bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of Schottky-contact process on 4H-SiC Junction Barrier Schottky (JBS) Diodes
Authors: Li Qingling , Zhu Tao , Li Jialing , Yan Hailiang 
Published: December 2021

Abstract
SiC Junction Barrier Schottky (JBS) Rectifier is a kind of unipolar power diode with low threshold voltage and high reverse blocking voltage. And the Schottky barrier Φ
BN is a main technology parameter, which could greatly affect the forward conduction power and reverse leakage current in the JBS rectifiers. Therefore, it is necessary to balance the influence of Φ
BN on the electrical characteristics of JBS rectifiers. In this paper, physical properties at the metal-semiconductor at the Schottky-contact could be optimized by the improvement of Schottky-contact process. And this optimization could significantly decrease Φ
BN to reduce the on-state voltage drop V
F and minimize negative impact on its reverse characteristics. After the completion of Silicon carbide JBS diodes, the static parameter electrical test was carried out on the wafer by using Keysight B1505A Power Device Analyzer/Curve Tracer. The test results show that the Schottky barrier height Φ
BN of JBS Schottky rectifier manufactured by the modified Schottky foundation technology decreased from 1.19eV to 0.99eV and I
R increased from 1.08μA to 3.73μA (reverse blocking voltage V
R=1200V). It indicated that the power consumption of Schottky barrier junction in JBS rectifiers could be significantly reduced by about 25%, and I
R could effectively be limited to less than 10μA.
Research on Internet of Things Technology for Intelligent Three-Dimensional Online Monitoring System of Marine Ranch
Authors: Chen Kuo , Chen Ke , Wang Lei 
Published: December 2021

Abstract
The online monitoring network of marine ranching ecological environment is one of the important development directions of marine ranching construction. Traditional marine monitoring systems are large in size, high in power consumption, and expensive. The investment in pasture construction cannot achieve a large amount of investment to form a monitoring network. There is an urgent need for a low-cost marine monitoring method that can afford a large area and high spatial density. This problem cannot be realized in traditional technology. Based on MEMS technology, this paper develops low-cost monitoring small floats that can be deployed in a large number of “swarm bees”, with high spatial and temporal resolution, and take into account environmental and biological population characteristics monitoring. Each small float is connected to a multi-node sensor chain and video monitoring node. The low-cost surface and underwater wireless transmission and wireless communication means are used for networking observation, which proposes a brand-new solution for the construction of the intelligent three-dimensional monitoring of the marine pasture environment.
Research on the Extraction of Image Edge Information in Convolutional Neural Networks
Authors: Zou Guanru , Luo Yulin , Feng Zefeng 
Published: December 2021

Abstract
Convolutional neural network is an important neural network model in deep learning and a common algorithm in computer vision problems. From the perspective of practical application scenarios, this paper studies whether padding in convolutional neural network convolution layer weakens the image edge information. In order to eliminate the background factor, this paper select MNIST dataset as the research object, move the 0-9 digital image to the specified image edge by clearing the white area pixels in the specified direction, and use OpenCV to realize bilinear interpolation to scale the image to ensure that the image dimension is 28×28. The convolution neural network is built to train the original dataset and the processed dataset, and the accuracy rates are 0.9892 and 0.1082 respectively. In the comparative experiment, padding cannot solve the problem of weakening the image edge weight well. In the actual digital recognition scene, it is necessary to consider whether the core recognition area in the input image is at the edge of the image.
Innovative design of exhaust enthalpy algorithm for small turbine
Authors: Zhu Lingkai , Wang Qian , Gao Song , Zhong Ziwei , Shang Panfeng , Guo Junshan , Han Yue , Zheng Wei 
Published: December 2021

