bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Experimental Study on Propped Fracture Conductivity in Tight Reservoir
Authors: Zhao Jinsheng , Li Yongming , Chang Du , Hui Bo , Li Xianwen 
Published: December 2022

Abstract
Propped fracture conductivity is a major element which will influence the stimulation effect after fracturing. To reveal the major factors affecting the propped fracture conductivity, the propped fracture conductivity with 20-40 mesh sand and ceramics of different sanding concentrations was experimentally studied. It can be concluded that, the propped fracture conductivity decreases as the closure pressure increases. For the silica sand proppant, when the closure pressure increased to 41.4 MPa, the downward trend of the conductivity became slower. For silica sand proppant, when the closure pressure is small, with the increase of sanding concentration, the fracture conductivity increases. When the closure pressure is greater than 41.4 MPa, the conductivity almost does not increase. Considering proppant costs, ceramic proppant can be used in low-closure pressure formations and silica sand proppant can be used in high-closure pressure formations.
Research and practice on the intelligent transformation of the gate control system in a large hydropower station
Authors: Lu Shuai 
Published: December 2022

Abstract
With the development of information technology, the research on the hydropower stations intelligence has gradually become an important indicator for the evaluation of the hydropower industry. Therefore, it is of great significance to carry out the intelligent transformation of relevant equipment and systems in hydropower stations by integrating emerging information technology. In view of the implementation content of the intelligent project transformation of the gate control system in a large hydropower station, this research studies the structural design of the control system, the power supply design, the component selection, the multiple landing gate function, the intelligent protection and monitoring function, and the intelligent network architecture design of IEC61850, and analyses the practice on the intelligent transformation. Therefore, this paper can provide a certain reference for the intelligent transformation, research, and development of the gate control system of hydropower stations.
Application of the unmanned inspection system in power generation enterprises
Authors: hao-Li Jing 
Published: December 2022

Abstract
In the power generation company, it is the unmanned inspection system that can through visual image recognition, infrared monitoring, and fixed-point sensor complete the real-time monitoring of the plant area and equipment, through the system building perception layer, network layer, and application layer realizes the remote control command issued, and complete the equipment running status and working parameters of the real-time monitoring and remote control. Through the design of the bearing state feature extraction method and the fusion of machine mechanism, and artificial intelligence data processing method, the intelligent diagnosis and early warning of the whole plant equipment are realized, and the one-click inspection of power generation enterprises is achieved.
An Electromagnetic Environment Monitoring Platform for AC-DC Parallel Line
Authors: Wang Guangzhou , Tian Xu , Liu Weipo , Li Yuan , Guo Xing , Li Haifeng 
Published: December 2022

Abstract
With the rapid development of China's economy, the electricity demand is increasing and the scale of power grid construction is expanding. Large-scale construction of transmission and substation projects leads to an increasing shortage of transmission corridors, which inevitably leads to the formation of parallel erection of AC and DC transmission patterns. When the AC and DC transmission lines are erected in parallel, the coupling effect makes the hybrid electromagnetic environment of the AC and DC parallel lines more complicated. In this paper, with the track-supported intelligent robot as the hardware and the AC-DC mixed electromagnetic environment data analysis management system as the software, an automated synchronous measurement system integrating the measurement equipment of frequency electric field, radio interference, synthetic electric field and meteorological monitoring equipment is established. It can realize mobile multi-point time-divisional measurement and dynamic management of data with one set of equipment, carry out hybrid electromagnetic environment monitoring for the typical AC-DC parallel line sections that have been put into operation, and provide technical support for the rational design of subsequent large-scale UHV projects and the evaluation of the AC-DC hybrid electromagnetic environment.
Low-Latency Autonomous Service Orchestration for the Power Communication Network
Authors: Liang Wenjuan , Li Yijie , Li Bo , Liu Pengju , Kong Deyu 
Published: December 2022

Abstract
Power communication network based on optical technology can support the operation, production and scheduling of power systems. However, due to the dynamic and information uncertainty factors in the network environment, the independent service arrangement of the power communication network is faced with many challenges. To address these challenges, a software-defined network (SDN)-empowered the power communication network architecture. In this paper, we proposed a routing algorithm based on ε-greed to minimize total transmission delay of service data flow, which can support the autonomous, intelligent, and flexible orchestration for the power communication network. Simulation verify the superiority of the proposed ε-greedy algorithm in the aspect of delay and optimal path selection.
A rapid detection and sorting system for seed maturity
Authors: Zhang Yuhan , yu Lintao , Zhang Shida 
Published: December 2022

