bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improvement of deformable DETR model for insulator defect classification detection method
Authors: Liu Yanli , Jin Jiahe , Zhang Heng 
Published: October 2024

Abstract
In response to challenges faced by traditional detection methods such as image blurring, scarcity of insulator defect datasets, and insulators as small targets, we propose an improved Deformable DETR network based on the DETR defect detection algorithm. This network model accurately identifies the position information of insulators and segments the insulators on the insulator string. To address the classification problem after defect detection, we introduce a fused insulator defect classifier with a self-attention mechanism built behind the Deformable DETR model. The detected insulators are classified into three categories. Due to the limited dataset of damaged insulators, corresponding loss functions are set to address the issue of sample imbalance, thereby increasing the model’s focus on damaged insulators. Experimental results demonstrate an accuracy of 97.5% on the test set, highlighting the network’s strong generalization ability.
Monitoring of floating structures using segmentation and object classification of remotely-sensed images
Authors: Wan Jiahua , Mariano Vladimir Y. 
Published: October 2024

Abstract
With the rapid development of Marine aquaculture, remote sensing monitoring of high space separation Marine floating raft aquaculture has become a key technology to ensure aquaculture efficiency and environmental sustainability. A novel multi-scale layered cascade capsule network (MSHCN) model is proposed to improve the accuracy and efficiency of remote sensing image analysis for high space separation Marine floating raft culture. Firstly, based on the basic principle of the capsule network, the traditional convolutional neural network is improved, and a multi-scale feature extraction mechanism is introduced to adapt to the diversity and complexity of images in the culture area. Secondly, by building a hierarchical cascade structure, the model can further improve its ability to capture small and large scale features, so as to better understand the spatial distribution characteristics of the farming environment. In addition, in the experimental part, we verified with real remote sensing data sets, and the results showed that the MSHCN model outperformed the current mainstream methods on several key performance indicators, including classification accuracy, detection speed and model generalization ability. Finally, the potential value of this model in practical application and future optimization direction is discussed, which provides a new idea and tool for the development of remote sensing monitoring technology for high space separation floating raft culture.
Research on porosity prediction method for petroleum reservoirs based on improved gene expression algorithm
Authors: Han Xuefeng , Lin Lin , Xu Huan 
Published: October 2024

Abstract
Porosity, an essential parameter of reservoir properties, plays a crucial role in reservoir evaluation. Indirect prediction of reservoir porosity using well-logging data is a fundamental task in assessing hydrocarbon reserves. Multivariate stepwise regression and artificial neural network methods are currently typical prediction approaches. However, these methods suffer from drawbacks such as high workload, extensive training sample requirements, and high computational costs. Genetic Expression Programming (GEP), a novel genetic algorithm, exhibits remarkable function discovery capabilities, high efficiency, and the ability to function without requiring large training samples. This paper proposes a method that utilizes GEP to mine a porosity calculation model from well-logging sample data and subsequently compute the porosity. The effectiveness and efficiency of this method are validated through actual well-logging data.
Enhanced rolling bearing fault diagnosis using DBO-VMD-KELM
Authors: Xu Haobo , Yang Yang 
Published: October 2024

Abstract
To enhance the extraction of fault signal characteristics from rolling bearings and enhance fault classification precision, this paper presents an approach for diagnosing rolling bearing faults, integrating Variational Mode Decomposition (VMD) and Kernel Extreme Learning Machine (KELM). Furthermore, the Dung Beetle Optimizer (DBO) algorithm is employed to optimize VMD and KELM, addressing the challenge of determining critical parameters effectively. Initially, VMD is employed to break down the fault data from rolling bearings, and the pivotal parameters [k, α] of VMD are globally fine-tuned via the DBO algorithm, with the minimum envelope entropy serving as the optimization criterion. The ideal components of the decomposed intrinsic mode function (IMF) are then determined by comparing their minimal arranged entropy. Nine time-domain indicators are then computed to create the feature vector. Finally, in the KELM diagnostic model, the constructed feature vector is input, and DBO is combined with KELM for optimization to achieve the final fault diagnosis. Simulation results show that the fault diagnosis rate of KELM is 92%, and the fault diagnosis rate of KELM optimized by DBO is 99%, effectively identifying the fault categories of rolling bearings.
Design of voice command recognition chip based on heterogeneous acceleration
Authors: Liu Xinyu , Li Jin , Jiang Junbang , Li Tiehong 
Published: October 2024