Abstract
This paper presents a method to determine the exhaust enthalpy of small steam turbine. Starting from the feed water pump group, the feed water pump and small steam turbine are studied as a whole. Based on the thermodynamic method, the efficiency of the feed water pump is obtained separately, and then the efficiency and exhaust enthalpy of the small steam turbine are deduced. This method only needs to measure the inlet and outlet pressure, temperature, feed water flow of feed water pump, inlet steam pressure, temperature and flow of small turbine. It has the advantages of less measurement parameters, low measurement cost and small measurement error. It provides a reliable basis for a comprehensive understanding of the performance of small steam turbine and guiding its economic and safe operation.
Policy-based monocular vision autonomous quadrotor obstacle avoidance method
Authors: Zheng Baojin , Guo Xiao , Ou Jiajun 
Published: December 2021

Abstract
Aiming at the obstacle avoidance control problem of small quadrotor, a method of quadrotor obstacle avoidance based on reinforcement learning is proposed. The proposed method can make training converge quickly and has good environmental robustness. The proposed methods include: (1) a framework adopts perception module and decision module to improve the generalization ability of the obstacle avoidance model; (2) An Actor-Critic framework-based Proximal Policy Optimization (PPO) algorithm to provide quadrotor with policy-based decision-making capabilities; The experimental simulation results show that the strategy-based framework converges quickly and has a high success rate, the training time is much lower than that of the value-based framework. The monocular vision observation ability is limited, which leads to deviations between local observation and global state, So LSTM layer is usually added to increase model performance. Policy -based decision can have a good obstacle avoidance effect without adding the LSTM layer, and have good generalization ability after short relearning after changing.
Intelligent Calculation Model and Algorithm Optimization of Optimal Operation of Integrated Energy System
Authors: Wang Kun , Yang Guochao , Cheng Baohua , Gan Zhiyong , Qi Yan , Wang Sen , Bai Xinyu , Bian Jiang 
Published: December 2021

Abstract
This paper analyzes the energy saving effect and optimal operation of the integrated park type energy system. The park type integrated energy system includes photovoltaic power generation system, ground source heat pump system, solar air conditioning system, regenerative electric boiler system and solar hot water system and so on. The energy saving benefit calculation model of each subsystem is established, and according to the operation history data of each subsystem, the calculation results of each subsystem are analyzed. It shows that: The energy saving income of photovoltaic power generation system is 734,200 yuan/year. The main revenue comes from the second and third quarters, during which it should pay attention to the maintenance of photovoltaic modules to ensure the maximum energy saving benefit. The energy saving income of ground source heat pump system in summer is 155,900 yuan/year, and the energy saving effect is not significant. It can cooperate with traditional units for cooling in summer. The heating in winter in the park is provided by ground source heat pump system, regenerative electric boiler system and solar energy air conditioning system. Its energy saving benefit is 3.995 million yuan/year which is the main source of energy saving income in the park, with remarkable energy saving effect.
Algorithm design and analysis of fire rescue problems
Authors: Fu Mengshuang 
Published: December 2021

AbstractWith the rapid economic development, the urban space environment is becoming more and more complex, various accidents and disasters occur frequently, and safety risks are increasing. The rescue tasks involved in the fire brigade are showing a trend of diversification and complexity. The fire rescue team always puts the people first and insists on serving the people wholeheartedly. It is the guardian of maintaining social stability in our country and safeguarding the health and safety of people’s lives and property and various disaster affairs. The society needs the participation in the fire rescue team. Aiming at the fire rescue problem, this paper uses the fire rescue call data onto 2016 to 2019 to predict the number of fire rescues / rescued calls based on the difference equation to improve the rescue efficiency of the fire brigade. Taking into account the impact on the domestic epidemiced in 2020 on people’s lives, the adjustment value was introduced to adjust part of the alarm data onto 2020 to ensure the accuracy and reliability of the data. Finally, the second-order difference equation is used to predict the alarm data onto 2021 through the least square method, which verifies the accuracy of the model.
Research on Image Classification Algorithm Based on Convolutional Neural Network
Authors: Luo Lihua 
Published: December 2021