Abstract
To solve the problems of low speed and low efficiency of seed sorting, this study designs an efficient seed separation method. In this method, a vibrator is used to scatter the seeds and make them fall rapidly through parallel multi-tracks. The seeds are irradiated by a light source with a wavelength of 670 nm after passing through the identification area. The high-speed linear array camera takes photos of the irradiated seeds. The convolution neural network algorithm combining residual learning and attention mechanism is used to analyze and judge the maturity of the seeds. The algorithm model can effectively capture the characteristics of the seed photos and conduct efficient processing analysis and judgment, Finally, the low maturity seeds are blown away by an air compressor and blower to achieve rapid and efficient seed sorting. The test shows that the sorting speed can reach 50 particles/s and above, and the sorting accuracy can reach 96%.
A Lightweight Two-Stream Model for Driver Emotion Recognition
Authors: Hu He-xuan , Zhang Lu-feng , Yang Tian-jin , Hu Qiang 
Published: December 2022

Abstract
In recent years, more and more attention has been paid to the traffic safety of drivers. Studies show that the motor vehicle speed will affect the incidence of traffic accidents and traffic accident casualty rate. In this paper, machine learning vision technology is used for real-time recognition of driver emotions, and a lightweight SIC-NET+RC-NET dual-stream recognition model is designed to cope with different scenarios of vehicle speed during driving. SIC-NET is a lightweight CNN network model, which greatly reduces the number of model parameters and computational power requirements, sacrifices part of accuracy, and greatly improves the real-time performance of model recognition. To improve the residual convolutional network model of inception blocks, RC-NET nested multiple micro-convolutional neural networks are used to identity mapping between different inception blocks to enhance the network training effect, expand network width and convolutional depth, and alleviate network degradation. This article stipulates that when the vehicle speed is greater than 60km/h, SIC-NET is used. When the vehicle speed is less than or equal to 60km/h, SIC-NET+RC-NRT is used. The FER2013 dataset was used for testing and validation in this paper. When SIC-NET is used, the model has better real-time performance and can provide a more timely warning when the vehicle speed is higher. When SIC-NET+RC-NET is used, the accuracy of the model in the FER2013 dataset is 69.65%, and the model can better cope with the scenario of a low vehicle speed. The experiment shows that the lightweight dual-flow model has good robustness and identification effect, which solves the problem of using the same model for different vehicle speeds and can effectively ensure the safety of the driver’s life and property.
Design and Implementation of Wastewater Pollutant Data Analysis and Visualization System for Vessels
Authors: Lv Taizhi , Tang Peiyi , Zou Yujuan 
Published: December 2022

Abstract
To monitor the discharge of water pollutants from vessels, a monitoring system for the discharge of water pollutants from vessels has been established on the Yangtze River. The system generates tens of thousands of ship wastewater pollutant data every day. The analysis and visualization of wastewater pollutant discharge data can help the monitoring department to grasp the discharge situation and improve the accuracy of supervision. This system extracts the data to the data warehouse in real-time based on the data extraction technology, improves the query efficiency based on the three-level storage architecture, makes real-time pollutant data statistics based on the stream computing technology, and realizes the visual display of the data based on the front-end and back end-separation technology. The system has been applied in the maritime supervision department to provide a data basis for vessel wastewater pollutant supervision and decision-making.
Research on PM2.5 Pollution Prediction Method in Hefei City Based on CNN-LSTM Hybrid Model
Authors: Wang Qiang , Zhang Yiwen 
Published: December 2022

Abstract
The air quality data has the characteristics of large scale and high time sequence. In the deep learning model, CNN has good feature extraction and expression ability through convolution operation. LSTM has strong time series data processing ability, which effectively solves the long-term dependence problem in the time series data that CNN cannot process. However, a single feature extraction or time series processing method will affect the accuracy of the model. This paper proposes a PM2.5 pollution prediction method based on the CNN-LSTM hybrid model, which can fuse the time series and nonlinear features of the data. By selecting the air quality and meteorological data of Hefei City in Anhui Province from 2018 to 2021 for prediction research, the experimental results show that the CNN-LSTM hybrid model is better than the LSTM model in terms of RMSE, MAE, and R2 evaluation indexes, which reflects higher prediction accuracy. It can not only be used for real-time prediction of PM2.5 pollution, but also for the prediction of other pollutants in the atmosphere. It also provides a reference for the application of CNN-LSTM model in solving other time series big data.
Classification of epileptic EEG based on improved empirical wavelet transform
Authors: Lv Chao , Song Bo 
Published: December 2022

Abstract
Electroencephalography (EEG) is the most commonly used method in the diagnosis of epilepsy diseases. In order to identify epilepsy EEG signals more effectively, an automatic identification method of epilepsy EEG signals based on improved empirical wavelet transform (EWT) is proposed. Firstly, in view of the difficulty of spectral division in the EEG signal processing of epilepsy by empirical wavelet transform, an improvement measure is proposed, that is, the average difference spectrum of the signal is obtained to replace the signal spectrum in the empirical wavelet transform, and then a number of component signal with epileptic characteristic can be obtained from the original signal. Afterward, feature extraction and classification are completed through a common spatial pattern and AdaBoost algorithm. Simulation analysis was carried out on the Bonn epilepsy EEG data set, and the EEG signals of healthy people and epilepsy patients were identified and classified in the interictal and ictal periods, and high classification accuracy was achieved.
Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network
Authors: Guan Zhuang , Hu He-xuan , Hu Qiang , Lu Jia-min 
Published: December 2022