Abstract
Speech, as one of the earliest forms of communication used by humans, can effectively convey information. However, the current deep neural network models for speech recognition are generally large in scale and can only be deployed in the cloud, which imposes high deployment environment requirements and power consumption, thereby limiting their implementation on embedded devices. In the context of end-to-end speech recognition, a series of challenges are encountered, including power consumption constraints, computing power limitations, network dependencies, privacy protection, bandwidth restrictions, and communication delays. To address these issues, this paper proposes the design of an end-to-end voice command recognition chip based on deep neural networks specifically for recognizing voice commands in specific scenarios. This chip achieves low power consumption and minimal delay in recognition. Additionally, we introduce a weighted, overloadable chip architecture to enable seamless scene migration, ultimately aiming to resolve the aforementioned challenges.
Lightweight bearing defect detection method based on collaborative attention and domain adaptive technology
Authors: Nie Wenzhong , Ju Zemin 
Published: October 2024

Abstract
With the rapid development of industrial manufacturing, bearing defect detection has become a key link to ensure the safe operation of equipment and improve production efficiency. Aiming at the problems of low accuracy and large parameters of traditional bearing defect detection models, this paper proposes a lightweight bearing defect detection method based on collaborative attention and domain adaptive technology. Firstly, the collaborative attention mechanism uses multiple parallel attention heads to extract the features of different regions in the image respectively. Each attention head can independently extract and weigh the features of specific regions, which can capture the complex features of bearing defects more comprehensively, including shape, texture, edge and other key information. At the same time, the domain adaptation technology aims to make the model adapt to the data distribution of different domains. It reduces the difference between the source domain and the object domain by aligning the feature distribution between the source domain and the object domain during the training process, thereby improving the performance of the model on the object domain. The experimental results show that the proposed method is 2.75% higher than the mAP@0.5 of YOLOv7, and has higher computational efficiency and better generalization ability than the traditional method.
Simulation of marine wireless sensor network coverage based on improved grey wolf optimization algorithm
Authors: Yang Zhongqiang , Palaoag Thelma Domingo 
Published: October 2024

Abstract
A new improved algorithm (IGWO) is proposed based on the Grey Wolf Optimization (GWO) algorithm to solve the issue of low overall coverage easily caused by the random deployment of nodes in wireless sensor networks. The IGWO algorithm improves its searchability by adjusting the convergence factor a and changing the updating rules of individual gray wolf positions. IGWO improves the overall search capabilities of the algorithm by converging and adjusting the trend of convergence factors and dynamically adjusts the gray wolf position update strategy by taking the Euclidean distance ratio values of the omega wolf to the alpha wolf, beta wolf, and delta wolf as parameters, further expanding the search capability of the algorithm. Using MATLAB for simulation, we select 20, 30, and 40 sensor nodes respectively, and the coverage of IGWO compared to GWO’s WSN increases by 0.09%, 2.09%, and 1.17%, respectively, proving that the IGWO algorithm can effectively improve the coverage and performance of WSN.
ETFE: Efficient transformer with feature enhancement for semantic segmentation
Authors: Liu Meilin , Li Tianping 
Published: October 2024

Abstract
Despite the powerful capabilities of Transformers in relation to modeling, their effectiveness in capturing multi-scale object features in image-based tasks is often limited. This limitation arises from the diverse shapes and distances between objects in natural scenes, which pose challenges for Transformers to accurately capture object sizes and distances. To overcome this problem, we propose an effective Transformer which extracts multi-scale features at different resolutions. First, we leverage a multi-head attention mechanism to extract richer semantic features at various resolutions. The semantic features are then passed through the Feature Gate to aggregate multi-scale semantic features. Finally, Attentional Feature Enhancement further strengthens the semantic information of target features, enabling the modeling of dependencies among multi-scale features. Comprehensive experiments conducted on the Cityscapes and ADE20K datasets have shown that our model outperforms competing methods in both performance and accuracy.
Identification of complex system radar emitter source based on one-dimensional convolutional neural network
Authors: Fu Yiyang , Guo Linqing , Yi Gaohao 
Published: October 2024

Abstract
With the continuous innovation of radar technology, the signal modulation of radar emitter sources has become more complex, which makes their identification face more challenges. We propose a method based on a one-dimensional convolutional neural network for the identification of complex system radar emitter sources and conduct an experiment on eight radar emitter sources with similar parameters and complex inter-pulse modulation. The identification accuracy reaches 88.24%.
LPBFuzz: Binary program fuzzing with enhanced coverage of low-frequency program branches
Authors: Shen Yuhui , Zhang Haoran , Shao Siyuan , Zhang Zhao 
Published: October 2024

