bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-task recognition of modulation types and arrival directions of underwater acoustic signals based on convolutional neural networks
Authors: Xu Yangyi , Zhang Youwen 
Published: August 2024

Abstract
Nowadays, underwater acoustic communication reconnaissance technology is of great importance in military, marine science, and resource exploration fields. The fields involve military reconnaissance and intelligence collection, ocean monitoring and security, marine scientific research, and resource exploration. This article mainly focuses on the identification of modulation types and emission angles contained in underwater acoustic signals. Convolutional Neural Networks (CNNs) are used for deep learning to train, validate, and obtain final results on signal data. This network achieves the independent tasks of automatic modulation classification and Direction of Arrival (DOA) estimation of radio signals simultaneously. The model achieves efficient identification and filtering by designing different filter sizes and sizes, as well as different Y-shaped connection structures. It includes one input block and two output blocks, and the two output blocks correspond to the outputs of modulation classification and DOA estimation. At the same time, based on this, the parameters of the network layer are modified to achieve the generalization ability of the neural network. The training and validation data of the network consists of simulation data from underwater acoustic communication emissions and real data from the Songhua Lake experiment in Jilin Province, China, to test the network recognition efficiency.
Online trajectory anomaly detection model based on graph neural networks and variational autoencoder
Authors: Lu Fangbo , Zhang Zhihao , Shui Changsheng 
Published: August 2024

Abstract
To efficiently determine whether an entire trajectory exhibits abnormal behavior, we introduce an online trajectory anomaly detection model known as GeoGNFTOD, which employs graph neural networks for road segment representation, creating a directed graph by mapping trajectories onto the road network. The graph representation is constructed based on the road segments in this directed graph. By utilizing Transformer sequence encoding, the trajectory representation is derived and hierarchical geographic encoding captures the GPS mapping of the original trajectories. Merging these two representations produces the final trajectory representation, serving as input for an LSTM-based variational autoencoder to reconstruct the trajectory representation, facilitating rapid online anomaly detection. Experimental findings on a large-scale taxi trajectory dataset illustrate the superior performance of GeoGNFTOD compared to baseline algorithms.
Research on electromagnetic pulse signal detection method based on intelligent compressed sensing technology
Authors: Wang Yongsheng , Peng Mingbin , Li Hongjie , Dang Yanyang , Zhang Zhengyang , Tan Xiangyu , He Wenzhi , Lyu Fangxing 
Published: August 2024

Abstract
Electromagnetic pulse signal measurement is crucial for national security, scientific research, technological advancement, and emergency preparedness, playing a vital role in various aspects of modern society. In this work, an electromagnetic pulse signal detection approach utilizing compressed sensing technology is suggested. By unitizing three parallel low-frequency analog-to-digital converters, the high-frequency acquisition waveform of the measured electromagnetic pulse signal can be recovered. The compressed sensing effect of the proposed system on electromagnetic pulse signal is tested by simulation, which verifies the feasibility of this method.
Research on multi-data fusion fire detection based on GWO-LSTM regression prediction model
Authors: Li Haoyang , Liu Jianxiang , Cao Zhenkun , Liu Yujun , Ni Ruju 
Published: August 2024

Abstract
Aiming at the current situation of unsatisfactory fire prediction index and accuracy, this study obtains the optimal network hyperparameter values for LSTM by introducing the GWO algorithm, obtaining the optimal solution of model parameters through fitness calculation, compensating for the drawback of traditional LSTM easily converging to local optimal solutions, accelerating the convergence speed of LSTM neural networks, and reducing training time. The MATLAB simulation experiments show that the accuracy of the improved LSTM model is 92.54%, 97.04%, and 88.17% respectively, with prediction accuracy improvements of 10.6%, 4.33%, and 22.3%. This significant improvement demonstrates the effectiveness of the improvement method and provides a reference for the development of fire detection technology.
Research and practice of virtualization PLC (vPLC) technology in 5G industrial network
Authors: Sun Ruiqi , Zhang Yan , Liu Liu , Liu Jiawei , Sun Shihai 
Published: August 2024

