bluuebunny/crossref_metadata_2025_split huggingface dataset:
DAT-Net: Filling of missing temperature values of meteorological stations by data augmentation attention neural network
Authors: Guo Xinshuai , Hou Tianrui , Wu Li 
Published: August 2024

Abstract
For a long time, filling in the missing temperature data from meteorological stations has been crucial for researchers in analyzing climate variation cases. In previous studies, people have attempted to solve this problem by using interpolation and deep learning methods. Through extensive case studies, it is observed that the data utilization rate of convolutional neural networks based on PConv is low at a high missing rate, which will result in the poor filling performance of each model at a high missing rate. To solve these problems, a Data Augmentation Attention Neural Network (DAT-Net) is presented. DAT Net uses encoder and decoder structures, which include a data augmentation training mechanism (DAM) to enhance model training. In addition, a time encoder (TED) has been developed to assist the model in learning the temporal dependencies of the data. To evaluate DAT-Net, 75% and 85% of experiments were performed, while comparisons were made with Linear, NLinear, DLinear, PatchTST, and GSTA-Net. The results showed that when the missing rate was 75%, DAT-Net decreased by 55.22%, 55.05%, 55.18%, 28.73%, and 12.35% on MAE and 54.08%, 53.88%, 54.08%, 35.48% and, 14.51% on RMSE,
R
2
increased by 3.80%, 3,75%, 3.68%, 0.55%, and 0.27%, respectively.
Path planning and collision avoidance approach for a multi-agent system in grid environments
Authors: Tian Delei , Zheng Bin 
Published: August 2024

Abstract
In multi-robot systems, ensuring efficient, safe, and conflict-free navigation for each robot is crucial, especially in scenarios requiring the coordination of complex tasks and interactions with the environment. Currently, most related research focuses on simplified grid environments, making it difficult to apply these findings to real-world scenarios. This paper introduces actual physical constraints into traditional grid environment path planning and designs a set of motion rules suitable for grid environments and a collision avoidance algorithm. We also propose a novel multi-agent path planning approach that integrates deep reinforcement learning with collision avoidance algorithms. Through a series of experiments, we have demonstrated that this method effectively guides agents in navigating from their starting points to their targets within grid environments that possess physical constraints, and significantly reduces the probability of collisions when used in conjunction with our collision avoidance algorithm. These findings not only showcase the efficacy of our approach but also open new avenues for deploying multi-agent systems in more complex real-world scenarios.
Power positioning control system for rake suction dredger
Authors: Zhao Ganyu , Yu Menghong 
Published: August 2024

Abstract
Rake suction dredgers are specialized vessels used for dredging projects in water bodies. Power positioning refers to the precise control of the vessel’s position in the water using power systems to complete operational tasks or maintain specific positions. This paper proposes a PID control algorithm based on reinforcement learning to address the accuracy and stability issues of power positioning in rake suction dredgers. The algorithm utilizes a deep Q-network as the core of reinforcement learning, combined with PID control principles, to enable the intelligent agent to control the vessel in real time. In algorithm design, the problem of overestimation is effectively addressed through a dual neural network structure, and an experience replay mechanism is introduced to enhance training efficiency and stability. In the reinforcement learning process, a reward function suitable for the characteristics of rake suction dredgers is designed, considering the balance between path-tracking accuracy and vessel stability. Experimental results demonstrate that the proposed algorithm can achieve power positioning control of rake suction dredgers under different sea conditions, with high path tracking accuracy and stability, providing an effective solution for the autonomous navigation and operation of rake suction dredgers.
Global and local complementary multi-path feature fusion network for the classification of crop remote sensing images
Authors: Zhang Keqian , Cui Tengfei , Wu Wei , Zhang Yichi , Zhang Ziqian , Si Qiliang , Song Xiangyang , Cheng Gang 
Published: August 2024

Abstract
The accuracy and efficiency of crop distribution information extraction are pivotal in ensuring global food security. In long-time-series optical satellite data, most existing methods focus on extracting spatial features using Convolutional Neural Networks (CNNs), which do not adequately mine and model the spatial-temporal information. The development of the attention mechanism allows for the extraction of global features in remote-sensing images of long temporal sequences. To extract global attentional features with complementary features in crop remote sensing images, we propose a Global and Local Complementary Multi-path Feature Fusion Network (GLMP), which is capable of extracting global features from remote sensing images of long temporal sequence, that enhances the local characteristics of crop images derived from CNNs, thus obtaining more effective multi-scale complementary features. This extraction of features enhances the comprehension of crop images, thereby boosting the performance of associated tasks. Within GLMP, we introduce two pivotal modules: the Hybrid Attention and Convolutional Paths Module (HACM) and the Multi-path Feature Fusion Module (MPFM). These modules synergistically converge multi-path features, yielding more discriminative feature information. Experimental results on the ZueriCrop dataset show that the proposed GLMP technique is effective; it performs promisingly having a total accuracy of 90.2% and an F1 value of 62.5%. Furthermore, the ablation study verifies the substantial improvement in classification accuracy for remote sensing crop images of long-time series in nature, specifically attributed to the HACM and MPFM modules.
Traffic light detection algorithm based on improved YOLOXs
Authors: Nie Zhiling , Jiang Shaochen , Wang Liejun 
Published: August 2024

