bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Discussion on Fire Image Recognition Based on Deep Learning
Authors: Cui Yongyi , Qu Fang 
Published: November 2021

Abstract
Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.
Influence of Supercritical Carbon Dioxide Brayton Cycle Parameters on Intelligent Circulation System and Its Optimization Strategy
Authors: Li Kai , Sun Kai 
Published: November 2021

Abstract
The supercritical carbon dioxide (SCO2) Brayton cycle takes advantage of the special physical properties of carbon dioxide near the critical point (31.1 °C, 7.39MPa), and has higher energy conversion efficiency than the current large-scale steam power cycle. This cycle can be widely used in the field of power generation, but a lot of research work is still needed in terms of component parameters and layout under different working conditions. In this regard, the purpose of this paper is to study the influence of supercritical carbon dioxide Brayton cycle parameters on cycle efficiency and its optimization strategy. Based on the first law of thermodynamics, this paper uses Aspen Plus software to establish S-CO2 Brayton cycle system models with different circulation arrangements. In this paper, the existing algorithm of the simulation system and the newly-built algorithm are used to build the S-CO2 shunt and recompression Brayton cycle system model, and the accuracy of the model is verified with experimental data from literature. Then this paper conducts disturbance experiments on the model to study the influence of heater heating, valve opening and precooler cooling on the system, and analyze the dynamic characteristics of the system. Experimental results show that the thermal efficiency of the simple Brayton cycle is much lower than that of the recompression Brayton cycle and the split recompression Brayton cycle under higher parameters. The compressor outlet pressure and the turbine inlet temperature have an effect on the efficiency of the recompression Brayton cycle. The impact is significant, and the optimal value of the compressor shunt coefficient is between 0.5-0.7, which provides a reference for the layout optimization method of the SCO2 Brayton cycle and the optimization of the same type of power generation cycle.
Modeling and Experimental Verification of Heat Dissipation Model for Test System of Li-ion Battery Test-bench
Authors: Qu Jie , Huang Meihua , Wang Chao 
Published: November 2021

Abstract
In order to develop a test-bench which can accurately test the mechanical signal of lithium-ion battery under various working conditions, the finite element model of heat dissipation simulation is established for different test systems designed in the mechanical system of the test-bench. At the same time, pulse excitation experiments are used to verify the simulation results, and the bulk force experiments are carried out to verify the optimal test system structure chosen accord to the simulation results. The mechanical structure of a test system is composed of a lithium-ion battery and upper/lower spacer. In its finite element model, the finite element model of the lithium-ion battery is established by the actual measurement after cutting lithium-ion battery by a diamond cutter, and spacers are established according to their actual design. The heat dissipation simulation finite element model can simulate the heat dissipation of an actual test system, which is conducive to the design and selection of an optimal test system, so as to improve the accuracy of test data measured through the test-bench and provide a reliable data basis for the development of the battery management system coupling temperature-current-voltage-swell-force.
Chaotic Analysis of Fractional-Order Permanent Magnet Synchronous Generator for Wind Turbine
Authors: Yang Li , Yang Fuzhao , Huang Sudan , Liang Tao , Huang Tianmin 
Published: November 2021

Abstract
Fractional-order wind turbine is a strongly coupled non-linear dynamic system. It mainly studies the significant chaos characteristics such as the complex chaotic motion with fractional order varying. According to the mathematical model of the system, the fractional order Lorenz chaotic equation is established by linear affine transformation and time scale transformation. The theory of Lyapunov stability analysis is adopted to deeply study the development process of the system from stable operation to chaotic motion. The correctness of the chaos characteristics of the system is verified.
Automatic Kinking Device System Design Based on Microcontroller
Authors: Gao Peng , Li Wei , Fang Jian , Wu Yue 
Published: November 2021

Abstract
To solve the problem that traditional Kinking equipment can not meet the needs of handcrafters today. The system to automation, intelligence asa breakthrough point, the use of STC12C5A60S2 micro-controller as the control core of the entire system, through the Hall encoder, film keyboard, LCD display, so that producers can use only keys to control the wire-tanger, and can be real-time from the display to observe the state of work, so as to achieve low-cost, high-efficiency wire production. Experiments show that in the case of 14 hours of continuous work, the tanger can still complete the normal tanger work and the accuracy rate is as high as 99.8percent. Low cost, good stability, high efficiency, small size, wire-tanger can assist people’s production and life, to achieve the goal of efficient production.
Application of Optimization Technology for Overhaul Decision of Substation Equipment Based on Machine Learning
Authors: Liu Ziquan , Zhu Xueqiong , Ma Jingtan , Hu Chengbo , Fu Hui , Zhao Ke 
Published: November 2021

