bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection of Surface Defects of Steel Plate Based on ViT
Authors: Fan Jiangling , Ling Xufeng , Liang Jingxin 
Published: August 2021

Abstract
A self-attention-based method termed as Vision Transformer (ViT) is applied to efficiently detect the Surface Defects of Steel Plate. The defect image is divided to N*N patches, each of which corresponds to a word, and the whole image data is used as a sentence or paragraph in NPL. A ViT framework is constructed by a learnable module with sequence length of L and 12 multi-head attention layers. We train the proposed model on the surface defects dataset. The experiment results show empirically that ViT has superior performance compared to alternative approaches.
Dynamics Analysis of Rolling Bearings for Motor of a Mobile Power Station
Authors: Li Xiaoquan , Ju Rui , Cai Shulin 
Published: August 2021

Abstract
The electric motor is an important part of a mobile power station. Under the complex operating conditions, the stiffness of the rolling bearing exhibits strong time-varying characteristics and non-linear characteristics, which is the one of the main sources for the system non-linearity. Taking the angular contact bearing as the research object, the coupled dynamic model of the bearing-rotor system is established to calculate the time-varying stiffness. The bifurcation law exhibited by the system can be attributed to the excitation of the non-linear bearing force. The numerical calculation method is used to study the motion state bifurcation law and dynamic frequency response characteristics under the rated working condition of the motor. Considering bearing damping, clearance and radial load, the bifurcation and time domain diagrams of different parameters, displacements and rotation speeds in different directions are studied respectively. The results show that when the parameters change, the system will experience chaos, quasi-period and period-doubling motion state; Damping suppresses vibration very clearly, while changes in bearing clearance and radial load significantly cause the system’s vibration amplitude to increase, and radial load changes accelerate system vibration. A reasonable bearing is of great significance for motor the design of a mobile power station.
Finite Element Simulation Analysis of Fluid Pipeline Leakage
Authors: Su Pan , Wu Jiechang , Liu Haisong , Sun Xiaolong , Feng Xuejiao 
Published: August 2021

Abstract
Due to the harsh working environment and the inconvenient maintenance, leakages of warship pipeline often occur. When the pipeline leaks, the fluid flow and pressure in the pipeline will change. The pressure fluctuation of the fluid is one of the main sources of abnormal vibration and noise in the pipeline system. In order to locate the leaking part of the pipeline effectively, it is necessary to use sensors to collect and capture these changing parameters to facilitate real-time monitoring of the pipeline system. In this paper, ANSYS finite element analysis software is used to establish the geometric model of “I” type combined pipeline, and the pressure variation characteristics under different leakage conditions are obtained through simulation.
Research on Dynamic Compression Testing of Silicone Rubber under Different Temperatures
Authors: Zhu Xintao , Li Wenbin 
Published: August 2021

Abstract
For study the mechanical characteristic of silicone rubber under impact loading, the Uniaxial compression tests of silicone rubber under different strain rates (1 × 103/s∼6 × 103/s) and temperatures (233K∼373K) were carried out with test device using the split Hopkinson pressure bar which has a controllers of temperature. The dynamic stress-strain curves and related dynamic mechanical parameters of silicone rubber were generated. The results show that the dynamic compressive strength of silicone rubber at different temperatures has the characteristics of the strain rate strengthening, which increases along with the raise in strain rate. Under dynamic condition, the stress-strain curve at 263K shows the characteristic of transition to “glass state”.
Measurement and Analysis of Optical Properties of Polymer Films with Triangular Pyramid Optical Functional Texture
Authors: Feng Wei , Xu Zhilong , Wu Haifeng , Fang Fang , Wang Su 
Published: August 2021

Abstract
A poly (vinyl chloride) film with triangular pyramid array structure was designed and fabricated. In this paper, a plane to plane hot embossing technique is used to fabricate a triangular pyramid antireflection film on the surface of single crystal silicon solar cells. By testing the optical properties of optical functional textured films, it is found that the reflectivity of triangular pyramid textured films obtained by hot embossing is significantly lower than that of smooth flat films; The absorption and antireflection of high light on the film surface are obvious; The photoelectric conversion efficiency of the solar cell is further improved. Triangular pyramid optical functional structure has great potential in improving the performance of silicon solar cells, which can be applied to different types of solar cells. These improvements are of great significance to the commercialization of silicon solar cell devices.
Polarization Field on Edge States of Single-layered MoS2
Authors: Hu Gongwei , Dan Minjiang , Zhang Yan 
Published: August 2021