Abstract
Fuzzing detects hidden defects and vulnerabilities in software by generating a large amount of malformed input data and monitoring program anomalies. The existing fuzzing methods suffer from data imbalance when modeling program branching behaviors, making it difficult to accurately locate data fields that are strongly correlated with low-frequency program branching decisions. In addition, the existing methods do not determine variation direction when mutating the test data and do not distinguish between valid/invalid data fields, resulting in duplicate testing of certain program branches. In this paper, we propose a fuzzing method to enhance the coverage of low-frequency program branches. A low-frequency perception network and a forward gradient-guided mutating strategy are proposed to clarify the magnitude and sign when mutating key data fields. Experiments show that the proposed method effectively improves code coverage by increasing low-frequency branch coverage.
Emergency control methods for power systems based on improved deep reinforcement learning
Authors: Zhang Jie , Zhu Yihua , Liang Zhuohang , Ma Qinfeng , Zhang Qingqing , Liu Mingshun , An Su , Pu Qingxin , Dai Jiang 
Published: October 2024

Abstract
In order to achieve fast and accurate transient stability analysis and emergency control, this paper proposes a transient stability emergency control method based on improved deep reinforcement learning. In order to fully explore the temporal and spatial variation trend of transient response, a multi-dimensional feature containing information such as transient situation energy is constructed, and the deep reinforcement learning model is transformed based on the time-space graph neural network. On this basis, an emergency control model is constructed, and the power grid knowledge is integrated into the emergency control decision-making scheme to reduce the exploration of invalid decision-making and improve the performance of the model. The effectiveness of the proposed method is verified in the IEEE-39 system.
Research on the influence of multi-agent deep deterministic policy gradient algorithm key parameters in typical scenarios
Authors: Lu Junsong , Wang Zongsheng , Pan Kang , Zhang Hanshuo 
Published: October 2024

Abstract
The MADDPG algorithm is widely used and relatively complete, but there is no intuitive data support for the values of some key parameters. Therefore, the influence law of key parameters on MADDPG in typical scenarios has been studied in this paper. Firstly, three typical experimental scenarios were identified, including collaborative cooperation, collaborative opposition, and collaborative pursuit with basic parameters and hyperparameters. Then, a research plan was formulated by using the control variable method to study the influence of learning rate, reward discount coefficient, and reward function coefficient on algorithm performance. Based on a large number of experimental data comparisons, the optimal values of each parameter under three experimental scenarios were obtained. The results showed that the optimal reward discount coefficient for all three scenarios was the same, indicating that its impact on scene complexity was relatively small. For the optimal learning rate, there was a general trend that the lower complexity collaborative cooperation scenario had an optimal value lower than that of the higher complexity collaborative pursuit and collaborative opposition scenarios. As for the reward coefficient, it could be concluded that when the reward coefficient was large in collaborative cooperation and collaborative opposition scenarios, the convergence and speed of the reward curve became poorer. The reward coefficient in the collaborative pursuit scenario had a less significant impact on the performance of the algorithm.
A low-dose CT image denoising method based on state space model
Authors: Peng Kuankuan , Gong Shihua 
Published: October 2024

Abstract
Low-dose CT medical imaging techniques introduce noise while reducing radiation risks, necessitating denoising processing. However, existing mainstream denoising methods face a difficult trade-off between preserving image detail information and accurately removing noise. To address this issue, we propose a low-dose CT image denoising method based on a state space model. Firstly, a dynamic edge information enhancement module is introduced to automatically extract edge information from images using a learnable LoG operator and fuse it into feature layers at different scales to suppress edge information loss caused by denoising processes. Secondly, a U-net encoder based on state space estimation is designed to dynamically model spatial relationships between pixels through neighborhood filtering, enabling consideration of local differences in pixel values during denoising and better preservation of edges and textures. Compared to existing denoising methods, our approach achieves stable noise removal in low-dose CT images while preserving the original texture structure.
Rule-based fine-grained vocabulary for enhanced Chinese pre-training
Authors: Zhang Yu , Gao Yang 
Published: October 2024

Abstract
Chinese pre-training is an essential direction in Chinese natural language processing, and vocabulary constitutes the foundation of pre-trained models. Existing methods for constructing vocabularies for Chinese pre-trained models typically treat each Chinese character as an indivisible token, overlooking the additional information embedded in the intrinsic structure of Chinese characters and its impact on model performance. To leverage this information, we propose a method of training with rule-based fine-grained vocabularies to directly learn the sequence of intrinsic structures of Chinese characters in Chinese pre-training. Specifically, we first construct a mapping rule-based fine-grained vocabulary based on the glyph and radical splitting mapping relationship of Chinese characters. Subsequently, we employ a whole char masking strategy to train Chinese pre-trained models based on this new vocabulary. Experimental results demonstrate that compared to the BERT baseline model, our model achieves promising performance on multiple downstream tasks, with fewer model parameters and stronger robustness. The proposed Chinese pre-training method exploits the intrinsic structural information of Chinese characters, providing a novel method for subsequent research in Chinese natural language processing.
Density clustering method based on k-nearest neighbor propagation
Authors: Hou Pengfei , Zhou Lichan , Yang Yuqing 
Published: October 2024