Abstract
The detection and recognition of traffic lights play a vital role in driverless and assisted driving technology, given the rapid advancements in autonomous driving. Vehicles can receive crucial information about traffic signals by precisely locating and identifying traffic lights to ensure safety. To make the traffic light detection model more lightweight and improve the detection speed, this paper proposes an improved YOLOXs traffic light detection algorithm. Firstly, the Ghost module is used to enhance the backbone model of YOLOXs in lightweight, reduce the parameters quantity of the network, and enhance the detection speed. Then, the CBAM module is added to the YOLOX neck model to direct the model’s focus toward the target area and ensure the accuracy of traffic light detection. Many experiments on the S2TLD public datasets display that the enhanced model has fewer parameters and achieves accelerated detection speed, up to 112 FPS, which can achieve real-time detection effects. At the same time, in contrast to the primary network, the accuracy of the enhanced network is improved by 1%, reaching 97%. The recall rate increased by 1.2% to 96.2%; average accuracy increased by 0.3% to 97.1%.
3DMMF: 3D object detection network based on multi-layer and multi-modal fusion
Authors: Zhou Jia , Xu Limei , Ma Wenhao , Zhou Zhiguo , Zhou Xuehua , Shi Yonggang 
Published: August 2024

Abstract
Cameras and LiDAR are important sensors in autonomous driving systems that can provide complementary information to each other. However, most of the LiDAR-only methods outperform the fusion method on the main benchmark datasets. Current studies attribute the reasons to the misalignment of views and difficulty in matching heterogeneous features. In particular, the single-stage fusion method makes it difficult to fully fuse the features of the image and point cloud. In this work, we propose 3DMMF: a 3D object detection network based on multi-layer and multi-modal fusion methods. 3DMMF works by painting and encoding point clouds in the frustum proposed by the 2D object detection network. Then the painted point cloud is fed to the LiDAR-only object detection network which has expanded channels and a self-attention mechanism module. Finally, CLOCs are used to match the geometric direction features and category semantic features of 2D and 3D detection results. Experiments on KITTI datasets show that this fusion method has a significant improvement over the baseline of the LiDAR-only method, with an average mAP improvement of 6.3%.
Multi-source data fusion for power outage warning based on transformer model
Authors: He Jindong , Lei Shanshan , Wu Lijin 
Published: August 2024

Abstract
In this study, we propose a method that uses the Transformer model to enhance power outage alerts by integrating data from multiple sources. By integrating data from various sources, including operational data from the local distribution network and meteorological data, we have constructed a comprehensive multi-source data framework for power outage warnings. The Transformer model, known for its ability to capture complex dependencies and patterns, has been employed to extract features and make accurate predictions. Results on actual power system data have shown that our approach significantly boosts the accuracy and stability of predictions. The fusion of multi-source data has enabled timely maintenance and protection measures, reducing the duration and impact of power outages. The findings from this study have provided valuable insights for power outage warnings and future research on multi-source data fusion.
An improved container-based deep forest model for predicting groundwater recharge
Authors: Liu Bao , Sun Yaohua , Gao Lei 
Published: August 2024

Abstract
This paper proposes ICDF, an improved container-based deep forest model, for effectively modeling and predicting groundwater recharge. The model consists of four points: the construction and expansion of the container module, the assignment of weights to the base model, the growth of the cascade layer, and the decision output. First, container modules are created and a maximization objective function is assigned to each container to control its growth. Next, different weights are assigned to each base model based on its contribution to container prediction. Cascade layers are built using container modules until the model prediction decreases. Finally, the average prediction vectors of the last cascade layer are output. The model’s performance is evaluated and compared with DF and its base models (random forest, adaptive boosting, and extreme gradient boosting) using a case study of 1549 bores in New South Wales, Australia. Remarkably, compared to DF, ICDF has improved prediction accuracy by 6.66%. Moreover, it outperformed the RF, AdaBoost, and XGBoost by 2.94%, 5.85%, and 5.3% in prediction performance, respectively. The ICDF exhibited superior capabilities in predicting groundwater recharge, offering significant improvement over existing models. Practitioners are encouraged to consider adopting ICDF for groundwater management.
Smith GA-PID feedback control of hot strip final rolling temperature
Authors: Wang Saisai , Tian Hai , Tian Jiaming , Wu Zijing , Li Chao , Tao Zhen 
Published: August 2024