Abstract
Operating quantum materials through the application of strong electric field holds great promise for the development of new-generation electronic and photonic technologies with currently inaccessible functionalities. The conventional way of applying gate voltage to produce sufficiently high electric field faces challenge in micro-nano level. Here, we explore polarization field of two-dimensional piezoelectric semiconductors under externally applied strain. The results suggest that the intensity of polarization field can exceed 10 MV/cm near the boundaries of single-layered MoS2 ribbon. Such polarization field can trigger the metallicity-to-semiconductor (M-S) phase transition of one-dimensional edge states, and even lead to metallic bulk states. Similar phase transition can also be driven by a pure external electric field but the required intensity is in the order of MV/cm. Electric field driving the semiconducting phase of edge states presents the sensitive dependence of ribbon width but relatively robust for polarization field. This study opens a new avenue to manipulate quantum materials by high polarization field.
Solution of a Plane Six Setting Sites Problem
Authors: Zhu Yuyang 
Published: August 2021

Abstract
We consider the optimal setting of n stations and solve the problem of n = 6 in the plane. Let S ⸦ E
2 be a set consisting of n points A
1, A
2, …, An
, d(Ai, Aj
) stands for the distance between Ai
and Aj
, and

σ
(
S
)
=

∑

1
≤
i
<
j
≤
n

d

(

A

i

,

A

j

)

,

d
(
S
)
=

min

1
≤
i
≠
j
≤
n

{

d

(

A

i

,

A

j

)

}

,

μ
(
2
,
n
)
=

σ
(
S
)

d
(
S
)

(
S
⊂
P
,

|

S

|

=
n
)

, inf

μ
(
2
,
n
)
=
min

{

σ
(
S
)

d
(
S
)

|

S
⊂
P
,

|

S
∣=
n

}

. In the paper, the results for

inf

μ
(
2
,
6
)
=
13
+
4

3

are obtained by several analytical methods, such as classifying, regional control and evaluating the boundary extremum.
The Mechanical Effect of Pit Excavation with Protection on Adjacent Pipelines
Authors: Wen Hao , Li Kaihong , Jia Yanjie , Zhou Daijun , Ma Jun 
Published: August 2021

Abstract
The normal operation of pipelines will be affected by the surrounding environment. As an outstanding problem, pit excavation with protection on adjacent pipelines has been paid more and more attention in urban infrastructure. Adding retaining structure in soft clay area can effectively reduce the influence of pit excavation on buried pipeline. In this paper, a3D simulation model of pipeline-soil-wall coupling is established. And the effect parameters of soil, pipeline and envelope on pipeline stress distribution, axial strain and pipeline displacement are analysed. It is found that the stress of pipeline concentrated on the middle part of the pipeline after excavation with retaining foundation pit. The larger the elastic modulus and cohesive force of soil, the smaller the deformation of pipeline, while the change of Poisson’s ratio is opposite. With the increase of pipeline wall thickness and distance between pipeline and diaphragm wall, the Poisson’s ratio is decreased. The internal pressure of the pipeline has hardly affected by the deformation of pipeline, but the deformation of pipeline is increased with the increase of the depth of the foundation pit. The thickness and modulus of elasticity of diaphragm wall have the same change. They are greater with the smaller of deformation capacity of pipeline. These results provide theoretical guidance for the research of foundation pit and pipeline, and provide reference basis for engineering construction.
Quantitative Analysis of the Effect of Pier Turbulence on Ship Yaw Moment
Authors: Tan Zhirong , Chen Wei , Chen Bin , Wang Yang 
Published: August 2021

Abstract
In order to quantitatively analyze the action mechanism of the pier flow field on the adjacent bow, Fluent was used to simulate the flow field variation characteristics of a single cylinder pier without ship passing. Secondly, the DES model and the dynamic grid technology are used to simulate the turbulence and the change of the ship’s bow moment when the ship passes through a single cylinder pier under different transverse distance parameters. The numerical simulation results under multiple working conditions show that when the ship approaches the pier, the bow moment is significantly affected by the flow field of the pier. With the increase of the distance between the pier and the ship, the influence of the pier flow field on the ship’s bow moment is gradually strengthened to gradually weakened.
A Moving Object Detection and Predictive Control Algorithm Based on Deep Learning
Authors: He Yan , Li Xiuxian , Nie Hongfei 
Published: August 2021

Abstract
In traffic scene, a low detection rate of dynamic target is caused by interference features of background area and fast speed of detected moving target. In this paper, an optimal target detection and prediction algorithm is proposed. Firstly, the algorithm of real-time motion parameters (speed, direction, etc.) detection of moving objects (vehicles) based on the depth theory is studied. The original prediction problem is transformed into the problem of automatic updating rate of uncertain parameters and the problem of minimizing the maximum path distance. Secondly, by on-line estimation of the automatic update rate, the proposed trajectory generated is guaranteed to be the minimum length. The allocation strategy minimizes the maximum distance traveled in the collision free. Then, the stability of the estimation error system is guaranteed base on deep learning algorithm. Finally, the simulation shows that the proposed algorithm can be used for moving target detection and path prediction accurately. The performance of the control system is improved.
An analytical method for parameter identification based on datasheet values
Authors: Lu Hao , Zhang Yunpeng , Hao Peng 
Published: August 2021

