure the reliability, the correlative damage coefficient is introduced to describe the component degradation process, and the component reliability model of the satellite attitude control system is established. Then, to solve the problem of large state space and changeable condition of system reliability model, the concept of tangent order pair is introduced into the multi-state dynamic fault tree of system. The depth-first search algorithm is used to sort the underlying event components of the fault tree, and the multi-state multi-value decision graph model of different state events is constructed. The minimum cut set of the model is obtained to evaluate the system state. The experimental results show that the proposed method can effectively simulate the system situation under different fault modes, effectively reduce the state space of the system reliability model, and improve the convergence and accuracy of the system fault probability evaluation.

Depth-First Search Performance in a Random Digraph with Geometric Outdegree Distribution

We present an analysis of the depth-first search algorithm in a random digraph model with independent outdegrees having a geometric distribution. The results include asymptotic results for the depth profile of vertices, the height (maximum depth) and average depth, the number of trees in the forest, the size of the largest and second-largest trees, and the numbers of arcs of different types in the depth-first jungle. Most results are first order. For the height, we show an asymptotic normal distribution. This analysis, proposed by Donald Knuth in his next to appear volume of The Art of Computer Programming, gives interesting insight in one of the most elegant and efficient algorithm for graph analysis due to Tarjan.

Research on optimization method for flyby observation mission adapted to satellite on-board computation

In this paper, the optimization problem of orbital transfer strategy for orbital flyby observation missions is studied. A hybrid optimization method is proposed, which is improved to make it more suitable for satellite on-board computing. This new algorithm is designed to solve the initial value sensitivity problem of the sequential quadratic programming algorithm (SQP). It is consisted of the depth-first search algorithm (DFS) and the SQP algorithm and thus has the characteristics of fast convergence, high reliability, and good robustness. With this method, the DFS with a large step size is calculated first, and then the optimal value in the calculation result is used as the initial value of the SQP algorithm for further optimization. This method can obtain the approximate optimal solution available in engineering. The numerical simulation of an orbital transfer optimization problem is set to verify the effectiveness of the new hybrid algorithm. The simulation results compared with the genetic algorithm (GA) show that the proposed hybrid algorithm can effectively reduce the on-board resource occupation when getting similar results and thus can meet the needs of satellite on-board computing.

Enhancing mitosis quantification and detection in meningiomas with computational digital pathology

Mitosis is a critical criterion for meningioma grading. However, pathologists’ assessment of mitoses is subject to significant inter-observer variation due to challenges in locating mitosis hotspots and accurately detecting mitotic figures. To address this issue, we leverage digital pathology and propose a computational strategy to enhance pathologists’ mitosis assessment. The strategy has two components: (1) A depth-first search algorithm that quantifies the mathematically maximum mitotic count in 10 consecutive high-power fields, which can enhance the preciseness, especially in cases with borderline mitotic count. (2) Implementing a collaborative sphere to group a set of pathologists to detect mitoses under each high-power field, which can mitigate subjective random errors in mitosis detection originating from individual detection errors. By depth-first search algorithm (1) , we analyzed 19 meningioma slides and discovered that the proposed algorithm upgraded two borderline cases verified at consensus conferences. This improvement is attributed to the algorithm’s ability to quantify the mitotic count more comprehensively compared to other conventional methods of counting mitoses. In implementing a collaborative sphere (2) , we evaluated the correctness of mitosis detection from grouped pathologists and/or pathology residents, where each member of the group annotated a set of 48 high-power field images for mitotic figures independently. We report that groups with sizes of three can achieve an average precision of 0.897 and sensitivity of 0.699 in mitosis detection, which is higher than an average pathologist in this study (precision: 0.750, sensitivity: 0.667). The proposed computational strategy can be integrated with artificial intelligence workflow, which envisions the future of achieving a rapid and robust mitosis assessment by interactive assisting algorithms that can ultimately benefit patient management.

Optimization of IOTA Tangle Cumulative Weight Calculation Using Depth-First and Iterative Deepening Search Algorithms

Optimization of IOTA Tangle Cumulative Weight Calculation Using Depth-First and Iterative Deepening Search Algorithms

Streamlining LEGO® model design: an automated optimisation approach

LEGO® models have become increasingly popular beyond their traditional toy and entertainment applications, and are now used in fields such as education, cognitive science, engineering and robotics. However, designing LEGO® models for such diverse applications poses new challenges in terms of automation and optimisation. To address such issues, this paper proposes a design support system that automatically generates LEGO® models from any 3D model of a target object using a specific set of standard LEGO® bricks in a layer-by-layer fashion. The system converts the 3D model into a LEGO® model using the minimum number of LEGO® bricks required. The core of the proposed system lies in the use of a greedy algorithm for merging optimisation of each layer of bricks and a depth-first search algorithm for optimising the connection between multiple layers of bricks. Experimental results on several 3D graphics validate the effectiveness of the proposed methodology and its ability to output the corresponding LEGO® model and detailed layout illustrations for each layer of bricks. The proposed system can provide LEGO® builders with an automated optimisation approach for designing LEGO® models, minimising errors compared to manual design.

Parallel path detection for fraudulent accounts in banks based on graph analysis.

This article presents a novel parallel path detection algorithm for identifying suspicious fraudulent accounts in large-scale banking transaction graphs. The proposed algorithm is based on a three-step approach that involves constructing a directed graph, shrinking strongly connected components, and using a parallel depth-first search algorithm to mark potentially fraudulent accounts. The algorithm is designed to fully exploit CPU resources and handle large-scale graphs with exponential growth. The performance of the algorithm is evaluated on various datasets and compared with serial time baselines. The results demonstrate that our approach achieves high performance and scalability on multi-core processors, making it a promising solution for detecting suspicious accounts and preventing money laundering schemes in the banking industry. Overall, our work contributes to the ongoing efforts to combat financial fraud and promote financial stability in the banking sector.