Abstract
With the continuous improvement of living standards and the continuous increase of electricity load, the number of power transmission and transformation equipment also increases rapidly. The original maintenance mode is not enough to guarantee the safe operation of the huge power grid. This paper mainly studies the research and application of machine learning based maintenance decision optimization technology for substation equipment. Starting from the technical principles of online monitoring and condition maintenance of substation equipment, this paper has realized an intelligent monitoring and maintenance early warning system combined with deep learning model. The main functions of this system include monitoring device management, operation monitoring and comprehensive display, etc., which can effectively carry out online monitoring and state early warning of substation equipment. It greatly improves the intelligent degree of operation and management of substation equipment, saves the cost of traditional manual monitoring, and effectively prevents the economic loss caused by substation equipment failure, which has far-reaching significance for promoting the construction of smart power grid.
RETRACTED: Driving Behavior Based on Big Data Analysis and EEG Data Analysis
Authors: Zhang Tao , Hong Wang 
Published: November 2021

Abstract
In recent years, as the number of automobiles in my country has increased year by year, the number of automobile accidents and casualties and the direct economic losses caused by them are very high. How to improve the level of road safety has become an important research content in the field of transportation. Driving a car is a complex activity involving perception, judgment, decision-making and manipulation, and requires the brain to coordinate and guide the driver’s driving functions. EEG signals can reflect the driver’s psychophysiological state, and then represent the driver’s perception activities. The application of EEG data analysis in driving behavior research explains the mechanism of driving behavior from a new perspective of cognitive neuroscience and brings new solutions to traffic safety problems. Driving behavior research is the main research content in the field of road safety. Recognizing and predicting the state of driving behavior is very important for the development of intelligent driving assistance systems and the improvement of road safety. This paper analyzes the EEG data while driving based on big data analysis. Firstly, the literature research method is used to summarize the EEG data analysis process and the research significance, and then the driver’s EEG data is analyzed and researched through simulated driving experiments. The relationship between its parameters and driving behavior. Experimental results show that the front area and buffer area have a strong correlation with all variables of driving behavior, especially the correlation with acceleration and forward time is about 35%. In addition, compared with other driving behavior variables, time zone has the strongest correlation with speed, about 56%. Approximately 46% of the samples are beta waves that are significantly related to driving behavior. In addition, alpha waves account for about 20% of the total number of samples, while the correlation between delta waves and driving behavior is the weakest, accounting for only about 10% of the total number of samples.
Research on New Rail Transit Power Supply System Scheme
Authors: Jin Song 
Published: November 2021

Abstract
The tourism rail transit project has broad development prospects in China. The new type of rail transit represented by trams and monorails is suitable for the demand of the project of the rail transit, and the traction power supply system is great significance for the normal operation of the new type rail transit. This paper introduces the external power supply, medium-voltage loop network and traction power supply of new type of rail transit project. Through the comparison of technical schemes, the selection for several important engineering schemes in the new rail transit power supply system engineering design are obtained.
A Protection Method for DC Distribution Network with Single Pole Grounding Based on Integrative Two-terminal MMC
Authors: Jiang Hua , Shen Xinglai , Ma Bing 
Published: November 2021

Abstract
Based on the analysis model of unipolar grounding fault occurred in two-terminal MMC DC distribution network, the characteristics of unipolar grounding fault are studied. The similarity characteristics of transient currents between the fault line and the non-fault line are analyzed when the fault is located in the feeder, tie line and bus respectively. According to the characteristics of single-pole grounding fault and the similarity principle, a new single-pole grounding protection method for DC distribution network based on two-terminal MMC is proposed. The simulation results show that the proposed single-pole grounding protection method can accurately determine the single-pole grounding fault at any position of the DC distribution network. The research results can effectively solve the problem of single-pole grounding protection in DC distribution network, and effectively improve the security and reliability of DC distribution network.
Development of Li-ion Battery Bulk Force and Expansion Displacement Test-bench
Authors: Qu Jie , Huang Meihua , Wang Chao 
Published: November 2021

