bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Accurate Modeling Method of Body-thread of Penetration Fuze in FEM
Authors: Ma Mengxin , Yang Dong , Xu Tengfei 
Published: May 2024

Abstract
To address the issue that the influence of body-thread on the acceleration signal characteristics in penetration fuze is ambiguous, an accurate modeling method of body-thread is proposed in the finite element method (FEM). The body-thread element segments are obtained by the three-dimensional entity thread model, the initial layer nodes set and elements on the outer surface of the thread are established, and the initial layer elements are copied, rotated, and translated. Repeat this transformation, and get a complete thread model. This method is applied to simulation analysis of a warhead penetrating 4-layer concrete plates, and the influence of thread connection on acceleration signal characteristics in penetration fuze is studied. By comparing the acceleration signal and frequency spectrum of the proposed thread model with that of the simplified thread model, the amplitude of the acceleration signal of the proposed one is smaller than that of the simplified one in some certain frequency spectrum. It shows that the thread connection structure has with damping effect and effectively reduces the high-frequency interference of acceleration signal in penetration fuze.
P-index: A Generalized Statistical Aggregation Metric
Authors: Jing Yunpeng 
Published: May 2024

Abstract
Data aggregation serves as a crucial technique in data analysis, particularly in the context of data dimensionality reduction. The most common approach involves employing statistical aggregation metrics such as mean, median, and others to characterize a dataset. However, different statistical aggregation metrics may yield disparate results, making the discrimination and selection of suitable metrics for describing data a research topic. Addressing this, the paper proposes a generalized statistical aggregation metric, categorizing several commonly used metrics under specific scenarios. This model offers a novel perspective for choosing among different statistical aggregation metrics and assists researchers in developing a deeper understanding of common statistical aggregation metrics. The introduction of this generalized metric provides a flexible and comprehensive method for considering multiple statistical measures simultaneously, enabling a more accurate grasp of the dataset’s essence and offering reliable insights for decision-making. The study contributes a new viewpoint to the selection and application of statistical aggregation metrics, advancing discussions in multi-objective optimization and data interpretability.
Minimum Volume Constraint with Perturbation for Non-negative Matrix Factorization
Authors: Sun Li , Yang Song , Yu Ruilin , Zhang Xinyuan 
Published: May 2024

Abstract
Nonnegative matrix factorization (NMF) has been applied in hyperspectral unmixing. The nonconvexity of the NMF’s cost function leads to solutions that are only locally optimal. Adding regularized terms to the NMF helps improve the solutions. In this study, we proposed a regularized NMF model, the regularized tern is the minimum volume constraint with perturbation. The NMF model is solved with multiplicative updated rules. Numerical results verified that adding a disturbance term to the minimum volume constraint effectively improves the spectral curve’s local accuracy while maintaining the original model’s advantages.
The Signed Total Mixed Roman Domination Numbers of Graphs
Authors: Hong Xia , Zhang Li , Guo Xiaobing 
Published: May 2024

Abstract
The problem of signed domination of graphs is a typical optimization problem. It requires that each vertex and edge be assigned a feasible label so that the sum of assigned labels is minimized. First, we propose the concept of a signed total mixed Roman domination number and introduce the related research progress. Finally, we determine several lower bounds of the signed total mixed Roman domination numbers which depend on the parameters of vertex number and edge number, degree and leaf number.
Research on Multi-target Tracking Algorithm Based on Track Segment Association
Authors: Yin Song , Liang Qing 
Published: May 2024

Abstract
In the process of multi-target tracking, the complex underwater acoustic channel, target model mismatch, target strong maneuvering and other factors are easy to cause track interruption and rerun, which have a bad influence on the situation assessment and tactical decision of the Unmanned sonar system. Aiming at the problem of track interruption, this paper integrates the track adhesion algorithm into the active multi-target tracking algorithm. Simulation results show that the track adhesion algorithm can effectively improve the correct track association rate and track lifetime. By comparing the effect of the new and old methods with the sea test data, the track adhesion can effectively solve the problem of track interruption in the case of target track maneuvering and improve the track life.
Modelling and Impact Analysis of Combined Dam Failure of Small Tandem Reservoir Groups in Hilly Areas
Authors: Liu Hu , Sang Guoqing , Jiang Daijing 
Published: May 2024

