bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extraction of Building from Remote Sensing Imagery Base on Multi-Attention L-CAFSFM
Authors: Yuan Fuxiang , Jin Huazhong , Guo Xinyi , Bao Zhixi 
Published: August 2023

Abstract
The extracted building information can be widely applied in urban planning, land resource management, and other related fields. This paper proposes a novel method for building extraction, which aims to improve the accuracy of the extraction process. The method combines a bi-directional feature pyramid with a location-channel attention feature serial fusion module (L-CAFSFM). By using the ResNeXt101 network, more precise and abundant building features are extracted. The L-CAFSFM combines and calculates the adjacent two-level feature maps, and the iteration process from high-level to low-level and from low-level to high-level enhances the feature extraction ability of the model at different scales and levels. We use the DenseCRF algorithm to refine the correlation between pixels. The performance of our method is evaluated on the Wuhan University building dataset (WHU), and the experimental results show that the precision, F-score, recall rate, and IoU of our method are 94.94%, 94.32%, 93.70%, and 89.25%, respectively. Compared with the baseline network, our method achieves a more accurate performance in extracting buildings from high-resolution images. The proposed method can be widely applied in urban planning, land resource management, and other related fields.
The Application of Classifier Model Based on FA-SVM Algorithm in Speech Recognition
Authors: Wang Haijun , Su Jianna , Jin Tao , Zhang Xiaojiao , Qiao Zhili 
Published: August 2023

Abstract
In recent years, speech recognition technology has been increasingly widely used. How to improve the robustness of speech recognition systems has become the focus of researchers. This paper proposes an accent recognition model based on the Firefly algorithm to optimize the support vector machine. Firstly, a set of penalty factors and kernel parameters in the support vector machine are taken as firefly individuals, and the important parameters affecting the performance of the support vector machine are optimized by the firefly algorithm. A Firefly-Support vector machine (FA-SVM) model is established and applied to accent recognition. The example analysis shows that the FA-SVM accent recognition model has taller classification accuracy and faster running speed than the PSO-SVM and GA-SVM classifier models.
Research and Development of Machine Vision Algorithm Performance Evaluation System in Complex Scenes
Authors: Zhao Runjing , Yang Lei 
Published: August 2023

Abstract
In recent years, deep learning technology has gained widespread attention and has been successfully applied in various applications, including machine vision for automatic driving. However, one of the major challenges in this field is the ability of AI models to perform well in complex scenes with varying weather conditions and lighting conditions. The proposed scheme uses VR technology to simulate different weather conditions and lighting conditions and to evaluate the performance of AI products in such complex scenes. To further enhance the generalization capabilities of AI models, the Software Development Kit has been updated with complex scene data with configurable parameters, and training data with typical characteristics according to machine vision tasks. This approach has the potential to significantly improve the accuracy and reliability of machine vision systems for automatic driving, ultimately enhancing their safety and effectiveness.
Improved TransT Target Tracking Algorithm Based on Hybrid Attention
Authors: Li Feng 
Published: August 2023

Abstract
The TransT method pioneered the introduction of the attention mechanism into the target tracking field, but there are still shortcomings in stability. To improve the stability of the algorithm, we propose an improved feature fusion algorithm for visual image target tracking, and we adopt the LKA attention mechanism to strengthen the local attention to the target to ensure the tracker achieves both global and local attention. The tracking of visual image targets is achieved. The experimental results show that the proposed method has a short execution time and high tracking accuracy.
Printing roller image salient object detection based on multi-scale feature fusion
Authors: Bao Chenyang , Cao Shaozhong , Zhu Weijun , Jian Tengfei 
Published: August 2023

