bluuebunny/crossref_metadata_2025_split huggingface dataset:
PCB Defect Detection Based on Image Processing and Improved YOLOv5
Authors: Chen Shiqiao , Liang Xiqing , Jiang Wenneng 
Published: August 2023

Abstract
Aiming at the problems of the high cost of PCB circuit board defect detection, many types of defects, and complex shapes, this paper proposes a PCB defect detection method that combines image processing and deep learning. We use the pruned YOLOv5 algorithm and image processing method to realize the identification and location of defects on the PCB board, which improves the accuracy of defect detection. The combination algorithm first puts the original image through image enhancement methods such as straightening, denoising, sharpening, and contrast enhancement, and uses the improved YOLOv5 convolutional neural network to identify defects. The experimental results show that, without reducing the detection accuracy of the original model, the algorithm compresses the model to 2.64 MB and shortens the inference time to 20.53 ms, which greatly improves the model deployment efficiency and detection speed.
Research on Landslide Detection in SAR Images Based on Multi-channel Change Detection Fusion
Authors: Li Zengqi , Chen Lifu , Long Fengqi , Li Zhonghao , Jin Haixin 
Published: August 2023

Abstract
The landslides have resulted in the grave loss of human life and economic damage. Currently, emergency investigations predominantly employ manual field investigations and remote sensing interpretation. Traditional methods for assessing landslides are time-consuming and ineffective for widespread and numerous landslides. In response, many scholars have successfully employed optical remote sensing imagery to extract landslides. Nonetheless, landslide regions are typically accompanied by copious rainfall along with inclement weather conditions, such as cloudy or overcast skies, which limits the observation capabilities of optical remote sensing methods. At this juncture, the advantages of SAR systems that can penetrate clouds, rain, and fog, and work around the clock under any weather conditions are highlighted. This paper proposes a framework using multi-channel SAR images to extract landslides. The framework consists of fused multi-channel change detection images and traditional detection methods, resulting in effective landslide detection. The proposed method was verified in an experiment using landslide data triggered by an earthquake in Milin County, Tibet, on November 17, 2017.
Multi-person Action Recognition Based on the HARNet Network in the Park Environment
Authors: Xie Shubiao , Liu Shumin , Chen Xingfeng , Zhang Xiaoming , Zhang Yunli , Dan Jiannan , Chen Shiyu , Wu Bingbing 
Published: August 2023

Abstract
Action recognition is crucial to public security, playing an important role in pedestrian protection. Recently, numerous approaches based on deep learning models have been reported for action recognition, which mainly has problems such as poor versatility, not good robustness, low recognition effects for low resolution, and occlusion in outdoor environments. This paper addresses this issue and aims to develop an approach for multi-person action recognition in the park environment. Frame extraction operation is first used to extract images from surveillance video in real-time, then YOLOv5 is adopted to detect and segment human targets from images. Thirdly, the AlphaPose algorithm is used to estimate the keypoint heatmap of the human body according to human targets. Finally, the heatmaps are preprocessed and then fused and classified by deep learning networks to complete the multi-person action recognition. In addition, an optimization method, i.e., Heatmap-based Action Recognition Network (HARNet), for ResNet is proposed in order to overcome the difficulty of information extraction from heatmaps, where the attention mechanism is considered. The approach presented here was tested on park surveillance video with multiple different scenarios, and the corresponding results verified its performance.
Transmission Line Instance Segmentation Algorithm Based on YOLACT
Authors: Chen Yong , Wang Yun-hui , Li Song , Li Meng 
Published: August 2023