Abstract
Under the influence of information technology innovation, the empowerment of 5G and industrial internet in the steel industry has gradually expanded from production assistance to industrial control. In this paper, a deep integration network of 5G and industrial control is constructed, and a virtualization PLC(vPLC) system solution deployed on private cloud servers is proposed. Secondly, IoT protocols and security policies for cloud control are designed. Finally, the proposed scheme was successfully applied to the remote control of equipment in the steelmaking production process of a certain steel enterprise. At the same time, the real-time performance, reliability and stability of the 5G-enabled vPLC system were tested. The result indicated that the 5G industrial network can work stably with a delay of less than 10 ms and 99.99% reliability. The vPLC system ran continuously for 2000 hours and maintained stable control functions. This result can provide a research foundation and technical direction for achieving centralized control and one-stop production management in the future.
End-to-end elevator voice command recognition system based on improved convolutional neural network
Authors: Liu Xinyu , Li Jin , Jiang Junbang , Li Tiehong 
Published: August 2024

Abstract
As a common life scenario, the use of voice control in elevator operation can effectively reduce public health risks and improve user experience in the post-epidemic era. However, due to the characteristics of its closure and public space, it is difficult to deploy the elevator. To solve this problem, an end-to-end speech command recognition algorithm based on a new deep neural network is proposed. The algorithm uses the self-built corpus for training, and uses the speech segment interception algorithm to obtain audio segments under the premise of speech stream as input, puts them into the network model in real time for reasoning, and drives the elevator to run according to the output results. The results show that compared with the recognition model using MFCC-CNN, the network has achieved about 10% improvement in accuracy under the premise of smaller computation, and compared with the DTW algorithm, the network has improved the accuracy by about 25%. Finally, a typical deployment environment is constructed to prove the correctness and effectiveness of the method in practical application.
Flow prediction of irrigation well pump based on experimental and big data analytics
Authors: Jiang Rui , Xian Haizhen , Li Fengchong , Su Wenqiang , Wang Ningling , Zhang Yuning 
Published: August 2024

Abstract
Currently, China’s agricultural irrigation consumes a huge amount of water, and traditional agricultural irrigation methods lead to low irrigation efficiency and serious water resource waste. Agricultural irrigation water management is also difficult to achieve refined management due to the lack of accurate monitoring of water use data and information in various irrigation areas. To find a more convenient flow measurement method, this paper proposes using data-driven monitoring of water pump characteristic parameters to predict flow rate. Three big data-based methods for predicting the flow rate of irrigation well pumps were compared, including RBF neural network, Support Vector Machine (SVM), and Extreme Learning Machine (ELM). The results indicate that the Extreme Learning Machine (ELM) model not only has short time consumption but also high prediction accuracy, laying the foundation for the application of big data technology in water management and high-quality irrigation water management that can save water.
Research on grid-related performance laboratory testing for variable speed pumped storage unit
Authors: Yan Yian , Li Shanying , Xie Huan , Wu Tao , Cao Tianzhi , Liang Hao , Liang Beihua , Lu Wenqing 
Published: August 2024

Abstract
Variable speed pumped storage unit is an energy storage device with excellent operation and regulation performance. For the grid-related performance laboratory testing of the AC excitation controller of variable speed pumped storage unit integrated into the power grid, this paper studied the methods of grid-related testing of the AC excitation controller based on actual pumped storage power station data, established semi-physical hardware in the loop simulation platform based on RTDS and its function boards, and proposed the key laboratory testing items for the grid-related test. The simulation testing results verified the functions of the platform.
Electro-hydraulic servo position control of bending machine based on Single Neuron PID
Authors: Miao Linchen , Shu Zhibing 
Published: August 2024