Abstract
In this study, aiming at the problem that the surface defect image of the printing roller has different sizes and a large number of defects, a salient object detection method based on stepwise multi-scale feature fusion is proposed, which consists of a stepwise multi-scale feature guidance module, a multi-resolution information fusion module, and a deeply supervised side output. Specifically, the stepwise multi-scale feature guidance module contains two paths, top-down and bottom-up, which strengthen the transmission of multi-scale features in the network, and fuse multi-level features through the multi-resolution information fusion module to suppress the interference of background noise and redundant features. In addition, deeply supervised side outputs are used to eliminate differences caused by background and foreground imbalance. The experimental results on the roller surface defect dataset show that the method can achieve Fmax, MAE of 0.7959, 0.0144, and the detection rate can reach 24.98fps, which has better detection performance than other algorithms.
Cross-modal multi-scale feature fusion-based RGB-T saliency object detection method
Authors: Zhang Guangyu , Niu Lianqiang 
Published: August 2023

Abstract
To cope with the challenge of significant target detection in complex scenes, this study proposes an RGB-T significant target detection method called CMFF. The method utilizes the complete potential of RGB and thermal infrared modal images and employs a codec structure and cross-modal multiscale feature fusion techniques. In the coding stage, two VGG16 backbone networks are used for multi-level feature extraction and CBAM attention module feature enhancement, and the enhanced features are fused using a stepwise fusion approach. Meanwhile, the weights of the two modalities are assigned using the L1-parametric fusion strategy to enhance the complementarity between them. In the decoding stage, global features are extracted from the high-level fused features by introducing the pyramid pooling module (PPM), and the low-level fused features are fused with multi-scale features in the up-sampling and encoding stages to enrich the global and local information of the feature map. Finally, this study conducted comparison experiments on the publicly available VT5000 dataset, and the method achieved an F-measure value of 0.863 and a mean absolute error (MAE) of 0.062, which significantly improved the overall detection performance relative to the six existing methods.
Research on New Hotel Intelligent Non-inductive Power Supply System
Authors: Duan Hongyi , Liu Niucong 
Published: August 2023

Abstract
The mainstream hotel intelligent sensorless power acquisition system in the market is mainly composed of infrared sensors and wireless timing switches, among which the infrared sensor is the core device. The widely used infrared sensors in the market generally detect the temperature changes that occur when a person moves in a room to determine whether there is someone in the room. This means that when a person stays stationary for a long time, the machine may not be able to recognize the temperature changes, resulting in problems such as power outages in the room. Therefore, the existing products have some shortcomings and still need to be optimized. Therefore, in response to the problem that infrared sensors cannot respond to a stationary human body for a long time, a device capable of periodically shielding the probe of an internal pyroelectric sensor is studied in the product structure to enhance the sensor’s sensitivity to a stationary human body, thereby solving the problem of not responding to a stationary human body for a long time.
Temperature Control of Methanol Distillation with High Gain Sliding Mode Variable Structure Based on SVM Multi-Classification
Authors: Dong Chao , Wang Chengzhi , Bo Cuimei , Li Jun 
Published: August 2023

Abstract
In the methanol distillation process, the production conditions are complex and changeable, and the temperature control requirements tend to be diversified. Traditional temperature control process requires manual adjustment of control parameters to adapt to different working conditions. In this paper, a sliding mode variable structure control algorithm based on support vector machine (SVM) multi-classification is proposed. By using the voting strategy of SVM multi-classification, the membership degree between the parameters η in the sliding mode control rate and the indexes of different working conditions is established, and the sliding mode control with a high gain observer is designed to control and adjust the actual temperature. The simulation of the whole temperature control system is realized by using the Simulink platform in Matlab, and the experimental results verify the effectiveness and correctness of the proposed control scheme.
Design and Implementation of Temperature-sensitive Particle Temperature Measuring Structure in Microchannel
Authors: Zhang Jianxin , Qie Jiating , Wang Lin , Liu Haoze 
Published: August 2023