Abstract
A new method for extracting five parameters of single diode is presented in this paper. Through the further derivation and simplification of the formula, five parameters under a single condition can be obtained analytically by using only three key operating points (open circuit voltage point (OC), short circuit current point (SC), maximum power point (MPP)) and datasheet values, which can achieve high accuracy in only two iterations. The accuracy and effectiveness of proposed method have been validated by experimental data under different environmental conditions.
Data Preprocessing and Quality Evaluation for Building the Power Grid Supervision Knowledge Graph
Authors: Zhang Xinjie , Wang Jian , Guo Lingxu , Wang Xin , Wang Yao , Xu Lei 
Published: August 2021

Abstract
In the process of constructing the power grid supervision knowledge graph, it is necessary to sort out and integrate structured and unstructured multi-source data for knowledge extraction and reasoning. In order to solve the quality problems of multi-source data redundancy and errors, this paper proposes a multi-source data quality evaluation system to achieve multi-dimensional quality evaluation of power grid supervision data such as maintenance, defect, measurement, alarm and oil chromatography. Data preprocessing methods are firstly adopted for text and numerical data separately to complete data noise reduction, data filtering, data filling, etc. According to the actual calculation example, the practicability and effectiveness of the data preprocessing method and data quality evaluation system are verified. Finally, the data value of the power grid supervision knowledge graph is significantly improved, which is helpful to comprehensively improve the equipment state perception ability.
Improved Aspect-level Sentiment Analysis Method based on Multi-Head Attention Mechanism
Authors: Yu Kaixuan , Li Yachao , Zhang Dongsheng 
Published: August 2021

Abstract
The purpose of the aspect-level sentiment analysis task is to analyze the sentiment orientation expressed by different aspects in the text. It has more fine-grained sentiment evaluation objects and is more in line with actual needs. Therefore, it has received extensive attention in recent years. At present, the model that combines the attention mechanism with the Recurrent Neural Network and its variants has gradually become the main method to solve aspect level sentiment analysis tasks. However, this type of method is limited by the deficiencies of the RNN itself, the training time is too long and the dependence between words decreases with the increase of distance. Even if the attention mechanism is added, the above problems will still exist. Therefore, this paper introduces the multi-head attention mechanism into the GCAE model (Gated Convolutional network with Aspect Embedding), and then proposes the GCAE-MHA model (Improved GCAE model based on Multi-Head Attention mechanism). This model models the context and specific aspects of the text sequence at the same time, and learns the interactive relationship between them. And finally uses the two together as the basis for sentiment classification. At the same time, in order to make up for the problem that the convolutional neural network can only extract the dependencies between local words, the GCAE-MHA model also uses the Dilated Convolutional Neural Network to replace it, which can extract the semantic information between long-distance words, and by setting different dilation rate to obtain a richer semantic feature. Finally, experiments are carried out on the SemEval2014 dataset and Twitter dataset. The experimental results show that the GCAE-MHA model can effectively improve the effect of aspect level sentiment analysis while ensuring the simplicity of the model network structure.
Applicability evaluation of blockchain technology in distributed power transaction
Authors: Zhang Han , Xue Song , Ma Li , Wu Zechen , Zhang Gao , Cong Pengwei 
Published: August 2021

Abstract
With the proposal of peaking carbon dioxide emissions and carbon neutrality target, distributed generation, especially distributed renewable energy generation, will play a more and more important role in the future power system and become the main source of power supply. The rapid development of blockchain technology provides more imagination and development opportunities for distributed power transaction. Its security, decentralization and rapidity are very suitable for distributed power transaction. This paper analyzes the key factors affecting the level of distributed power transaction, establishes the applicability model of various transaction modes for distributed transaction, analyzes the applicability of blockchain technology for distributed transaction, and puts forward the improvement direction of blockchain technology in distributed power transaction in the future.
Error analysis in a D(H)/Pd gas-loading system when using isothermal calorimetry
Authors: Wang Xingye , Shen Bingjun , Jin Lihong , Zhou Dan , Tian Jian 
Published: August 2021

Abstract
In the field of low energy nuclear reaction, isothermal calorimetry is often used to calculate the excess heat power released in the system. Due to the structure of the reaction chamber and the differences of the thermal conductivities of the gases used in the calibration and triggering experiments, the chamber will have different temperature gradient or distribution which leads to errors when calculating the excess heat power using isothermal calorimetry. Different gas pressure also has an influence on the heat transfer.
Construction and experiment of phenotyping system based on field wheat
Authors: Hecang ZANG , Jie ZHANG , Qing ZHAO , Qiaoli ZHAO , Guoqing ZHENG , Guoqiang LI 
Published: August 2021

