bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on EEG Feature Extraction and Recognition Method of Lower Limb Motor Imagery
Authors: Li Dong , Peng Xiaobo 
Published: July 2022

AbstractAiming at the problems of difficult signal acquisition, low signal-to-noise ratio and poor classification accuracy of BCI technology, based on the theory of EEG, this paper designs a leg raising EEG experiment of lower limb motor imagery and collects EEG signal data from 20 subjects to improve the accuracy of classification and recognition The process of feature extraction and classification recognition is explored, and a multi domain fusion method is proposed for EEG signal feature extraction from time domain, frequency domain, time-frequency domain and spatial domain. At the same time, bagging and gradient boosting ensemble learning algorithms are applied to EEG signal classification and recognition, and multi domain fusion features are tested by constructing different classifiers, The final classification accuracy reaches 87.8% and 93%, which is better than the traditional SVM classification method.
Circular L(j,k)-Labeling Numbers of Cartesian Product of Three Paths
Authors: Wu Qiong , Rao Weili 
Published: July 2022

AbstractThe circular $$L(j,k)$$

L
(
j
,
k
)

-labeling problem with $$k\ge j$$

k
≥
j

arose from the code assignment in the wireless network of computers. Given a graph $$G$$
G
and positive numbers $$j,k,\sigma $$

j
,
k
,
σ

, and a circular $$\sigma $$
σ
-$$L(j,k)$$

L
(
j
,
k
)

-labeling of a graph $$G$$
G
is an assignment $$f$$
f
from $$[0,\sigma )$$

[
0
,
σ
)

to the vertices of $$G$$
G
, for any two vertices $$u$$
u
and $$v$$
v
, such that $$|f(u)-f(v){|}_{\sigma }\ge j$$

|
f

(
u
)

-
f

(
v
)

|
σ

≥
j

if $$uv\in E(G)$$

u
v
∈
E
(
G
)

, and $$|f(u)-f(v){|}_{\sigma }\ge k$$

|
f

(
u
)

-
f

(
v
)

|
σ

≥
k

if $$u$$
u
and $$v$$
v
are distance two apart, where $$|f\left(u\right)-f\left(v\right){|}_{\sigma }=min\left\{|f(u)-f(v)|, \sigma -|f(u)-f(v)|\right\}$$

|
f

u

-
f

v

|
σ

=
m
i
n

|
f
(
u
)
-
f
(
v
)
|
,
σ
-
|
f
(
u
)
-
f
(
v
)
|

. The minimum $$\sigma $$
σ
such that graph $$G$$
G
has a circular $$\sigma $$
σ
-$$L(j,k)$$

L
(
j
,
k
)

-labeling of a graph $$G$$
G
, which is called the circular $$L(j,k)$$

L
(
j
,
k
)

-labeling number of graph $$G$$
G
and is denoted by $${\sigma }_{j,k}(G)$$

σ

j
,
k

(
G
)

. In this paper, we determine the circular $$L(j,k)$$

L
(
j
,
k
)

-labeling numbers of Cartesian product of three paths, where $$k\ge 2j.$$

k
≥
2
j
.
A Formalization of Topological Spaces in Coq
Authors: Yan Sheng , Fu Yaoshun , Guo Dakai , Yu Wensheng 
Published: July 2022

AbstractIt is a wish for Wu Wen-tsun to implement the mechanical proving of theorems in topology. Topological spaces constitute a fundamental concept of general topology, which is significant in understanding the essential content of general topology. Based on the machine proof system of axiomatic set theory, we presented a computer formalization of topological spaces in Coq. Basic examples of topological spaces are formalized, including indiscrete topological spaces and discrete topological spaces. Furthermore, the formal description of some well-known equivalent definitions of topological spaces are provided, and the machine proof of equivalent definitions based on neighborhood system and closure is presented. All the proof code has been verified in Coq, and the process of proof is standardized, rigorous and reliable.
A Storage Scheme for Access Control Record Based on Consortium Blockchain
Authors: Shi Yunmei , Li Ning , Hou Shoulu 
Published: July 2022

AbstractThe heterogeneous access control information scattered around different organizations or units is difficult to be gathered and audited, however, easy to be maliciously tampered. Aiming at the problems, this paper presents a blockchain-based storage scheme to store access control information, which can protect information privacy and facilitate the audit work. This is achieved by exploiting consortium blockchain, cryptography technology. Based on the scheme, we define the format of Access Control Record (ACR), design upload and download protocols, and realize the signature and encryption process for ACRs in a simulation environment. Theoretical analyses demonstrate that the proposed storage scheme needs lower storage cost and has higher efficiency compared with existing schemes, and can resist typical malicious attacks effectively.
A Multi-modal Seq2seq Chatbot Framework
Authors: Ji Zhi 
Published: July 2022