Abstract
Any physical change or chemical reaction is closely related to temperature. Especially in the context of microscale continuous flow, the uniform temperature distribution affects the whole process of the reaction, so it is very important to detect the temperature in the microsystem. Temperature-sensitive particle temperature measurement is a kind of micro-scale non-contact temperature measurement. At present, the temperature-sensitive particle temperature measurement technology is mainly based on the direct design of experiments based on non-simulation. Starting from the microscopic point of view, this paper firstly analyzes the principle of fluorescence temperature measurement, establishes the consistency between the temperature-luminescence intensity relationship and the force-luminescence intensity relationship, and then uses the simulation software to build a microchannel temperature measurement model to analyze the temperature measurement effect of the selected temperature-sensitive particles. By comparing the Computational Fluid Dynamics (CFD) simulation results, the design of a non-contact particle temperature measuring structure inside the micro-channel is realized, which provides a feasible method in simulation.
Monocular 3D Object Detection Using Depth Fusion
Authors: Qiao Dewen , Niu Hongxia 
Published: August 2023

Abstract
It is an important task to estimate a 3D bounding box from monocular images for autonomous driving. However, the monocular pictures do not have distance information, so it is difficult to acquire accurate results. For the sake of solving the trouble of low accuracy of the monocular image in 3D target detection because of lacking distance information, an improved monocular three-dimensional target detection algorithm based on GUPNet and neural network was proposed to promote the precision of target detection. First, based on the geometric method proposed by GUPNet, the depth, and uncertainty are obtained by direct regression using a neural network. According to the difference in the accuracy of the two methods, a parameter α was introduced, and their depth scores are obtained from the uncertainty. According to the depth score and parameter α, the depth obtained by the two methods is fused to get the final depth. Test results prove that the proposed algorithm promotes average detection precision of KITTI data set in simple, medium, and difficult cases.
Material Consumption Smoothing for Mixed-model Assembly Lines Using an Improved Target Tracking Method
Authors: Zhang Yongyang , Mai Hanhua , Luo Junhao , Qiao Huarui , Liu Pai 
Published: August 2023

Abstract
To improve the material consumption smoothing for mixed-model assembly lines, this paper proposes a new optimized approach based on Euclidean distance and an improved target tracking algorithm. With this new method, the average demands for materials for each product are calculated, and the distances between the actual usage and average demand are computed with the Euclidean distance formula, which is the consumption rate of materials. Then, the material consumption rates of each product are sorted in order, and the minimum value is listed as the optimal scheduling. Therefore, by moving in circles, the optimal scheduling of all products can be achieved. In addition, the production simulation model is established using the FlexSim software to decrease the processing bottleneck. Through running the simulation model, the cycle time is reduced, and the line balance is increased.
Inverse Reconstruction of Rearview Mirror Shell and Mold Flow Analysis
Authors: Huang Xi-feng , Zhang Wu , Wang Yun-ming 
Published: August 2023

Abstract
The three-dimensional model of the automobile rearview mirror is made in three steps. Firstly, a non-contact 3D laser scanner is used to obtain the point cloud of the automobile rearview mirror, and then Image ware is used to denoise the point cloud, construct lines from the points, and obtain the grid model of the lines. Finally, the grid model is poured into the 3D software for surface reconstruction, and the product model is redesigned. After obtaining the three-dimensional model of the automobile rearview mirror, Moldflow is further used to simulate the injection molding process. The runner system of the first mock examination with two cavities is established. The filling parameters of the side gate runner system and the ejector rod latent gate runner system are compared. Combined with the CAE analysis results, it is concluded that the side gate runner system has better filling process parameters.
Research on 3D Particle Field Reconstruction Method Based on Deep Learning
Authors: Wang Shuai , You Fucheng , Wang Xuewei 
Published: August 2023