Abstract
At present, the hot strip finishing temperature control system generally adopts a computer model or mechanism model to predict the strip’s final rolling temperature, which is open-loop control. To realize the precise control of the final rolling temperature of high-performance steel, this paper introduces the PID feedback control strategy at the end of the finishing zone, to realize the high-precision temperature control of the strip steel. The Smith compensator is used to predict the compensation for the large hysteresis problem of the final rolling temperature, and various intelligent algorithms are used to optimize the three parameters of the PID controller, eliminate the nonlinear problem, and obtain better dynamic performance. After the experiment, it is concluded that the Smith GA-PID optimization control strategy system has a small overshoot, the peak time and regulation time are the shortest, and the dynamic response is the fastest. Compared with the traditional PID control method, the overshoot is reduced by 97.6%, and the required regulation time is reduced by 7.85 s.
Fault detection of industrial processes using attention-based gated recurrent unit autoencoder with skip connection
Authors: Wang Yanan , Jin Qibing , Li Dazi , Wang Bin , Zhang Yang , Zhu Qian 
Published: August 2024

Abstract
With the continuous evolution of modern industrial technology, industrial production has grown progressively complex, necessitating the use of various sensors to measure multiple process variables. However, intricate temporal dependencies and nonlinear relationships between data presented by multivariate sequences pose significant challenges to process fault detection. In response to these challenges, this paper proposes an attention-based gated recurrent unit autoencoder with skip connection (SAGRU-AE) model for monitoring large-scale, nonlinear, and multivariate industrial process faults. SAGRU-AE combines gated recurrent units, multi-head self-attention, and autoencoder to extract features from multivariable time series data efficiently. Concurrently, feature reuse is achieved through the skip connection structure, which improves the accuracy of data reconstruction. Based on the implementation of process data feature extraction and input reconstruction in SAGRU-AE, two statistics have been developed, namely the H2 statistic and square prediction error (SPE) statistic, for fault detection tasks. Ultimately, the feasibility and effectiveness of the proposed algorithms are validated through experimentation on the TE process.
Machine anomalous sound detection based on audio synthesis generative adversarial network
Authors: Cheng Tianyao , Guo Fabin 
Published: August 2024

Abstract
In the pursuit of implementing safety monitoring within Industry 4.0 smart production, deep learning methodologies are employed to harness the audio signals emanating from the operation of machinery and equipment, facilitating anomaly detection. Aiming to address the challenge of the few-shot samples for anomalous sound detection in an unsupervised setting, this paper introduces a machine anomalous sound detection method using an Audio Synthesis Adversarial Generative Network (ASGAN). The augmentation of classifiers within the network’s discriminator is proposed to execute a multi-classification task, thereby transforming the unsupervised training milieu into a semi-supervised one and enhancing the discriminator’s accuracy. To address the oversight of audio frequency domain features by conventional generative adversarial networks, the squeeze-and-excitation network is applied to concentrate on crucial frequency bands within the frequency domain. Experimental validation is conducted by using the DCASE Challenge Task2 dataset, revealing that the performance of the anomaly detection method leveraging audio synthesis generative adversarial networks surpasses that of the baseline system.
Improvement of defect detection for steel based on YOLOv7 algorithm
Authors: Zhu Guiwei , Zheng Guanghai 
Published: August 2024

Abstract
Working on the conundrum of low efficiency of strip surface defect detection, a surface defect detection algorithm for boards and strips leveraging YOLOV7 is introduced. First, the global attention mechanism is set up to enhance global information interaction and expression capabilities, and improve detection performance; in second place, the C2f component is integrated into the feature pyramid network to the lightweight pattern; ultimately, Focal-EIoU is exerted to replace loss function of the YOLOv7 pattern to solve the divination box and the problem of wrong amplification of length and width, improving the accuracy of defect classification and positioning. The outcomes reveal that the precision of the elevated algorithm has been boosted by about 4.3%, the recall rate has reached 78.3%, and mAP increased by 3.4%. This technique exhibits high accuracy and efficiency in detecting surface defects on the tape.
Research on PCB tiny displacement estimation method for ORB feature-guided FREAK descriptor enhancement
Authors: Yang Duo , Su Yang , Wu Hongwei , Yi Pengfei 
Published: August 2024