Abstract
In the field of intelligent power patrol inspection, the transmission line is an important identification and detection target. The measurement of line spacing and ground distance are key technologies in the field of inspection. Therefore, it is necessary to quickly and accurately segment transmission lines. The transmission lines occupy a large span and vary widely in length. To improve the segmentation rate and accuracy of the transmission lines, we adopted EfficientNet as the main network. With the same accuracy, the number of parameters is reduced by 80% compared with ResNet 101. The network training cost is reduced, and the detection rate of the model is improved. To deal with the influence caused by the large change in transmission line length, we introduce adaptive anchor box calculation and the FPN + PAN structure. At the same time, multiple transmission lines are often overlapped, so the traditional NMS makes it easy to cause the omission or confusion of lines. We improve the NMS. Finally, we adopted the modified CIoU loss function to optimize the loss function. From the experimental results, our model has good performance for instance in segmenting transmission lines.
Tooth Surface Registration of Spiral Bevel Gear Based on Improved Genetic Algorithm
Authors: Liu Yongsheng , Hu Panru , Xu Zhibo , Chen Yixin , Tan Jiamin 
Published: August 2023

Abstract
To reduce the tooth surface measurement error of spiral bevel gear, a tooth surface registration method using an improved genetic algorithm (GA) was proposed according to the measuring principle of a coordinate measuring machine (CMM). To improve the performance of GA, the unit quaternion method was used to limit the initial population generation space, and then the simulated annealing algorithm was used to improve the search process. Finally, the measured and theoretical tooth surface was matched. Experimental results show that the improved genetic algorithm can compensate for more than 55% of the measurement error of the tooth surface.
Robotic Scheduling Strategies based on Machine Learning Algorithm
Authors: Wu Hongzhong , Xing Tenghui , Li Weiqing , Chen Hongbiao , Kang Yutao , Li Jiayang 
Published: August 2023

Abstract
Recently, robot scheduling has been widely concerned by numerous companies and researchers. They adopt an intelligent algorithm to design scheduling strategies for automated guided vehicle (AGV) and achieve required moving routes and certain aims. However, the existing robot scheduling strategies employ mathematical program models to arrange robots’ control and ignore machine issues that the robots may have. This will cause conflict and may lead the whole robot to a deadlock when the scale of conflict reaches a critical point. In this paper, we present a novel scheduling strategy for intelligent robots by training machine learning algorithms. Initially, we assume the robots contain a communication system, which can only receive signals from the central controller. Subsequently, we utilize the input parameters including robot conditions, working area, and working aims to train the machine learning algorithm. The work accomplishment ratio and conflict ratio are two essential indicators to measure our proposed method. Additionally, we also simulate the existing robot scheduling models to compare. From the extensive experimental results and comparison evaluations, we can conclude that our proposed method can achieve the robots scheduling tasks with reasonable costs. At last, we discuss the challenges of applying machine learning techniques to robotic scheduling and potential solutions and provide future directions in this field.
Garment Segmentation Network Based on Texture and Semantic Decoding Module
Authors: Liu Zhixian , Li Huiyin , Zhu Ping 
Published: August 2023

Abstract
With the great development of the apparel e-commerce industry, more and more people are engaged in this field. In reality, clothing is easily affected by background, perspective, occlusion, and other factors, so clothing segmentation is not accurate enough. This paper proposes a clothing segmentation network based on the texture and semantic decoding module of clothing. Based on the swing transformer network, this network proposes a clothing texture analysis module (CTAM) and a clothing semantic decoding module (CSDM). In the coding process, CTAM extracts clothing texture features to enhance spatial information, while CSDM uses mixed attention to enhance image context information during decoding to improve the accuracy of clothing segmentation. The experimental results show that compared with the original model, the average pixel accuracy of the proposed method on the LIP data set is increased by about 2%, and the average crossover ratio is increased by about 1.5%. The segmentation network significantly improves indistinguishable garment boundaries and the accuracy of garment image segmentation.
Research on the Identification Method of Radionuclide Based on the Lightweight Model
Authors: Yuan Chang 
Published: August 2023