Abstract
Reconstructing 3D particle fields from single holograms is an important topic in the computer vision field. To increase network accuracy, we incorporate a channel attention mechanism based on the U-Net architecture in this research; meanwhile, we use the LeakyReLU activation function to accelerate the network convergence. Deep learning is used to extract information from a single hologram that can recreate the 3D particle field. The network receives the digital hologram as input, and the radius and 3D locations of the particles are converted into 2D grayscale images as real labels. Simulation and experimental results show that 2D grayscale images with clear edge textures can be rapidly encoded using the neural network, and the average SSIM and PSNR of the network output results with the real target on the test data set can reach 0.989 and 32.56.
Research and Design of Intelligent Anti-epidemic Disinfection Robot Based on ROS
Authors: Wang Siyu 
Published: August 2023

Abstract
In the context of the global epidemic of COVID-19, in order to solve the problems of heavy disinfection and epidemic prevention tasks in densely populated indoor public places and a low degree of automation and intelligence, a smart anti-epidemic disinfection robot solution that integrates multiple disinfection methods is proposed. The solution is designed and developed based on the open-source robot operating system (ROS) with digital intelligent remote disinfection function, micron-level atomization disinfection function, and multi-angle ultraviolet light disinfection function, providing a full range of high-efficiency disinfection and disinfection for the air environment bacteria protection. It has the functions of autonomous positioning and navigation, intelligent path planning, and real-time perception of environmental parameters. It is also equipped with a remote monitoring platform to monitor the status of the disinfection robot itself and task execution in real time to ensure stable operation and to issue disinfection tasks at regular intervals or in certain areas. The practice has proved that this solution can effectively solve the problem of the high labor cost of manual disinfecting so that one person can quickly complete the whole-area disinfecting work, greatly improving work efficiency, and it has good application value and is worth promoting.
The Grass Square Latch Paving Agency Based on the Bionic Spinning Knife
Authors: Li Jia-lin , Li Chao , Wu Yong-tao , Cao Shao-peng , Wang Meng-hao 
Published: August 2023

Abstract
To better solve the problem of land desertification, this paper designed a portable grass grid-laying robot to lay grass squares to prevent wind and sand fixation. Among them, the ploughing module was proposed to use a bionic rotary tillage knife to improve soil cutting efficiency. By analyzing the characteristics of the forefoot claw contour curve of the groundhog, the point cloud data of the claw toe profile curve was obtained by reverse engineering technique, and then the Gaussian equation of the contour curve was fitted by MATLAB and the toe tip profile fitting curve was obtained. The BT model of the obtained fitting curve was applied to the national standard rotary tillage knife to design a preliminary bionic rotary tillage knife. When W0 was 50 mm and θ was 35°, the resistance of the rotary tillage knife was the least and the cutting performance was the best, and the final bionic rotary tillage knife was designed. In the field test, the data pointed out that compared with the traditional national standard rotary tillage knife, the maximum stress of the bionic rotary tillage knife was only 32.21 MPA, the average torque decreased by 10.16% compared with the national standard rotary tillage knife, the average power decreased by 7.68% to 71.4 KW, the tool quality was reduced by 8.26%, and the crushing rate was greatly improved. This study can provide some reference for the design of grass grid laying machines and rotary tillage knives to reduce resistance and consumption.
Research and Design of Speed Profiles for Stacker Cranes
Authors: Zhang Yuzhe , Li Minghui , Fang Yanchao 
Published: August 2023

Abstract
Given the industrial and commercial pursuit of efficiency, it has become urgent to enhance the overall operational efficiency of automated three-dimensional warehouses. This paper focuses on improving the operational efficiency of the stacker in such warehouses. To this end, we deduce the deflection formula and simulate the swing of the stacker to identify the factors that affect its operational stability. Based on these findings, we propose a new speed curve optimization control scheme that properly controls the maximum acceleration and selects an appropriate acceleration change form. This scheme can effectively restrain column swing and improve the stacker’s operational stability, thereby boosting work and production efficiency.
Design of an IoT Air Disinfection Machine Control System
Authors: Hu Yuanhang , Sun Shaodong , XiaJie  , Jiang Yongcheng 
Published: August 2023