Abstract
Given the hurdles posed by existing techniques for identifying microscopic offsets in miniature printed circuit boards (PCBs), including their low accuracy and high iteration count, this study presents an innovative approach that measures micrometer offsets by utilizing the ORB feature-guided FREAK descriptor enhancement. It improves matching outcomes with a brute-force matcher and RANSAC via ORB feature extraction to guide FREAK descriptors for better feature uniqueness. A PCB small offset detection algorithm is developed with a homography matrix and a theoretical offset model. A novel distance threshold performance evaluation method for this suggested matching algorithm that objectively evaluates matching results is also proposed in this article. The proposed matching method outperforms alternative algorithms in precision and recall, according to its experiments. This research conducts simulation detection of PCB calibration bare boards and instance verification of real PCB boards by constructing a PCB tiny offset detection experimental system. The results show that micron-level detection precision can be achieved without iteration.
A data augmentation method for multiaxial fatigue life prediction based on physics-informed tabular generative adversarial network
Authors: He Gaoyuan , Zhao Yongxiang , Yan Chuliang 
Published: August 2024

Abstract
Currently, in the multiaxial fatigue life prediction problem, machine learning (ML) algorithms are still limited by small samples. For ML tasks requiring large amounts of data, this paper proposes a Conditional Tabular Generative Adversarial Network (CTGAN) model using a physical equation as a generation constraint. The proposed method can make synthetic samples satisfy specific physical knowledge. The method is evaluated using two datasets of different materials. ML-based algorithms verify the feasibility of using synthetic datasets for life prediction. Verification analysis shows that the proposed method successfully synthesizes high-quality multiaxial fatigue datasets and effectively improves the prediction accuracy of ML algorithms.
Identification of emitting times based on improved CRNN model
Authors: Liang Cuiping , Li Yang , Wei Yuyun , Zhang Dan , Wang Ying , Jiang Suiping 
Published: August 2024

Abstract
The number of times that equipment can emit is limited, and exceeding the usage limit will affect its shooting accuracy. Existing research mainly focuses on the classification and detection of emitting times, with few studies focusing on the recognition of emitting times. This paper combines multi-scale feature extraction with the CRNN model and proposes an improved CRNN emission frequency recognition model. After experimental verification, the improved model has a high prediction accuracy on high-quality emitted sound data, with an accuracy of over 95.2%. The accuracy of the data in the presence of strong interference is 88.6%. The experimental results show that the method proposed in this paper is effective.
Time-optimal trajectory planning for manipulator based on an improved sparrow search algorithm
Authors: Xu Hanwen , Gao Wengen , Zhu Jiaming , Zhang Guoqing 
Published: August 2024

Abstract
With the development of industrial automation, it is crucial to use manipulators for efficient painting. To solve the problem of inefficient trajectory planning of manipulators in the point-to-point painting process, this paper proposes a time-optimal trajectory planning method based on an improved sparrow search algorithm. Initially, the standard D-H parameter method is used to establish the kinematic model of the manipulator. To ensure the continuity of each joint in the manipulator, a 3-5-3 polynomial interpolation function is utilized to construct the motion trajectory in joint space. Subsequently, the improved sparrow search algorithm is utilized to optimize the point-to-point motion trajectory of the manipulator with a constraint on motion time. Finally, simulations carried out in MATLAB demonstrated that the manipulator, which was optimized by using the improved sparrow search algorithm, exhibited a significant increase in motion efficiency compared to its pre-optimization state. Additionally, the movements of the manipulator’s joints were smooth.
Research on dynamic bottleneck prediction methods in manufacturing workshops
Authors: Xia Maoyu , Lin Shuo , Han Zhonghua 
Published: August 2024

Abstract
To address the challenge of identifying bottleneck responsibilities when multiple bottlenecks arise in process workshops and predicting bottleneck trends before their occurrence, a novel dynamic prediction method based on the Markov chain is introduced. This approach effectively identifies all bottlenecks in a multi-bottleneck process shop and identifies the primary bottleneck unit with the highest level of responsibility, enabling anticipation of bottleneck shifts prior to their occurrence. Initially, the concept of bottleneck is elaborated, accounting for the intricate interplay among manufacturing processes, leading to the development of a static mathematical model for multi-bottleneck prediction incorporating parameters such as time, cost, and quality. This model evaluates the bottleneck severity of each manufacturing unit, facilitating the identification of all bottleneck units and the assignment of bottleneck responsibilities, thereby establishing primary and secondary bottlenecks. Subsequently, a bottleneck state transition matrix is formed based on historical bottleneck processes, initializing the state matrix through the process with the highest bottleneck responsibility in the static setting. Leveraging the Markov chain prediction technique, the method anticipates the location of bottleneck transitions preceding the occurrence of bottleneck drift. Finally, simulations were conducted in an authentic production line environment to validate the method’s efficacy. Results indicate a 9.1% increase in the main bottleneck coincidence rate compared to single-bottleneck prediction models. Moreover, the Markov chain prediction accurately forecasts bottleneck trends prior to drift occurrences, boasting an 86.4% accuracy rate. These findings underscore the applicability and guidance potential of the proposed method.