Abstract
Density clustering, an effective data analysis tool, performs well on arbitrary shapes and non-convex datasets. However, it still has some limitations in identifying the cluster structures of datasets with irregular shapes and uneven density distribution. Aiming at the above problem, this paper proposes a density clustering method based on k-nearest neighbor propagation. Firstly, based on the theory of k-nearest neighbor, the nearest hub points are defined to replace the sample points within their k-nearest neighbors, and the cluster boundary is ascertained relying on the propagation of the nearest hub points with density gradient trend so as to divide the nearest hub points into multiple groups; secondly, a group merging method of the nearest hub points based on the set similarity is given to obtain the set of nearest hub points; and finally, the remaining sample points are assigned to the set of their nearest hub points to obtain the final clusters. The correctness and effectiveness of the algorithm are verified by experiments on synthetic and UCI datasets, and the results show that the algorithm can better cope with the clustering needs of multiple complex data distributions compared with other comparative algorithms.
Research on the application technology of expert knowledge graph based on genetic search algorithm
Authors: Song Yukun , Yang Wei , Kong Weizheng 
Published: October 2024

Abstract
The present study investigates the application of a genetic search algorithm on an expert knowledge graph. By employing binary coding for candidate experts in the expert database, the matching evaluation function is utilized to define the environmental fitness index, thereby optimizing the selection of expert groups. Experimental results demonstrate that the GES algorithm exhibits a superior matching degree and reduced time consumption when addressing science and technology item-matching requirements in review work. In comparison with the traditional BFS algorithm and GIS algorithm, the GES algorithm showcases enhanced potential for application and stability when confronted with large datasets. This paper further analyzes the impact of factors such as the total number of experts, number of fields, and number of scientific and technological projects on algorithm performance, confirming that adjustment of algorithm parameters can effectively enhance matching degree while reducing time consumption, thus providing technical support for practical implementation.
Assessment of the impact of ethnic minorities’ capacity in Binh Dinh province, Vietnam, to access land and exercise land use rights by application of linear structure model (PLS-SEM)
Authors: Hang P T , Hai N T , LThuy P T , Huyen N T , Ngu N H 
Published: October 2024

Abstract
The study aimed to evaluate the impact of ethnic minority people’s capacity on land access and exercise of land use rights. The research model and hypotheses were tested using exploratory factor analysis (EFA) and PLS-SEM methods with 70 preliminary quantitative survey samples and 273 official quantitative survey samples of ethnic minorities. The research results revealed that strengthening the capacity of ethnic minorities is the key to improving their effectiveness in accessing land and exercising land use rights. The research findings demonstrate a robust and affirmative correlation between the capacity of ethnic minorities to acquire land access and the implementation of land use rights (βCO→LA = 0.325; βCO→LUR = 0.736; estimated path coefficients close to +1 represent strong positive relationships). The research model attains a significance level of 1% (tCO→LA = 8.412; tCO→LUR = 42.643). The outcomes of this research can serve as a foundation for the People’s Committee of Binh Dinh Province, Committee for Ethnic Minority, to further establish policies, support programs, and training to enhance the capacity of ethnic minorities, and to provide guidance on improving the effectiveness of land access and exercise of land use rights for this community.
The Environmental Perception and Vegetarian Motivation of People in Ho Chi Minh City, Vietnam
Authors: Nho T T N 
Published: October 2024

Abstract
Studies have previously shown that there were many vegetarian movements in the world, using many different models to indicate vegetarian food choice behavior. Among the factors affecting the motivation to become a vegetarian, perception and attitude are considered important factors influencing the decision to switch from using foods of animal origin to plant-based ones. There are many measured aspects of perception ranging from health, religion, love of animals, or animal rights. Among these, environmental perception increasingly shows a clear influence on vegetarian motivation, especially for young people. This current study aims to find out whether factors relating to perception including environmental perception, influence people’s motivation to become vegetarian in Ho Chi Minh City. To achieve this goal, the research surveyed 360 people living in Ho Chi Minh City (divided into three groups (students, office workers, and manual workers) using a direct questionnaire. Research results show that among the four independent variables (perception, ability, opportunity, barriers), there are three variables (1) perception; (2) opportunity; and (3) barriers having a significant relationship with the vegetarian motivation of people in Ho Chi Minh City. From the findings, this study points out key highlights in people’s current vegetarian food choices in this area, including environmental perception.