Abstract
Numerous small reservoirs in hilly areas play a significant role in flood control and profit-making in the basin, but there is also a risk of dam failure when encountering special circumstances such as excessive flooding, which may cause large losses downstream. In this paper, we take the small reservoirs in Shandong Province as the research object, analyze the hydraulic topology of three types of tandem reservoirs, generalize the shape of the breach by using the transient lateral local one-break to the end, calculate the dam-break flooding process by using the quadratic parabolic method, and simulate the dam-break by using the MIKE21 model based on the principle of the two-dimensional incompressible Reynolds mean stress equation for the incompressible fluid, and analyze the peak flow under various combinations of dam-break flow by setting various combinations of the number of dam-breaks and the timing of dam-break simulation. By setting various combinations of dam failure numbers and timing, we analyzed the changing rules of peak dam failure flow, inundation area and other elements under various combinations, and sought the most unfavorable combinations and parameters such as the timing of dam failure, and the results of the analyses can provide technical support for the assessment of the risk of dam failure in the tandem reservoirs in the hilly area and the risk correspondence.
An Improved Sobol Sensitivity Analysis Method
Authors: Chen Lixin , Xu Zhongxiang , Huang Desuo , Chen Zhining 
Published: May 2024

Abstract
The Sobol method is a variance-based global sensitivity analysis method that evaluates single-input and multiple-input interaction effects by calculating the contribution of a single input to the output variance and the contribution of multiple inputs to the output variance. The Sobol method requires each input to obey a uniform distribution of U [0,1], but it is difficult to meet the requirements in practice. Taking the sum function as an example, this paper analyzes the inapplicability of the existing Sobol method when the input does not obey the uniform distribution U [0,1]. To solve the inapplicability of the Sobol method and broaden the application scope, an improved Sobol sensitivity analysis method is proposed. First, the effect of the joint probability density function not 1 on sensitivity calculation is studied; second, the input parameters are changed to uniform distribution U [0,1] through variable substitution; finally, a complete algorithm model is presented and logical sensitivity analysis results are obtained. Application verification shows that the improved Sobol method is more scientific, applicable and practical.
Analysis of Vibration Characteristics of Horizontal Axis Wind Turbine Tower under Random Wind Action
Authors: Yang Hexu , Li Xiaopeng , Liu Wenwen , Yang Zemin 
Published: May 2024

Abstract
As the main supporting structure of wind turbines, the service quality of towers directly affects the operational safety of wind turbines. Researching tower vibration characteristics, evaluating its service quality and early warning of damage is an important means to improve the operational safety of wind turbines. Firstly, the external load conditions of the wind turbine tower were determined through wind load calculation, and the load and dynamic theory of the variable pitch torque wind turbine tower were summarized. Secondly, based on the Euler-Lagrange energy equation, the Lagrange equation of the simplified dynamic model of the wind turbine tower is established, and the vibration characteristics of the tower under different wind speeds are analyzed. The research results can provide a theoretical basis for the research on the characteristics of the wind turbine tower.
Inference of Dynamic Spatial Panel Data Model and Its Application in Carbon Emission Analysis
Authors: Gu Wenbo , Kang Fangyuan 
Published: May 2024

Abstract
The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.
Integrated Simulation Research of Multi-natural Energy-driven Unmanned Surface Vehicle
Authors: Chen Ao , Chen Tianding , He Hong , Wang Hui , Fan Yimei , Yu Xin 
Published: May 2024

Abstract
In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV’s equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations.
Improved GMMKNN Hybrid Recommendation Algorithm
Authors: Hao Yaxian , Feng Lijiao 
Published: May 2024

Abstract
This paper proposes a hybrid recommendation algorithm that combines the advantages of Gaussian Mixture Model (GMM) and K-Nearest Neighbors (KNN) algorithms. The algorithm first applies GMM to cluster the training data, grouping users with similar interests using clustering techniques. It then utilizes the KNN algorithm for prediction. During the KNN recommendation process for a target user, the algorithm searches for neighboring users with similar interests and features within the same cluster. This significantly reduces the search scope of the nearest neighbors, thanks to the assistance of the GMM algorithm. The algorithm then selects the K most similar neighbors from the target user’s cluster as the candidate pool for personalized recommendations. Additionally, the algorithm improves the weight calculation method by incorporating a Gaussian kernel function for weight estimation. Experimental results on the dataset demonstrate that the proposed improved algorithm effectively enhances the accuracy of prediction results.
An Image Feature Extraction Algorithm Based on Tissue P System
Authors: Huang Yourui , Song Hongping , Han Tao , Xu Shanyong , Jiang Lingya 
Published: May 2024