Abstract
Nowadays, we are in the information age. Pictures carry a lot of information and play an indispensable role. For a large number of images, it is very important to find useful image information within the effective time. Therefore, the excellent performance of the image classification algorithm has certain influence factors on the result of image classification. Image classification is to input an image, and then use a certain classification algorithm to determine the category of the image. The main process of image classification: image preprocessing, image feature extraction and classifier design. Compared with the manual feature extraction of traditional machine learning, the convolutional neural network under the deep learning model can automatically extract local features and share weights. Compared with traditional machine learning algorithms, the image classification effect is better. This paper focuses on the study of image classification algorithms based on convolutional neural networks, and at the same time compares and analyzes deep belief network algorithms, and summarizes the application characteristics of different algorithms.
A Scrambled Image Blind Evaluation Method Based on Space and Frequency Domain
Authors: Wang Congli , Li Xiong 
Published: December 2021

Abstract
Most of the image scrambling degree evaluation algorithms rely on the statistical features of the original image, which cannot achieve blind evaluation. Based on the uniform distribution model of the statistical characteristics of ideal scrambled images, a blind evaluation algorithm of image scrambling degree combining space and frequency domain is proposed in this paper. In the space domain, the uniform distribution characteristics of the gray histogram of the ideal scrambled image are used, and the uniform distribution characteristics of the Discrete Fourier Transform (DFT) spectrogram in the frequency domain are combined with the gray correlation analysis theory. The two are weighted to realize the space and frequency domain evaluation of the scrambled image performance. The experimental results indicate that the evaluation algorithm in this paper can consider the performance evaluation of both pixel value and pixel position dislocation, avoiding the disadvantage of ineffective spatial domain evaluation when only the pixel position scrambling is performed. It has very sensitive to the histogram distribution and frequency domain features of encrypted images, and has good agreement with Human Visual System (HVS). The original image is completely independent, and it enables blind evaluation objectively.
Application study of UAV path planning based on the balanced search factor artificial bee colony algorithm
Authors: Hao Wenlong , Luo Bo , Zhang Zhiyuan 
Published: December 2021

Abstract
In this paper, aiming at the shortcomings of slow convergence speed and weak local search ability of traditional artificial bee colony algorithm in path planning, an artificial bee colony algorithm based on balanced search factor is proposed for UAV path planning. Using a search strategy based on balanced search factor, the depth search is carried out while maintaining a certain population diversity. The global search ability and local development ability are balanced, the average accuracy of path planning is improved, the robustness of path planning is enhanced, and the ability to obtain better path solutions is improved.
Research on application of single index membership function based on power coefficient method
Authors: Liu Na 
Published: December 2021

Abstract
The power coefficient method can determine the satisfactory value and the unallowable value of the index, quantify multiple targets, and then determine the power coefficient value of each target. Combining the distribution characteristics of the four types of membership functions, this paper innovatively proposes to solve the regional distribution problem of single-index membership functions by means of an improved efficiency coefficient method, and combined with empirical research, the application of this mathematical method in practical engineering.
Computer Mathematical Statistics and Comparative Analysis of Meteorological Conditions in the Bohai Rim Region through Big Data Digitization
Authors: Jin Tiantian , Li Yang , Du Jianshuang , Wang Lei , Mao Zhizheng 
Published: December 2021

Abstract
Based on the data of environmental monitoring stations and meteorological stations in typical city in the Bohai Rim region in June 2021 and June 2020. The air quality in June 2021 was better than that in the same period in 2020. Ozone (O3) was the primary pollutant in June 2021 and June 2020. The overall meteorological conditions in May 2021 were relatively favorable. In June 2021, the number of light wind days and wind speed had no significant impact on air quality compared with the same period last year. The dominant wind direction in June 2021 was southeast wind, which was not conducive to the increase of ozone concentration compared with the southwest wind frequency in the same period in 2020; the relative humidity in June 2021 was slightly higher year-on-year, which was slightly conducive to the hygroscopic growth of pollution; the static stability index in June 2021 was lower year-on-year, and the weather conditions for comprehensive pollution diffusion were preferred year-on-year; the height of the mixed layer in June 2021 was lower year-on-year, the vertical diffusion weather conditions were deviated year-on-year; the average temperature, sunshine hours and total radiation in June 2021 were all low, which was not conducive to the generation of ozone.
Research on the computer intelligent recognition of electric appliance by Yolo V5 algorithm
Authors: Zhang Xiaolong , Qian Bin , Si Haitao , Zeng Liuying , Wang Hui 
Published: December 2021