Abstract
In response to the actual needs of informatization of the collectors engaged in the whole process of field wheat production, this paper constructed a phenotyping system based on field wheat, which was of great significance to improve the efficiency of data collection and reduce the labor intensity of the collectors. Based on the collection and management of phenotyping traits data from sowing to harvesting, combined with barcode recognition, database technology and mobile terminal APP technology, a field wheat phenotyping system was designed and developed, and the data was compared with collection efficiency by manual collection methods. The system was composed of the phenotyping trait collection system APP and the phenotype trait management system Web. The APP development architecture used client/server (C/S), the development language used Java, and the phenotyping traits were collected. As the core, upload collected data to the Web to realize data exchange and sharing between APP and Web; Web development architecture used browser/server (B/S) to realize the distribution of test tasks in the wheat production process, data management, report generation center, and statistical analysis. Compared with the manual data collection method, the application of APP could improve the data collection efficiency by 72%. This system had been realized the rapid collection, efficient management and automatic analysis of wheat phenotyping traits, which ensured the standardization of data collection and management, and improved the efficiency of data collection and utilization. The system could support the phenotyping collection and management of other crops, and it is also suitable for field phenotyping surveys in various industries such as fruits, vegetables and so on.
News text classification based on hybrid model of Bidirectional Encoder Representation from Transformers and Convolutional Neural Network
Authors: Wei Fan , Li Fan 
Published: August 2021

Abstract
At present, with the rapid development of Internet technology, text data show massive characteristics. Network media and new media platforms have become an important part of journalism and communication, and network news has become one of the important sources of information. In order to meet the needs of online news reading users and improve the efficiency of personalized news recommendation by content distribution platform, it is urgent to effectively manage and utilize online news. Based on this, this paper studies and implements the algorithm for news text classification, and the goal is to build a classification model for news text with higher classification accuracy. In this paper, BERT and CNN algorithm are combined to classify news texts. The core idea is to send BERT as the embedding layer into the CNN model. The experimental results show that this method is better than the simple BERT model or the simple CNN model.
Task Scheduling Method for Embedded System Composed of All Linear Tasks
Authors: Chen Xing , Li Hongkun , Chen Yugang 
Published: August 2021

Abstract
There are still a large number of small and medium-sized embedded software applications that do not use operating system due to various conditions, for which a lot of time and effort are spent to build appropriate software structure in doing development. This paper presents a concept of linear task and a scheduling method based on the linear task, which can all use C language to realize the fully distributed dynamic multi-task scheduling of embedded single machine system based on the basic foreground-background system structure. On this basis, the system structure of the embedded software application without operating system can be quickly built and the subsequent application function development can be completed. Through the example implementation and simulation verification, it is proved that this method has good hardware independence, low CPU occupancy, small data and code space, flexible application support, comprehensive function, easy time coordination and simple real-time control. The application of this method to build an embedded system composed of all linear tasks can provide an effective foundation for dealing with the increasingly complex application requirements and limited development cycle.
Anti-follower jamming performance analysis of dual sequence frequency hopping in Gaussian white noise channel
Authors: Meng Qingbin , Quan Houde 
Published: August 2021

Abstract
With the increasingly severe wireless communication confrontation, the traditional conventional frequency hopping (CFH) system has poor performance in anti-follower jamming, so a new dual frequency hopping mode is proposed from the perspective of frequency hopping principle. Firstly, the communication model and anti-jamming principle of the dual sequence frequency hopping (DSFH) system are introduced. Secondly, the typical follower jamming model and its parameters in communication are summarized, and the anti-follower jamming performance of different parameters of the system under follower jamming is analyzed. Finally, the CFH and DSFH system are compared. Bit error rate performance under different parameter conditions. The results show that under the AWGN channel, the anti-follower jamming performance of the DSFH system is better than that of CFH, and the value of the bit error rate is lower than that of CFH.
Characters, Spirals and Unit Dynamics by training neural network model
Authors: Luo Zhe 
Published: August 2021

Abstract
Deep learning is an integral part of machine learning by building a structure like the neural network in the human brain. Amounts of different neural network models have been emerged, improved, and transformed. This article will implement various neural network models for three different specific tasks (Japanese character recognition/double helix task/remote unit dynamics) and analyse the fine-tuning parameters process (different network layers, different types of network layers). The result is that generally, models with more layers would perform better. However, other parameter could have significant effects on the performance. For example, activation function, different numbers of hidden nodes, different initial weights, and different periods, etc.