Abstract
As digital images continue to generate an increasing amount of data, image feature extraction has become a crucial component of image recognition. This paper proposes an image feature extraction method based on membrane computing to extract image features. The author first uses the rotation invariant local phase quantization (RILPQ) to extract image features and combines the tissue P system with the binary particle swarm optimization (MBPSO) to select the best image features and maximize the classification accuracy. Based on 4 public datasets, 28 datasets are newly constructed, and the proposed method is verified on 28 datasets. Specifically, firstly, local binary pattern (LBP) algorithm and RILPQ are used to extract image features, and then MBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA) and membrane genetic algorithm (MGA) are used to select the optimal features. The experimental results demonstrate that our proposed image feature extraction method achieves high classification accuracy, stability, and convergence.
Towards Robust Subspace Clustering via Joint Feature Extraction and Cauchy Loss Function
Authors: Xu Yesong , Hu Ping 
Published: May 2024

Abstract
The purpose of the subspace clustering approach is to discover the similarity between samples by learning a self-representation matrix, and it has been widely employed in machine learning and pattern recognition. Most existing subspace clustering techniques discover subspace structures from raw data and simply adopt L2 loss to characterize the reconstruction error. To break through these limitations, a novel robust model named Feature extraction and Cauchy loss function-based Subspace Clustering (FCSC) is proposed. FCSC performs low dimensional and low-rank feature extraction at the same time, as well as processing large noise in the data to generate a more ideal similarity matrix. Furthermore, we provide an efficient iterative strategy to solve the resultant problem. Extensive experiments on benchmark datasets confirm its superiority in the robustness of some advanced subspace clustering algorithms.
Self-testing Algorithm of Metro One-button Switch Station Equipment Based on a Neural Network
Authors: Chen Yun , Xu Xiaozhou , Zhang Hao , Liao Hongyun , Xia Haiyang , Wu Qian , Ren Tianyang 
Published: May 2024

Abstract
According to the operation and management requirements of subway stations, equipment managed by the station, such as lighting, escalators, broadcasting, etc., must be operated with one-button switch according to a certain process every day. The first step in one-button switch station operation is to perform routine self-testing on these devices. Traditional self-testing operations mainly rely on manual handling by operators, which greatly affects their work efficiency. To optimize the operational processes, it is necessary to establish intelligent self-testing algorithms. An AC-CNN (Attention Causal Convolutional Neural Network) model to increase the self-testing efficiency in subway switching stations was established in this paper. AC-CNN model is a deep learning prediction network based on attention mechanism, which can capture the causal convolutional layer of the time before and after relationship as the basic network structure, optimize the convergence process through residual connections, and fuse global and local attention representations through matrix multiplication in the attention mechanism module to reduce useful information loss. The algorithm used the one-hot encoding and sliding window feature engineering operations to preprocess the historical self-testing operation data and completed the prediction of the self-testing information of metro one-button switch station equipment through the deep analysis of AC-CNN. The on-site application results indicated that AC-CNN had greater advantages in terms of accuracy and operation time compared to those of existing BP neural networks, GBDT, and LSTM algorithms, which can significantly improve the work efficiency of subway staff on one-button switch station.
Research on Semantic Segmentation Algorithm for Multiscale Feature Images Based on Improved DeepLab v3+
Authors: Wang Qing-li , Liu Hai-ping , Wen Tao , Hu Jun 
Published: May 2024

Abstract
Aiming at the question of erroneous segmentation and missed segmentation that occurs when the DeepLab v3+ model cannot fully utilize high-resolution shallow features, a feature image semantic segmentation algorithm based on improved DeepLab v3+ with multiscale features is proposed. First, in the backbone network, multi-scale pyramid convolution is introduced; second, the standard convolution in the pooled pyramid of null-space convolution has been taken by depth-separable convolution, which declines the number of parameters in the overall model; finally, a multi-scale approach is used in the decoding layer to capture the acquired global background, and the background features are combined with the shallow features, and the fused shallow features are enriched by the fusion-attention mechanism to provide the semantic information for images. The experimental results show that in the cityscape validation set, this paper’s method has a better edge segmentation effect, and the average cross-merge rate reaches 74.76%, which is 2.20% higher than the original algorithm. By comparing with other algorithms, the effectiveness of this paper’s method in improving mis-segmentation and omission segmentation is verified.
Influence of Different Wingtip Devices on Aerodynamic Characteristics of UAV
Authors: Xiao Yan-ping , Sun Pei-yan 
Published: May 2024