Abstract
In the process of power operation, the correct identification of tools can lay a foundation for the detection of violations in power operation. In order to realize the recognition of power instruments, based on the current Yolo V5 algorithm, a detection algorithm for power instruments is proposed by improving Yolo V5 algorithm. Firstly, the model of Yolo V5 algorithm is introduced. Then the establishment of the power tool database and the process of model training are analysed. Finally, the test results are analysed and evaluated. The models generated after training were accelerated by TensorRT and then deployed on Jetson Xavier NX.
CFD-Based Wind Field Correction Method for Terrain Wind Tunnel Tests
Authors: Zhou Qi , Zhu Yuxiang , Wang Yu , Han Jiceng 
Published: December 2021

Abstract
At present, the wind tunnel test results will have certain deviation and distortion when the wind tunnel test is conducted on certain mountainous terrain with complex local terrain and large variation of wind field characteristics due to the accuracy range of the measuring instruments used in wind tunnel test. In order to correct and obtain correct wind tunnel test results, the wind tunnel tests and numerical simulations were conducted on a super-large bridge in the mountainous area of Southwest China, and the wind parameters of the wind field at the bridge site were obtained. The CFD results were compared with the wind tunnel test results to confirm the credibility of the CFD results; a method was proposed to correct the deviated wind tunnel test data based on the CFD simulation results; the deviated wind tunnel test data were corrected and predicted with the above method, and a more satisfactory correction result was obtained.
Study on Full-scale Measurements of Wind Field Characteristics of Coastal Complex Mountainous Terrains
Authors: Shao Shuai , Luo Kewei , Zhang Hongjie , Li Yangsen , Fu Benzhao , Lin Ruizong 
Published: December 2021

Abstract
Based on full-scale wind field measurements of coastal complex mountainous terrains, data of fluctuating wind velocities at the height of 30m for four sites, including mountaintop and hillsides, are obtained. The wind load characteristics of mean wind velocities, wind directions, turbulence intensities and speed-up ratios of wind velocities are comprehensively examined. Results show that the maximum mean wind velocity at the mountaintop site is 12.4 m/s. The probability density distributions of mean wind velocities for the four measurement sites can be well represented by the Weibull probability model. The predominant wind directions are around the northeast and southwest. The longitudinal, lateral and vertical turbulence intensities decrease with the increase of mean wind velocities. The turbulence intensities for the mountaintop site are as many as 0.13,0.12 and 0.089 under maximum wind velocities, respectively for the previously mentioned three directions. The speed-up ratios of wind velocities between mountaintop and hillside sites are reduced, as the wind velocities increase. However, in cases of intensive wind with mean wind velocities larger than 8 m/s, the speed-up effects of wind velocities can also appear. The maximum speed-up ratio can reach 1.17.
Computer Mathematical Statistics and Analysis on Automobile HFCs Reduction Path Using Big Data Technology
Authors: Ren Jiabao , Liu Xuefeng , Wang Chaoqian , Wang Lei 
Published: December 2021

Abstract
China has submitted an application to join the Kigali amendment to the Montreal Protocol. As the second largest hydrofluorocarbons (HFCs) consumer industry in China, the automotive industry is facing enormous pressure to reduce HFCs. This paper analyzes the current situation of HFCs use in the domestic automotive industry, and makes a simulation prediction of HFCs use data in the automotive industry from 2024 to 2030, analyze and evaluate the advantages and disadvantages of different technical routes. The relevant contents of this paper will provide reference for the formulation of HFCs reduction policy.
Intelligent Numerical Computing Methods for Determining the Concentration of Citric Acid by Accurate Measurement and Calculation
Authors: Fu Yueyang 
Published: December 2021