AbstractThe pandemic has forced young people to stay away from school and friends, complete online learning at home and live at home. Therefore, various mental illnesses such as anxiety and depression occur more frequently. Chatbot is a communication method that is more acceptable to young people. This paper proposes a multi-modal chatbot seq2seq framework, which divides the mental state of young people into different types through multi-modal information such as text and images entered by users in the chatbot. This model combines image description and text summarization modules with the attention mechanism in a multi-modal model to control related content in different modalities. Experiments on multi-modal data sets show that this method has 70% average accuracy and real users who use this system also believe that this method has good judgment ability.
Five Application Modes of Mobile Government
Authors: Yu Gangqiang , Li Dongze , Liu Jinyu 
Published: July 2022

AbstractTo solve the problem that traditional e-government tends to lose real-time control of content and process, mobile government was created, it has 5 main application modes, which are mG2G mode between government departments and other government departments, mG2E mode between government and internal staff, mG2B mode between government and business, mG2C mode between government and the public, and mG2V mode between government and organizations & people outside the country. Mobile government uses mG2C, mG2B and mG2V as the external service mode and mG2G and mG2E mode as the internal management mode to continuously improve the quality and level of external services through continuous optimization of internal management.
Human Action Recognition Based on Attention Mechanism and HRNet
Authors: Liu Siqi , Wu Nan , Jin Haifeng 
Published: July 2022

AbstractA human action recognition network (AE-HRNet) based on high-resolution network (HRNet) and attention mechanism is proposed for the problem that the semantic and location information of human action features are not sufficiently extracted by convolutional networks. Firstly, the channel attention (ECA) module and spatial attention (ESA) module are introduced; on this basis, new base (EABasic) and bottleneck (EANeck) modules are constructed to reduce the computational complexity while obtaining more accurate semantic and location information on the feature map. Experimental results on the MPII and COCO validation sets in the same environment configuration show that AE-HRNet reduces the computational complexity and improves the action recognition accuracy compared to the high-resolution network.
Design of the Electric Power Spot Market Operation Detection System
Authors: Zeng Min , Mu Qichun 
Published: July 2022

AbstractWith the continuous deepening of the construction of the electric power pot market, it is necessary to optimize the operation mechanism of the spot market according to the operation of the spot market, study the information interaction and data integration technology of the spot market to support the coordinated operation of multiple markets, and design the overall architecture, application architecture, functional architecture of the information interaction and data integration platform of the spot market for the coordinated operation of multiple markets Hardware architecture and security protection system provide technical support for information interaction and data integration of multiple market coordinated operation of the power spot market. Through data visualization technology, this paper realizes the data visualization and background management of the provincial power spot market operation detection system, which is convenient for decision-makers to carry out data analysis and management.
Analysis of the Micro Implicit Feedback Behavior of User Network Exploring Based on Mobile Intelligent Terminal
Authors: Wang Wei , Zhang Chuang , Zheng Xiaoli , Du Yuxuan 
Published: July 2022

AbstractIn the face of the information recommendation requirements in mobile Internet applications, in order to better use the user micro implicit feedback behavior obtained by the mobile intelligent terminal to improve the recommendation efficiency, this paper intends to carry out the analysis of the implicit feedback behavior by analyzing the behavior distribution and behavior correlation. The analytical results reveal the particularity of the implicit feedback behavior in mobile intelligent terminal.
A Novel Home Safety IoT Monitoring Method Based on ZigBee Networking
Authors: An Ning , Li Peng , Wang Xiaoming , Wu Xiaojun , Dang Yuntong 
Published: July 2022

AbstractThis paper realizes the design of home safety early warning system by studying the wireless communication networking technology of ZigBee and WiFi, as well as sensor communication technology, which is based on taking home safety monitoring as the application background. In this study, CC2530 chip was used as ZigBee wireless communication module. A novel home security IoT monitoring method was proposed through sensor triggering, human activity trajectory perception algorithm design, and wireless networking and communication optimization. Meanwhile, the safety early warning and remote monitoring of home staff can be realized, and home safety can be guaranteed. The system can achieve the purpose of home monitoring and early warning with low software and hardware cost through the experimental design and result analysis. It can not only provide reference for the design of sensor communication system, but also provide technical reference for aging society and response.
An Improved Raft Consensus Algorithm Based on Asynchronous Batch Processing
Authors: Li Hao , Liu Zihua , Li Yaqin 
Published: July 2022

AbstractThe consensus algorithm has been popular in current distributed systems as it is more effective in solving server unreliability. It ensures a group of servers can form a coordinated system, and the entire system continues to work when a part of the service point fails. Raft is a well-known and widely used distributed consensus algorithm, but as it has a built-in purpose of comprehensibility, it is always compromised in terms of performance as a trade-off. In this paper, we mainly aim to improve the traditional Raft consensus algorithm’s performance problem, especially in high concurrency scenarios. We introduce a pre-proposal stage on top of the algorithm to achieve efficiency optimization through batch asynchronous log replicated and disk flushing. The experiment proved that the improved Raft could increase the system throughput by 2–3.6 times, and the processing efficiency for parallel requests can be increased by 20% or more.
Design and Implementation of a Novel Interconnection Architecture from WiFi to ZigBee
Authors: Gu Yu , Wu Chun , Li Jiangan 
Published: July 2022