Abstract
Aiming at the lack of sufficient meteorological data for runoff prediction in areas with few data, the accuracy of the data is improved by using soft thresholding under the deep attention mechanism, and a deep residual shrinkage long-short memory network prediction model is constructed combined with the watershed characteristic data. The model is validated by using 134 watersheds with spatial correlations in the CAMELS dataset. According to the experimental findings, the model is more accurate than the LSTM model, HIV model, and mHM model in predicting runoff in regions with little data, with an accuracy of 86%.
Dynamic Load Regulation of Robots with Multi-sensor Fusion
Authors: Cao Xinqian , Tan Benying , Li Yujie , Ding Shuxue 
Published: December 2022

Abstract
Multi-sensor fusion technology has been extensively deployed for the autonomous navigation and control of robots. Theoretically, by increasing the number of sensors, more effective data on the surrounding environment and obstacle information can be obtained. However, in practical applications, a massive number of sensors will not only increase the cost of using sensors in the system but also add to the difficulty and cost of data processing, leading to unnecessary resource consumption. To solve this problem, first, the robot navigation area is divided according to the importance of the navigation area and the characteristics of sensor parameters. The navigation non-core area adopts the single lidar sensor, while the core area adopts the multi-sensor fusion of “lidar +” to reduce the number of sensors used in the fusion and improve the performance of multi-sensor fusion after the division. Then, in consideration of the higher requirement for computing capacity of the device imposed by deep learning, the multi-sensor fusion algorithm is introduced into the dynamic load regulation mechanism to solve the problem from the algorithm level so that the outperformance of deep learning in feature extraction and other aspects can be utilized to reduce the performance requirements of the device and the overall power consumption of the system. Finally, by building the ROS experimental platform, it is verified that the robot can use autonomous navigation in complex and diversified home scenes.
Endoscopic Image Denoising Algorithm Based on Spatial Attention UNet
Authors: Zhang Biao , Li Mianhao , Zhou Changjiang , Yang Qing , Wang Liqiang , Yuan Bo 
Published: December 2022

Abstract
Endoscopic image has complex backgrounds and spatially different noise, bringing mainstream denoising methods to the problem of incomplete noise removal and the loss of image detail. Thus, an endoscopic image denoising algorithm based on spatial attention UNet network is proposed in this paper. UNet based on residual learning is utilized as the backbone network. Spatial attention modules based on noise intensity estimation and edge feature extraction modules are used to remove noise better while preserving the image details and improving generalization ability. We take endoscopic images of real scenes using gastroscopy and compare our method with mainstream methods. Experimental results show that our approach improves PSNR by 3.51 or 2.93 and SSIM by 0.03 or 0.015 compared with CBDNet or EDCNN, respectively. Our method can effectively improve the impact of noise on the image quality of endoscopic images, thus better assisting doctors in diagnosis and treatment.
Reservoirs Optimal Operation Based on Reinforcement Learning
Authors: Hu He-xuan , Qian Ze-yu , Hu Qiang , Zhang Ye 
Published: December 2022

Abstract
Reinforcement learning has been widely used to solve the problem of optimal operation of a single reservoir. However, the research on the combination of reinforcement learning and optimal operation of reservoir groups is still blank. The complexity of reservoir groups brings difficulties to the application of reinforcement learning. In order to solve the complex problem of optimal operation of the reservoir group, Q-learning combined with the penalty method is used to generate the optimal operation scheme of the reservoir group by exploring the optimization Q value of the operation sequence. The experimental results show that this method can achieve the optimal solution in theory. Furthermore, Q-learning combined with a feasible direction method is used to establish time feasible state table and state-feasible action hash table according to the constraints of the discrete four-reservoirs problem, so that the Q-learning algorithm can shorten the optimization time and obtain the optimal solution.
Vehicle Route Planning Using A* Algorithm in Real-world Roads
Authors: Liu Xingyu 
Published: December 2022

Abstract
With the technical progress in the fields of robotics as well as artificial intelligence, many intelligent algorithms which focus on robot path planning problems have been developed so far. A* algorithm is an efficient and technically mature path planning algorithm that can efficiently solve robot path planning problems. In this context, this paper proposes an improved A* algorithm to plan roads for automobiles in urban areas, introduce time factors into the cost function, select an urban area, use grid map modeling, and simulate in the MATLAB environment. The final result shows that this kind of algorithm can efficiently solve the problem of automobile road planning.
Study on the UWB location algorithm in the NLOS environment
Authors: Li Jinwang , Gao Tongyue , Wang Xiaobing , Guo Weiping , Bai Daizhuang 
Published: December 2022