Abstract
According to the Bronsted-Lowry theory, an acid is a proton donor, and a base is a proton acceptor. An acid-base reaction involves the proton transfer between chemicals, where a base containing hydroxide ion (OH-) accepts a proton (H+) from an acidic solution to form water (Khan,2016). In the above equation, HCl as an acid donates one H+ ion, and NaOH as a base accepts the proton to form one water molecule (H2O). So, a proton from the acid is transferred to the anion of the base. Then, the metal cation (Na+) and the conjugate base anion (Cl-) form the salt NaCl.
Study and application of computer information big data in basketball vision system using high-definition camera motion data capture
Authors: Wei Jingwei 
Published: December 2021

Abstract
Contemporary computer big data technology is developing rapidly, and it has played a role in promoting the intelligent development of sports. Based on this research background, the thesis revolves around the application of computer big data artificial intelligence in the field of basketball training. The study found that computerized big data in basketball training and teaching is mainly reflected in the following aspects: high-definition camera motion data capture, player analysis to obtain assisted training system, etc. The final paper uses a basketball vision system as a case study to analyze specific applications of big data for computer information.
Real-time vehicle detection and computer intelligent recognition through improved YOLOv4
Authors: Ni Zhenhao , Liu Tingna , Li Ke , Bai Yongqiang , Zhu Zhongjie 
Published: December 2021

Abstract
Vehicle detection is one of the key techniques of intelligent transportation system with high requirements for real-time and accuracy. To better balance the requirements, a vehicle detection algorithm based on the You Only Look Once (YOLO) v4 is proposed in this paper. On the one hand, the improved depthwise separable convolution is adopted to ensure the real-time performance. On the other hand, a novel feature fusion network is designed to gather more original feature information of different depth network layer. Experimental results show that the proposed algorithm can reduce the detection time by half while ensuring the accuracy, compared with the pristine YOLOv4.
Research on Automated Information System of Non-sense Attendance Using Face Recognition and Large Database
Authors: Wang Taiyou , Chen Litiao 
Published: December 2021

Abstract
In order to meet the needs of today’s large-scale enterprise human resource management attendance business, this paper designs an embedded face recognition attendance system, and designs and analyses the implementation method of the system in detail. The system consists of two parts: face-swiping attendance and attendance management. Among them, face-swiping time attendance uses the Face Boxes model to detect faces, and the Face Net model to identify the person who punches in. Tests have found that the system is simple to operate, has high recognition accuracy, and has a certain practical value.
Research of Metric Learning-Based Method for Person Re-Identification by Intelligent Computer Vision Technology
Authors: Rao Qiyu 
Published: December 2021

Abstract
Person re-identification technology aims to establish an efficient metric model for similarity distance measurement of pedestrian images. Candidate images captured by different camera views are ranked according to their similarities to the target individual. However, the metric learning-based method, which is commonly used in similarity measurement, often failed in person re-identification tasks due to the drastic variations in appearance. The main reason for its low identification accuracy is that the metric learning method is over-fitting to the training data. Several types of metric learning methods which differ from each other by the distribution of sample pairs were summarized in this article for analysing and easing the metric learning methods’ over-fitting problem. Three different metric learning methods were tested on the VIPeR dataset. The distributions of the distance of the positive/negative training/test pairs are displayed to demonstrate the over-fitting problem. Then, a new metric model was proposed by combining the thoughts of binary classification and multi-class classification. Related verification experiments were conducted on VIPeR dataset. Besides, the semi-supervised metric learning approach was introduced to alleviate the over-fitting problem. The experimental results reflect gap between training pairs and test pairs in the metric subspace. Therefore, reducing the difference between training data and test data is a promising way to improve the identification accuracy of metric learning method.