Abstract
Traditional CNN has problems with significant parameters and is difficult to deploy in a resource-constrained portable spectrometer for radionuclide identification. We propose a lightweight neural network model based on GhostNet to address these problems. First, the Ghost-CS module is built by adding the channel mixing operation in the Ghost module to enhance the information exchange between different channels; Secondly, using the ECA module reduces the noise information in the spectrum. The results showed that the algorithm maintains a high recognition accuracy while the number of model parameters is greatly reduced, making it easier to implement in portable spectrometers.
The Ground Test System of Double Layer Air Floating Platform and Its Application
Authors: Wu Bojian , Zhao Yujie , Wang Song , Li Zongfeng 
Published: August 2023

Abstract
The suspension test system within the high microgravity scientific experiment cabinet is equipped with double-layer vibration isolation, effectively isolating external disturbances and creating a high microgravity environment within the inner body. Based on this characteristic, this paper proposes a double-layer plane air platform ground test system design. By using a high-precision acceleration sensor and gyroscope of the suspension test platform, the system coordinates the measurement of the double-layer air floating platform. The control mode and algorithm for double-layer vibration isolation in the suspension test platform are simulated through ground physics. Ground verification results from the double-layer plane air flotation test system show that the double-layer attitude control hardware of the suspension test platform meets the requirements, and the algorithm control effect is good, which provides sufficient experimental evidence for the normal operation of the suspension test system in orbit.
Numerical Simulation of Torque Variation During Paste Mixing and Kneading
Authors: Kang Zeqi , Tie Jun , Zhu Yan , Wang Zifeng , Li Guanlin 
Published: August 2023

Abstract
In the production process of aluminum electrolytic carbon anode, the kneading process is to fully mix asphalt and powder to obtain the best paste, because the kneading process is completed in a high-temperature kneading machine, there is no suitable automatic real-time judgment method for the uniformity of paste. Real-time online monitoring based on the viscoelastic change of paste during kneading is a potential method to judge the uniformity of paste. In this paper, the Discrete Element Method numerical simulation is used to study the variation law of stirring torque in the mixing and kneading wet mixing process, mainly examining the effect of viscosity on stirring torque. The results showed that the mixing and kneading torque varied periodically and the total torque of the stirrer was positively correlated with the viscosity.
Investigation on the Height of Fracture Zone in Goaf of Steep Coal Seam Based on Microseismic Monitoring
Authors: Yang Shouguo , Liu Haoxin , Huang Rumin , Li Jialin 
Published: August 2023

Abstract
The height of the fracture zone in the mined-out area is the basic parameter for controlling mine strata, preventing and controlling mine water, and arranging drilling holes for high-level gas extraction. Aiming at the development law of fracture zone in the steep seam and the limitation of traditional drilling inspection method, microseismic monitoring technology is used to investigate the height of fracture zone in goaf. Taking the 3103 south working face of a mine in Sichuan as an example, the scheme of microseismic monitoring and data collection of the working face is worked out. Through data analysis and processing, the range of the fracture zone in the goaf is 6.8 m– 34.2 m, and compared with the numerical simulation analysis results, the relevant results are basically consistent. It provides a new technical means for the investigation of “three zones” of overlying strata in the goaf of a steep seam.
Design of High-precision Water Quality Ammonia Nitrogen Online Detection Technology System Based on Micro-control Technology
Authors: Wang Tian , Wen Li , Ming-Gang Xu , Na He , Yong-Yue Ma 
Published: August 2023