Abstract
At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.
Research on Inverse Kinematics Solution of Manipulator Based on FGA Optimized BP Neural Network
Authors: Mao Liangan , Li Li 
Published: December 2022

Abstract
In view of the complexity of the traditional inverse kinematics solution, it is solved by the mapping of the BP neural network, but the precision is low and the network performance is not good. By using FGA to optimize BP neural network, the shortcomings of the neural network itself can be overcome. Taking the pose matrix and joint angle of the manipulator as the input and output variables of the prediction network, the inverse solution prediction BP network of the manipulator is constructed, and then samples are selected for training. Finally, the network test and simulation results show that the optimized BP neural network has higher prediction precision and improved network performance.
An Improved CLIQUE Algorithm
Authors: He Honglei , He Yuxuan , Zhu Chunjiang 
Published: December 2022

Abstract
CLIQUE is a clustering algorithm based on grid and density, which is sensitive to grid division parameter M and density threshold R, and has poor clustering accuracy. This paper proposes an improved algorithm, which is not sensitive to the initial grid division parameter M, and can automatically split the grid. Combined with the K-means algorithm, the clustering effect is improved. The F value on the S1 dataset is 36.9% higher than that of the classic CLIQUE algorithm.
Human Ear Recognition Algorithm of YOLOV3 Based on Attention Mechanism
Authors: Lei Yanmin , Pan Dong , Feng Zhibin , Qian Junru 
Published: December 2022

AbstractTo address the problems of false detection and the lack of accurate target bounding boxes in the process of detecting human ear recognition, a SE-YOLOv3 target detection algorithm with improved YOLOv3 is proposed. Based on the YOLOv3 algorithm, the effects of embedding the SE attention module into different positions of the model on the detection performance of the algorithm are studied and analyzed separately. It is proved that embedding the SE attention module in the parallel ResNet network in the backbone of the YOLOv3 model can effectively improve the detection accuracy of the algorithm. And it is experimentally demonstrated in CCU-DE and USTB human ear datasets.
Improving the performance of Uyghur speech recognition based on Factorized Time-Delay Neural Network
Authors: Zhang LuZhuo , Liu LanJiao , Huang GaoCe , Zhu HaiPeng , Zha HaiPeng , Qiu ZiCheng , Zhang Xia , Lu WenQiang 
Published: December 2022

Abstract
Speech recognition systems have low accuracy in recognizing the Uyghur language, a low-resource language, due to its strong language specificity and few public training datasets. Given this problem, considering the characteristics of Uyghur, we use morpheme units to build a language model and use mixture data augmentation methods to expand the training data. A 9-layer TDNN-F is applied, which can effectively utilize contextual information. An optimal 9.88% WER (Word Error Rate) is achieved in experiments on the open-source dataset THUYVG-20. Compared to the baseline system of this dataset, the WER is reduced by 6.7%, which significantly improves the accuracy of the Uyghur speech recognition, and provides a reference in other low-resource languages for speech recognization.
Research on the Tray-type Shuttle Crank Connecting Rod-parallelogram Mechanism Jacking System
Authors: Cao Chongzhen , Song Hong , Wang Fengqin , Xu Haohao , Feng Kecheng , Liang Fuxing 
Published: December 2022

Abstract
Pallet shuttle plays a key role in the efficient and stable operation of high-density automated storage systems. For tray type through the roof overloading its cargo, we design a crank connecting rod-combinations of parallelogram mechanism type jack-up system. This paper introduces the principle of the jack-up structure in detail, and part of the dynamics of jack-up institutions is analyzed, calculated, and simulated. The driving motor of the jacking mechanism is selected. The feasibility of the scheme is verified by a prototype test.
Galaxy Morphology Classification with DenseNet
Authors: Hui Wuyu , Robert Jia Zheng , Li Hansheng , Wang Zijian 
Published: December 2022

Abstract
Galaxy classification is crucial in astronomy, as galaxy types reveal information on how the galaxy was formed and evolved. While manually conducting the classification task requires extensive background knowledge and is time-consuming, deep learning algorithms provide a time-efficient and expedient way of accomplishing this task. Hence, this paper utilizes transfer learning from pre-trained CNN models and compares their performances on the Galaxy10 DECals Dataset. This paper applies opening operation, data augmentation, class weights, and learning rate decay to further improve the models’ performance. In our experiments, DenseNet121 outperforms the other models and achieved approximately 89% test-set accuracy within 30 minutes. The second best-performing model, EfficientNetV2S, takes double the time achieving 2.43% lower test set accuracy.