Abstract
In this paper, a control system for an IoT air disinfection machine is designed based on the ESP32 microcontroller. The system utilizes the ESP32 as the main control chip and adopts a control scheme based on IoT technology. Remote control and monitoring of the air disinfection machine are achieved through a cloud server. The sensor module detects parameters such as particles, carbon dioxide, temperature, and humidity in the air to realize real-time monitoring of air quality. The control module programs the opening and closing of actuators such as relays and motors to achieve the automatic control of the air disinfection machine. The communication module is connected to the cloud server to realize remote control and monitor of the air disinfection machine. The power management module monitors and controls parameters such as current and voltage to achieve energy consumption monitoring and control of the air disinfection machine. After testing, the system fully meets the design requirements.
Research on Simulation Technology of Intelligent Manufacturing Scene Operation Mechanism Based on Evolutionary Game Theory
Authors: Men Feng , Wang Mian , Dong Fangqia 
Published: August 2023

Abstract
Intelligent manufacturing is a continuously evolving and iteratively improving system engineering that requires long-term adherence and step-by-step implementation. The continuous process changes during scenario iteration have stimulated the demand for process simulation tools in intelligent manufacturing management processes. However, currently, existing tools mostly simulate process coherence and rarely simulate and verify the stability of the process. Accordingly, the article proposes a construction idea for an intelligent manufacturing scenario iteration management platform and combines the analysis of scenario innovation entities and strategies to innovate the simulation method of scenario innovation operation mechanism based on evolutionary game models, improve the comprehensiveness of simulation, and support the continuous iteration of intelligent manufacturing scenarios.
Control Method of Wire Repairing Robot Based on Semantic Segmentation and Current Detection
Authors: Zhang Lingrui , Wang Xuhong , Fan Shaosheng 
Published: August 2023

Abstract
The traditional repair of conductors in power systems, using manual work at height, carries a high level of risk. To ensure personnel safety and improve operational efficiency. Based on the development of a conductor repair robot using pre-stranded wires, the focus is on a semantic segmentation-based adaptive conductor arc sag control method, which enables the robot to adapt to conductors with different arc sags by segmentation identification of the conductor, offset calculation, and proportional-integral controller design to realize the robot hanging on the conductor. Subsequently, the winding gap is monitored according to the change of motor current, and a fuzzy controller is used to realize the robot winding operation. The robot prototype tests show that the robot is capable of adapting to the wire pitch and completing soft repair work.
Prediction of Industrial Boiler Energy Efficiency Using Stacking Ensemble Learning
Authors: Zhong Defeng , Zhang Guobao , Zhang Yongchun , Liang Guoan , Huang Yongming 
Published: August 2023

Abstract
Accurate predictions of energy efficiency facilitate the energy-saving renovation of industrial boilers. To improve prediction accuracy, this paper proposes an industrial boiler energy efficiency prediction model based on the stacking ensemble learning method. The base models, including LR, RF, GBDT, XGBoost, and ANN, are established in this study, and then the stacking ensemble learning method is employed to integrate these base models into a strong prediction model. Experimental results indicate that the stacking model outperforms the base models in terms of accuracy and stability.
Study on the Propagation Law of Shock Wave Pressure in the Tunnel
Authors: Chen Jiahui , Xu Chundong 
Published: August 2023

Abstract
As a typical underground fortification, tunnels are one of the key targets of an explosive warhead. The study of the propagation law of shock wave pressure in the tunnel is of great significance to accurately evaluate the explosive power of ammunition. The mathematical model of the propagation law of shock wave pressure in the tunnel is established by carrying out the explosion test of the warhead under different test conditions based on dimensional analysis and combined with the experimental data. And the effectiveness of the mathematical model is verified by making the explosion test of the warhead in the tunnel, and the result shows that the maximum relative deviation between the measured value and the calculated value of the shock wave overpressure peak is 10.61%. Therefore, it is proved that the mathematical model can be used to predict the shock wave overpressure peak in the tunnel and provide some reference for the power evaluation of warhead explosion in the tunnel.