Abstract
In order to test the bulk force and expansion displacement of lithium-ion batteries, it is planned to develop a corresponding test-bench, which is mainly composed of a measurement-control system and a mechanical system. To improve the accuracy of the test data, the coupled thermal-structure simulation of the test system in the mechanical system of the test-bench is carried out to select an optimal mechanical structure of the test system. At the same time, for safe and convenient testing, a monitoring-testing system software was developed to ensure the reliability and safety of data collection. Finally, through the test-bench, the battery expansion displacement-SOC curve and the battery bulk force-SOC curve under different discharge rates were tested, providing a basis for the development of a battery management system coupling temperature-current-voltage-displacement-force.
On the Application of Impeller in Multi Axis CNC Machine Tools
Authors: Ye Weiwen 
Published: November 2021

Abstract
Multi axis CNC machine tool has good linkage processing effect. Through the application of integral impeller in CNC machine tools, to improve the adaptability of CNC machine tools to complex surface processing parts, to improve the accuracy of multi axis CNC machine tools. The first part of this paper introduces the integral impeller and its machining characteristics; the second part introduces the basic NC machining process of integral impeller; the third part discusses the application of impeller in multi axis CNC machine tools from the creation of guide track, the simulation of integral impeller, software processing and generation. The purpose is to provide some reference for the processing and production of integral impeller.
Introduction of additive technologies in PJSC «UEC-Saturn»
Authors: Zavodov S A , Fedoseev D V , Polyakov S A 
Published: November 2021

Abstract
This article describes the process of assimilation of additive technologies at PJSC UEC-Saturn, introduced and advanced domestic materials for various areas of additive production, the results of manufacturing critical parts of the gas turbine engine by this technology.
Hierarchical ensemble learning method in diversified dataset analysis
Authors: Liu Ze yuan , Li Xin long 
Published: November 2021

Abstract
The remarkable advances in ensemble machine learning methods have led to a significant analysis in large data, such as random forest algorithms. However, the algorithms only use the current features during the process of learning, which caused the initial upper accuracy’s limit no matter how well the algorithms are. Moreover, the low classification accuracy happened especially when one type of observation’s proportion is much lower than the other types in training datasets. The aim of the present study is to design a hierarchical classifier which try to extract new features by ensemble machine learning regressors and statistical methods inside the whole machine learning process. In stage 1, all the categorical variables will be characterized by random forest algorithm to create a new variable through regression analysis while the numerical variables left will serve as the sample of factor analysis (FA) process to calculate the factors value of each observation. Then, all the features will be learned by random forest classifier in stage 2. Diversified datasets consist of categorical and numerical variables will be used in the method. The experiment results show that the classification accuracy increased by 8.61%. Meanwhile, it also improves the classification accuracy of observations with low proportion in the training dataset significantly.
Edge Detection technique based on HDR image quality assessment
Authors: Wang Dingxian 
Published: November 2021

Abstract
Image edge detection is one of the major study aspects in current computer image processing field. The quality of the input images is uneven, some have large fuzzy areas, some are underexposed, and the edges of objects in the images are difficult to detect, and the application scenarios of image edge detection are limited. In the view of the above problems, this paper has proposed that by applying High Dynamic Range (HDR) image quality assessment technology, combining multiple images with different exposures into one HDR image with detailed edge information, This technology effectively solved problem of low edge information richness, improved the effectiveness of edge detection algorithms, and contributed to the development of edge detection technology.
The Forward Modeling for Surface-borehole Observation Responses of Polarized Target
Authors: Meng Qingxin , Wang Zhigang , Lu Yao , Hang Guoyuan 
Published: November 2021

Abstract
Surface-hole induced polarization method is a typical deep resource exploration technology, which plays an important role in the mineral survey. The traditional surface-hole induced polarization method is mainly to observe a single polarized secondary field. At present, the time spectrum observation of polarization fields is becoming more and more popular, which greatly enriches the interpretation technology of induced polarization data. In this work, the time spectrum induced polarization method is expounded, the decay polarization fields were numerical calculated and analyzed for the typical 2-dimensional geoelectric model. The results show that for a single polarized target, the time spectrum obtained from different azimuth observation responses are basically the same, which can effectively reflect the time-varying characteristics of the polarized fields. The observation responses of polarized target at different depths can still reflect the time spectrum of decay fields. The main change of the observed response of the polarized target closer to the borehole is the response amplitude. The conclusions and simulations of this study can provide working mode for relevant research and reference for similar work.
Generative Facial Prior for Large-Factor Blind Face Super-Resolution
Authors: Guo Xiaomeng , Yi Li , Zou Hang , Gao Yining 
Published: November 2021