Abstract
Due to the fixed parameters, inadequate dynamic performance, and significant tracking error of the traditional PID control algorithm, it is challenging to satisfy the increasingly stringent precision demands for position control of the slider in the hydraulic servo system of the bending machine. Given the significant impact of external load on the position accuracy of the bending machine’s slider, a single neuron network is employed to dynamically adjust parameters in response to external load interference, thereby compensating for the limitations of traditional algorithms. Consequently, this paper proposes the design of a single neuron PID (SN-PID) controller to enhance system control accuracy. Through multiple simulation comparisons, it is evident that the SN-PID controller system exhibits superior anti-interference capabilities, enhanced adaptability, and reduced following error compared to traditional PID control algorithms in achieving slider position accuracy.
The YOLOv8m road object detection algorithm based on a lightweight design
Authors: Gao Deyong , Chen Taida , Miao Lan 
Published: August 2024

Abstract
To address the issues of low detection accuracy, slow detection speed, and high computational resource consumption in current road object detection algorithms, a lightweight YOLOv8m road object detection algorithm is proposed. Firstly, a lightweight convolution, GSConv, is introduced, and a C2fGSConv module is specifically designed to substitute the Conv and C2f modules within the Neck layer. This substitution effectively decreases the complexity of the algorithm during the feature fusion stage, thereby enhancing the network’s inference speed. Secondly, a lightweight convolution partial convolution (PConv) is introduced to redesign the detection head of YOLOv8, further reducing the model’s parameter size. Lastly, the efficient attention mechanism is integrated into the Backbone layer to enhance the algorithm’s detection accuracy for small road objects. Experimental results on the BDD100K road object detection dataset show that the improved algorithm achieves a 0.5% increase in detection accuracy, a 31.39% reduction in model computation, a 27.4% improvement in detection speed, and outperforms other comparison algorithms in various evaluation metrics, meeting the requirements of road object detection tasks.
Fault diagnosis method for continuous casting machine’s sector segment based on SG-PCA-LSTM
Authors: Pang Aokang , Rong Zhijun , Li Xuelin , Wang Yiheng 
Published: August 2024

Abstract
To address challenges such as the high variability of variables and difficulties in feature extraction during the casting process of the continuous casting machine’s sector segment, a fault diagnosis method based on SG-PCA-LSTM is proposed. This method aims to overcome the issue of fault features obscured by noise in the total tension time series by employing the SG smoothing algorithm for filtering and denoising. By leveraging the inter-segment data correlation and the advantage of PCA in extracting fault feature information, combined with the powerful learning capability of LSTM in modeling, a Principal Component Analysis - Long Short-Term Memory (PCA-LSTM) fault diagnosis model is established. Through comparative analysis against different diagnostic methods in terms of recognition rate, false positive rate, etc., the results obtained by this method are compared against those obtained by using other algorithms. Experimental results demonstrate that the proposed method exhibits good overall performance in terms of accuracy and training time.
Vector control of permanent magnet synchronous motor based on self-disturbance rejection and PI parallel connection
Authors: Shen Wei , Ran Quan , Zhao Shiping 
Published: August 2024

Abstract
Addressing the issue of susceptibility to disturbances in PMSM systems, a vector control system for permanent magnet synchronous motors with a self-disturbance rejection controller and PI parallel connection was designed. Firstly, the system’s speed error, electromagnetic torque, and load error are used as inputs for the self-disturbance rejection controller and PI control, respectively. Then, the self-disturbance rejection controller is connected in parallel with the PI and designed as a speed loop for PMSM to improve system stability. Finally, we use the improved sparrow algorithm to optimize the parameters of ADRC and enhance the overall performance of the system. The simulation results in Simulink indicate that parallel systems have fewer overshoots, higher stability, and better overall performance compared to ADRC and PI control.
Rock lithology classification algorithm based on improved self-attention mechanism VIT
Authors: Liu Hongqi , Shi Shaolong , Ma Tongle 
Published: August 2024