Abstract
The wingtip device plays an important role in increasing the range of UAVs because the winglets can effectively reduce the induced drag. The purpose of this paper is to explore the influence of wingtip device parameters on the lift-to-drag ratio of UAVs. Using the Fluent analysis system, the drag reduction performance of the winglet-winglet structure based on a UAV was analyzed at different angles of attack. Firstly, from the perspective of aerodynamic performance, the optimal cant and sweep angles of the blended winglets were selected by simulations. Then, the best-blended winglet and the blended split were simulated and analyzed, and the difference between the two on the lift-to-drag ratio and the flow field around the wing were compared. Compared with the traditional wind tunnel test, the CFD technology used greatly shortens the research cost and experimental cycle and provides help for the design of the UAV wingtip work.
Study on the Evolution Pattern of Complex Topographic Flow Field in Typical Tableland and Hogback Ridge
Authors: Peng Xiufang , Wu Zhepan , Shi Chen , Xue Feifei , Cao Xiaoyan 
Published: May 2024

Abstract
The flow field in complex mountainous wind farms is highly intricate, affecting the accuracy of wind resource assessments and the reliability of wind turbine operation. In order to study the evolving characteristics of the flow field in typical complex terrains under different inflow wind directions and wind speeds, this paper, based on the open-source software OpenFOAM, adopts a neutral atmospheric boundary layer model combined with Reynolds-averaged turbulence model to construct a high-precision flow field model for complex terrains. The research reveals that this model requires fewer computational resources and can effectively simulate the flow characteristics around complex terrains. Wind resource distribution in tableland terrains and hogback ridge terrains is more influenced by the height of the mountains, and the recovery speed of the wake behind the mountains decreases with a reduction in slope. In hogback ridge terrains, under a wind direction of 0 °, the wind acceleration effect at the top of the hogback ridge is stronger than under a wind direction of 270°, as it experiences more intense compression from the mountains. This paper can provide reference for micro-siting in complex terrain.
Numerical Simulation Study on Blasting-induced Damage Characteristics of Frozen Rock
Authors: Chen Xinqing , Cheng Ruishan , Wang Haiquan 
Published: May 2024

Abstract
Rock masses in cold regions are exposed to a negative temperature environment for extended periods, causing significant changes in their mechanical properties. This inevitably results in substantial differences in the blasting characteristics compared to rocks at normal temperatures. To reveal the influence of different temperatures on the blasting characteristics of frozen rocks, this paper utilized a finite element method-based LS-DYNA software for numerical simulation of single borehole blasting at freezing temperatures of -5°C, -10°C, -20°C, and -30°C. The results showed that under negative temperature conditions, the degree of rock blasting-induced damage gradually decreased as the rock temperature decreased. As the temperature decreased from -5°C to -30°C, the volume of rock blasting-induced damage decreased by approximately 14%. Additionally, this paper has further investigated the effects of ignition locations, free surface, and borehole burdens on the blasting-induced damage characteristics of frozen rock. It was found that the volume of the blast cavity is the largest in reverse detonation. While forward detonation is more favorable for damaging the top rock and helps to improve the rock fragmentation at the orifice. Increasing the free surface can significantly improve the effect of blasting-induced damage. There is an optimal borehole burden distance, which can fully utilize the explosive energy and break the rock efficiently. Finally, based on multivariate nonlinear regression analysis, an empirical formula of the reduction coefficient of the specific charge for frozen rock mass is developed, which can provide a theoretical basis for designing and optimizing rock engineering blasting in alpine and cold regions.
Weak Solutions to an Initial-Boundary Value Problem of a Consistent Three-phase-field Model
Authors: Ma Li , Cong Yuhao , Zhu Peicheng , Yang Manman 
Published: May 2024

Abstract
Global warming has caused the melting of glaciers, especially the Greenland sea ice which is a great threat to human beings. The phase-field method is used to describe the evolution of multi-phase microstructure of Greenland sea ice. This paper shall investigate the global existence, uniqueness and regularity of weak solutions to an initial-boundary value problem for a three-phase-field model based on criteria that lead to both physical and mathematical consistency. For global solutions, it is calculated by applying the method of continuation of local solutions. This model allows for the formulation of spurious phases, which is completely consistent with physical reality.