Abstract
Most existing face super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic down sampling). However, these methods suffer a severe performance drop in various unknown degradations in real-world applications. Previous methods usually rely on facial priors, such as facial geometry prior or reference prior, to restore realistic face details. Nevertheless, low-quality inputs cannot provide accurate geometric priors while high-quality references are often unavailable, which limits the use of face super-resolution in real-world scenes. In this work, we propose GPLSR which used the rich priors encapsulated in the pre-trained face GAN network to perform blind face super-resolution. This generative facial priori is introduced into the face super-resolution process through channel squeeze-and-excitation spatial feature transformation layer (SE-SFT), which makes our method achieve a good balance between realness and fidelity. Moreover, GPLSR can restores facial details with single forward pass because of powerful generative facial prior information. Extensive experiment shows that when the magnification factor is 16, this method achieves better performance than existing techniques in both synthetic and real datasets.
A Medical Service Application Based on 3D-CNN and Knowledge Graph
Authors: Ni Jiasheng 
Published: November 2021

Abstract
Remote medical prognosis application is a category of medical tests tool designed to collect patients’ body conditions and offer diagnosis results synchronously. However, most online applications are predicated on a simple chat bot which typically redirect patients to other online medical websites, which undermines the user experience and may prompt useless information for their reference. To tackle these issues, this paper proposed a medical prognosis application with deep learning techniques for a more responsive and intelligent medical prognosis procedure. This application can be break down into three parts-lung cancer detection, a database-supporting medical QA bot and a Hierarchical Bidirectional LSTM model (HBDA). A 3D-CNN model is built for the lung cancer detection, with a sequence of sliced CT images as inputs and outputs a probability scaler for tumor indications. A knowledge graph is applied in the medical QA bot implementation and the HBDA model is designed for semantic segmentation in order to better capture users’ intention in medical questions. For the performance of the medical prognosis, since we have limited computer memory, the 3D-CNN didn’t perform very well on detecting tumor conditions in the CT images with accuracy at around 70%. The knowledge graph-based medical QA bot intelligently recognize the underlying pattern in patients’ question and delivered decent medical response. The HBDA model performs well on distinguish the similarities and disparities between various medical questions, reaching accuracy at 90%. These results shed light for the feasibility of utilizing deep learning techniques such as 3D-CNN, Knowledge Graph, and Hierarchical Bi-directional LSTM to simulate the medical prognosis process.
Finger Vein Recognition System Based on Convolutional Neural Network and Android
Authors: Wang Yangfeng , Chen Tao 
Published: November 2021

Abstract
With the rapid development of science and technology, biotechnology has developed rapidly. Among the many biometric technologies, finger vein technology has the characteristics of vitality, portability, and non-replicability, so it is considered to be the most promising biometric technology. However, the accuracy of finger vein recognition is affected by the collection device, the surrounding temperature and the algorithm. The flaws cannot be applied to real life on a large scale. This paper designs a finger vein recognition system based on convolutional neural network and Android, which mainly includes the following three parts. First, the system hardware includes the design of the acquisition device, the selection of the core development board and the display screen. Second, the design of the entire system software architecture is based on the MVVM architecture, which ensures low coupling of the program and is easy for later expansion and maintenance. The software includes collection function, recognition function and administrator function. Finally, a lightweight neural network is proposed for finger vein feature extraction, and proposed a storage method based on MMKV to meet the real-time performance of the system.
Automated band selection for Bayesian FFT modal identification Based on RetinaNet
Authors: Wang Chen , Xue Zhilin , Su Yipeng , Li Binbin 
Published: November 2021

Abstract
Bayesian FFT algorithm is a popular method to identify modal parameters, e.g., modal frequencies, damping ratios, and mode shapes, of civil structures under operational conditions. It is efficient and provides the identification uncertainty in terms of posterior distribution. However, in utilizing the Bayesian FFT algorithm, it is tedious to manually select frequency bands and initial frequencies. This step requires professional knowledge and costs most of time, which prevents the automation of Bayesian FFT algorithm. Regarding the band selection as an object detection problem, we design a band selection network based on the RetinaNet to automatically select frequency bands and a peak prediction network to predict the initial frequencies. The designed networks are trained using the singular value spectrum of measured ambient vibration data and verified by various data sets. It can achieve the human accuracy with much less operation time, and thus provides a corner stone for the automation of Bayesian FFT algorithm.