Abstract
Based on Lambert’s theorem, this paper adopts the integration of micro-control technology and sequential injection technology and designs a water-quality ammonia nitrogen online detection system to improve the accuracy of ammonia nitrogen detection and to solve the demand for the determination of ammonia nitrogen index in the surface I to V water. The linearity of the assay data can be improved by controlling the variables in a control experiment, reducing the volume of the digestion cell, and reducing the inner diameter of the Teflon tube. Compared with the traditional ammonia nitrogen detection system, it has higher detection accuracy, and the number of reagents used and waste liquid generated have been greatly reduced, effectively improving the accuracy of ammonia nitrogen index detection. In this experiment, the determination of the ammonia nitrogen index was accomplished by salicylic acid spectrophotometry, and the range of determination covered the standard limits of ammonia nitrogen in a surface water environment from Class I to V water. The experimental results showed that: The regression model for the detection of ammonia index by the salicylic acid method was developed using the least squares fitting algorithm, and the linear range was from 0 to 2 mg/mL. The correlation coefficient of the experimental data for the 10 mm inner diameter tube was 0.9981 and that for the 15 mm inner diameter tube was 0.923. And the concentration and absorbance were both positively correlated, and the trend of the experimental data was consistent with the theoretical analysis. The system is designed to reduce operation and maintenance costs, improve detection accuracy, and efficiently analyze the ammonia nitrogen index in surface I-V water, providing technical support for environmental protection and surface water quality monitoring.
Fault Detection of Organic Heat Carrier Boilers Based on SeqGAN-CNN
Authors: Wang Yunhu , Zhang Guobao , Sun Tao , Zhang Yongchun , Huang Yongming , Liang Guoan 
Published: August 2023

Abstract
Organic heat carrier boilers are extensively used in industrial applications. However, the low accuracy of current fault detection methods, which primarily rely on statistical analysis and traditional machine learning methods, has become a significant challenge. This paper proposes a SeqGAN-CNN-based approach for detecting faults in organic heat carrier boilers. Firstly, SeqGAN is used to augment the dataset, solving the problem of limited fault sample data for training the CNN model. Then, the differential encoding method is utilized to transform one-dimensional monitoring data into two-dimensional grayscale images, resolving the issue that the CNN model’s input is generally multi-dimensional images while this study’s dataset is one-dimensional. Finally, the CNN model is trained and tested, and a simulation comparison is conducted with common industrial boiler fault detection methods. The experimental results demonstrate that the proposed method achieves higher accuracy, indicating that this method is accurate and effective for detecting faults in organic heat carrier boilers.
Design of an Intelligent Sensor System Based on Fluorescence Analysis
Authors: Li Wen , He Na , Wang Lei , Luo Xue-Ke , Tian Wang 
Published: August 2023

Abstract
In response to the current problems of serious pollution of typical organic substances in groundwater systems at petrochemical production sites, high cost of detection equipment, non-portability, low accuracy, and poor stability, a smart sensor for benzene and PAHs was proposed based on the fluorescence analysis method. The relationship curve between concentration and absorbance was derived using Lambert’s law from the different wavelengths and peaks of the absorbed light of different substances and was used to detect the concentration of organic substances. The experimental results showed that the online organic detection sensor based on fluorescence analysis had good stability for the detection of benzene and benzo(b)fluoranthene in groundwater systems at petrochemical production sites and could detect organic concentrations accurately and efficiently, providing data support for environmental protection and wastewater treatment. The method was low cost, simple in structure, high in detection accuracy, and good in interference immunity to meet analytical testing requirements.
Variational Auto-Encoder Reconstruction Networks for Classification of Hyperspectral and LiDAR Data
Authors: Zhai Ziming 
Published: August 2023

Abstract
The classification of remote sensing scene objects has been the subject of extensive studies in recent years due to the quick advancement of earth observation and remote sensing technology. More concentration, Hyperspectral images, and LiDAR data are complementary, and their combined use for data fusion can better mine the multi-dimensional features of ground objects in remote sensing scenes, which can effectively improve the classification accuracy and reliability of ground objects in remote sensing scenes. Single-modal remote sensing data frequently cannot fully meet the needs of ground feature classification due to the increasingly complex types of ground features. In order to solve this problem, we have developed two distinct multi-source fusion classification approaches using LiDAR and hyperspectral data and deep learning techniques. They are the reconstructed multi-layer perceptron network based on the variational auto-Encoder (encoding-decoding form) and the two-stream input convolutional neural network based on the cross-channel reconstruction mechanism. These two approaches can help us find more effective and deeper feature extraction methods and feature fusion methods in this research direction and design training. We adopted the network architecture model used in this area of research and used experimental data to demonstrate the effectiveness and superiority of the suggested network model.
The underwater obstacle avoidance method based on ROS
Authors: Zhang Jingguo , Lu Zongqing , Yu Chunliang , Shang Yingda , Chen Yuxiang 
Published: August 2023