Abstract
When applying VIT to image classification, although good accuracy has been achieved, there is a limitation on the number of stacked layers. The similarity between different tokens increases as the model deepens, leading to low accuracy in shallow VIT models and wasting computational resources in deep VIT models. To address this issue, a multi-valued self-attention mechanism is proposed. By introducing an additional clue “V,” that does not vary arbitrarily, the dot product similarity observes each position in the token sequence more, reducing the depth of the attention mechanism network and avoiding the problem of similarity disappearing between deep tokens. Additionally, the loss function is improved by combining the Focal Loss function with Label Smoothing, enhancing the model’s handling of uncertain images while retaining the category focus of Focal Loss. Experiments are conducted on a rock lithology classification dataset, showing that compared to the VGG model and ResNet-18, the accuracy of the ResNet-50 model improves by 17.6%, 12%, and 4.9%, respectively. This paper also demonstrates the effectiveness of the multi-valued self-attention mechanism, which significantly reduces depth and parameter count while maintaining comparable accuracy. Finally, generalization experiments on the CIFAR-10 dataset further validate these conclusions.
Performance prediction method of solid rocket motor based on the ground test
Authors: Hu Xiaomei , Hua Yuting , Weng Jiahong , Teng Xianshu , Zhang Mingnan 
Published: August 2024

Abstract
Solid rocket motor (SRM) is a core component of rockets. Considering the high cost of solid rocket motors and the high demand for transportation and storage, there is an urgent need to predict the performance of solid rocket motors before the ground ignition test to lower the cost of tests. Using data mining technology to predict various parameters of rocket motors with historical data has become a new development direction. In order to improve the data fitting effect and prediction accuracy, a novel prediction model was proposed, which tries to combine the Whale Optimization Algorithm (WOA) with the Error Correction Gated Recurrent Unit neural network (ECGRU) to correct the basic prediction performance of the Long Short-Term Memory neural network model (LSTM). Compared with traditional methods, experiments showed that the novel model was beneficial in improving the reliability of the prediction of the thrust and specific impulse.
Research on visual servo strategy for the uncalibrated non-standard manipulator
Authors: Wang Wei 
Published: August 2024

Abstract
Aiming at the problem that the hand-eye calibration of the uncalibrated non-standard manipulator cannot be addressed by the general calibration method, i.e., the eye-in-hand and eye-to-hand calibration method of the visual servo system was proposed to guide the manipulator without calibration. First, a camera was installed on the manipulator to complete the eye-in-hand calibration and achieve transformation from the end coordinate system to the camera coordinate system. Second, a second camera was used to achieve eye-to-hand calibration and obtain the transformation relationship from the end coordinate system to the calibration board. The entire calibration process only requires the collection of images from two scenarios, i.e., the docking and the completion of the docking processes. The implementation results show that the rotation error between groups is less than 1.5°, and the translation error between groups is less than 8 mm. Therefore, the proposed visual servo strategy can accurately guide the uncalibrated non-standard manipulator.
Employment of conditional random fields for monocular depth estimation
Authors: Zhang Xudong , Zhu Guosong 
Published: August 2024

Abstract
Estimating depth from a solitary RGB image, known as monocular depth estimation, presents a significant challenge. Currently, most methods for this task involve designing increasingly complex networks to regress the depth map straightforwardly. However, we have adopted a more interpretable approach by using Conditional Random Fields from optimization methods. Additionally, to facilitate better information transfer between nodes, a multi-head attention mechanism is employed to calculate multiple energy functions, which are then optimized by the network into an accurate depth map. Experiments demonstrate that our method can accurately estimate the depth of landscapes.
Design of embedded real-time human detection system based on Zynq platform
Authors: Duan Hongliang , Xu Yang , Lu Zhenlin , Yang Ruoling , Qin Xiansheng 
Published: August 2024

Abstract
With the continuous breakthroughs in intelligent algorithm models, the computational complexity of target detection algorithms has continued to increase. At the same time, the performance and power consumption requirements of edge systems for hardware devices have become increasingly stringent. To better weigh the detection speed and accuracy of the artificial intelligence algorithm deployed on the embedded Zynq platform, we streamline and optimize the YOLO-v4 network model and then retrain the model based on the COCO dataset. Finally, the quantification and compilation processes are carried out in sequence to realize model migration and deployment from the server side to the Zynq device. Through experimental verification, the detection time of the modified YOLO-v4 algorithm model in this paper is 31.42 ms, and the detection error is only 4.0 pixels.
Design and application of portable eddy current inspection device for thimble tubes of nuclear power plant
Authors: Yu Yongcheng , Wang Baige , Yang Hongbo , He Yi , Li Xiang 
Published: August 2024