Abstract
To achieve real-time obstacle avoidance for underwater robots, the artificial potential field method can be selected as the obstacle avoidance path planning algorithm. However, this method applies a large attractive force to the underwater robot when it is far from the target point, causing it to collide with obstacles. Additionally, when the underwater robot just enters the repulsion influence zone, the repulsion force that it experiences undergoes an abrupt change, resulting in a non-smooth obstacle avoidance path. Furthermore, developing the obstacle avoidance function based on software platforms such as MATLAB has a long algorithm verification cycle, and it is difficult to transfer the algorithm model to practical scenarios. Therefore, this paper proposes an underwater robot obstacle avoidance method based on ROS. Firstly, a dynamic adjustment factor is introduced into the potential field function of the artificial potential field method to redefine the obstacle influence zone, and an improved artificial potential field method is designed as the obstacle avoidance path planning algorithm for underwater robots. The experiment is conducted on the ROS simulation platform, and the results show that the improved algorithm can solve the two problems existing in the artificial potential field method. Secondly, nodes required for underwater robot obstacle avoidance are established based on the ROS operating system, and the improved artificial potential field method is integrated into the obstacle avoidance path planning node. Finally, the obstacle avoidance experiment of the Bluerov2 underwater robot is conducted in a pool, and the experimental results demonstrate that the proposed method can achieve real-time obstacle avoidance for underwater robots.
Design of Intelligent Voice Greenhouse System Based on STM32 MCU
Authors: Xie Yuhang 
Published: August 2023

Abstract
The traditional agricultural management mode no longer meets the needs of modern society, necessitating the development of modern agriculture. However, the limited education level in rural areas and the difficulty in popularizing complex IoT systems and internet platforms pose challenges to the development of intelligent agriculture. To address this, this paper proposes an intelligent voice greenhouse system based on a traditional STM32 single-chip microcomputer. The system utilizes various sensors to monitor environmental parameters and enable voice interactive control. This simple and practical approach promotes the use of smart agriculture, making it more accessible and easier to use. Physical testing has confirmed the effectiveness of this method, which will contribute to the development of intelligent agriculture in the future.
A Transformer-Based Model for Stratospheric Wind Field Prediction
Authors: Xu Yuhua , Jiao Xun , Kong Weisi 
Published: August 2023

Abstract
The stratospheric wind field environment is a key influence on the flight control of stratospheric aerostat, which is a popular field of research in recent years. In this paper, a Transformer-based stratospheric wind field prediction model is proposed for the construction of stratospheric wind field model and prediction, and the feasibility of the model is verified on a 5-year historical data set of the wind field in a place, then the parameters of the model are tuned by ablation studies, and finally, the Transformer prediction model is compared with GRU, LSTM and BP prediction models. The experimental results have shown that the proposed Transformer stratospheric wind prediction model can fully extract global features and its prediction accuracy is significantly higher than those of other prediction models.
Research on the New Energy Acceptance Capacity of Electric Networks Based on Random Optimization
Authors: Lu Mingyang , Jing Lantao , Ye Peng , Zhang Qiang , Wang Chao , Li Xinwei 
Published: August 2023

Abstract
According to the randomness of new energy power generation and power load in the power network, a new energy acceptance analysis method of power networks based on stochastic optimization is proposed. The algorithms for optimization based on the randomized simulation techniques is used, and the influence of the confidence level of different power network constraints and the combination of different new energy power generation access nodes is verified in IEEE-14. The results indicate that the model can fully consider the real-time operational fluctuations of new energy generation and power load in the electric network, and the solution algorithm is simple and easy to operate with fast calculation speed and small errors.