Abstract
The Thimble tube is an important piece of equipment in the nuclear power plant; its role is to provide a neutron flux detector with a channel for the reactor fuel assembly. Eddy current detection is the main way to evaluate and track the wear of the thimble tube. In order to conduct more efficient detection of the thimble tube, this paper has carried out a series of studies on the mechanical device of the eddy current detection of the thimble tube and the software control system. The inspection system is compact, portable, and does not require an external power supply or gas source, making it easy to carry and operate. It is well-suited for promotion in various nuclear power plants.
Bogie-YOLO: A key component detection model for high-speed train bogies
Authors: Wang Jiagan , Li Jinglong 
Published: August 2024

Abstract
The performance of high-speed trains heavily relies on the bogie, a critical component supporting the body and steering. Traditional methods for inspecting bogie components, which rely on manual identification, are not just time-consuming but also expensive. Furthermore, deep learning models often face difficulties in handling dimly lit, intricate backgrounds and detecting numerous small objects. To solve these challenges, we propose Bogie-YOLO, a targeted detection model tailored for bogie photography scenes. Bogie-YOLO utilizes the YOLOv5 framework and introduces the Coordinate and Squeeze-and-Excitation Attention Module (CSAM), improving positional information and channel relationships. Additionally, to expedite convergence, we adopt EIoU as the bounding box loss. Experimental results demonstrate an average Precision of 87.9% and an average mAP0.5 of 83.2% on the dataset of key components of train bogies.
Study on the influence of the blade inlet area-to-impeller inlet area ratio on hydraulic performance under various specific speed conditions
Authors: Chen Biwen , Chen Changsheng , Ma Zhenlai , Li Guoping , Zhang Yi , Zhang Ning 
Published: August 2024

Abstract
To investigate the unknown effects of the relationship between the impeller inlet area and blade design on the hydraulic performance of centrifugal pumps at various specific speeds within typical rotating fluid equipment, this paper aims to conduct experiments on three types of pumps with different specific speeds (ns
=155, ns
=125, and ns
=88). The study analyzes how the S1/S0
ratio, which represents the blade inlet area to impeller inlet area ratio, affects the hydraulic performance of different centrifugal impellers. Furthermore, it explores the changing patterns of how the S1/S0
ratio influences the pump’s head and efficiency. The results indicate a recommended range of S1/S0
ratios for centrifugal pumps optimized for efficiency at different speeds. When considering the impact on cavitation performance, it is suggested that the S1/S0
ratio should not exceed the upper limit of this recommended range.
A study on air quality prediction with multiple features based on GCN-LSTM
Authors: Jin Yongming , Ren Ge , Hu Yuxin , Wang Wennan , Zhang Jintao 
Published: August 2024

Abstract
Air pollution is closely intertwined with public health. However, air quality forecasting is a very challenging project, and the influencing factors are intertwined. This study focuses on analyzing and forecasting urban air quality from a spatiotemporal perspective, based on various factors that affect air quality. In this paper, a diverse dataset impacting air quality was collected, including pollutant concentrations, meteorological conditions, and traffic flow data. A GCN-LSTM model was developed to thoroughly explore the spatial relationships between different sites within the study area. This paper also conducted forecasts over different time lengths. The experiments revealed that weather data and traffic data can help improve air quality prediction results. The forecasting accuracy for RMSE and MAE were improved by an average of 8.7% and 8.5% in PM2.5, and 12.8% and 11.9% in NO2, respectively. Within the model, the GCN-LSTM model, which considers spatiotemporal features, outperformed single models and proved to be more advantageous in long-term forecasting. The findings of this study contribute to the improvement of air quality prediction effectiveness.