AbstractThe signal layer heterogeneous communication technology is a cross-technology communication (CTC) technology, which is a direct communication technology between different wireless devices. Since ZigBee and WiFi have overlapping spectrum distribution, the ZigBee transmission will affect the CSI sequence. We propose a CTC technology based on machine learning and neural network, from Zigbee to WiFi, leveraging only WiFi channel state information (CSI). By classifying WiFi CSI, we can distinguish whether there is ZigBee signal transmission in WiFi signal. This paper uses the machine learning method and neural network method to classify CSI sequence analyzes the importance of CSI sequence features to the classifier, improves the accuracy of machine learning classifier by extracting multiple CSI sequence features, and improves the classification accuracy by neural network classifier. In our experimental data set, the highest accuracy can reach 95%. The evaluation results show that our accuracy is higher than the existing methods.
Robust Controller Design for Steer-by-Wire Systems in Vehicles
Authors: El Akchioui Nabil , El Fezazi Nabil , El Fezazi Youssef , Idrissi Said , El Haoussi Fatima 
Published: July 2022

AbstractThe steer-by-wire (SbW) technology enables to facilitate better steering control as it is based on an electronic control technique. The importance of this technology lies in replacing the traditional mechanical connections with steering auxiliary motors and electronic control and sensing units as these systems are of paramount importance with new electric vehicles. Then, this research paper discusses some difficulties and challenges that exist in this area and overcomes them by presenting some results. These results meet the SbW’s robust performance requirements and compensate oscillations from the moving part of the steering rack in the closed-loop system model: modeling, analysis and design. Thus, the issue of robust control for nonlinear systems with disturbances is addressed here. Finally, the results are validated through detailed simulations.
Study on the Comparison and Selection of County Energy Internet Planning Schemes Based on Integrated Empowerment-Topsis Method
Authors: Tan Qingkun , Yin Jianbing , Wu Peng , Xu Hang , Tang Wei , Chen Lin 
Published: July 2022

AbstractEnergy Internet is an important way to solve current energy and environmental problems. It combines the planning of multi-energy systems such as electricity, natural gas, heat and transportation, combines energy conversion and utilization with comprehensive demand response, and integrates energy supply network planning with sources and loads. Energy hub planning is combined. Firstly, through the literature survey method and expert interview method to identify the factors that affect planning, and establish a factor index system. Secondly, in order to make the calculation results more meaningful, subjective and objective weighting are combined, and the expert scoring method and the entropy weight method are used to determine the weight of the factors at each stage. Finally, a calculation example is used to verify the rationality of the topsis method for county-level energy Internet collaborative planning. The results of the calculation example show that collaborative planning can avoid the shortcomings of single-subject planning, and the model has certain applicability.
Application of Big Data Technology in Equipment System Simulation Experiment
Authors: Hou Jiajun , Zhan Hongtu , Jia Jia , Li Shu 
Published: July 2022

AbstractIn order to solve the problem of single utility between system data in simulation experiment, the simulation experiment method and experimental framework of equipment system development are analyzed. This paper constructs the experimental framework of big data technology in equipment system simulation, uses big data analysis technology, analyzes the application process in equipment system simulation experiment, and puts forward the shortcomings and difficulties of applying big data technology in equipment system simulation experiment. By introducing big data technology, it provides a reference basis for weapon equipment system development demonstration.
Complex Relative Position Encoding for Improving Joint Extraction of Entities and Relations
Authors: Cai Hua , Xu Qing , Shen Weilin 
Published: July 2022

AbstractRelative position encoding (RPE) is important for transformer based pretrained language model to capture sequence ordering of input tokens. Transformer based model can detect entity pairs along with their relation for joint extraction of entities and relations. However, prior works suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first use BERT with complex relative position encoding (cRPE) to encode the input text information, then decompose the joint extraction task into two interrelated subtasks, namely head entity extraction and tail entity relation extraction. Owing to the excellent feature representation and reasonable decomposition strategy, our model can fully capture the semantic interdependence between different steps, as well as reduce noise from irrelevant entity pairs. Experimental results show that the F1 score of our method outperforms previous baseline work, achieving a better result on NYT-multi dataset with F1 score of 0.935.
The Research of Adaptive Modulation Technology in OFDM System
Authors: Zhang Xiuyan , Tao Guobin 
Published: July 2022

AbstractOrthogonal frequency division multiplexing (OFDM) as a special multi-carrier transmission technology has good resistance to narrow-band interference and frequency selective fading ability. Compared with traditional modulation techniques, adaptive modulation can enhance bandwidth efficiency and system capacity. Therefore, applying adaptive modulation in OFDM systems can take full advantage of spectrum resources, and it is suitable for the high-speed and reliable mobile communication systems in the future. The purpose of this paper is to improve traditional OFDM adaptive algorithms (Hughes-Hartogs, Chow) to realize bits allocation, power allocation better. In this paper, simulation results demonstrated that the improved Levin-Campello algorithm lowers algorithm’s complexity greatly and owns better flexibility, at the same time, it guarantees good the bit error rate (BER) performance and can be applied to speech communication (fixed rate) and data communication (variable rate) in wireless communication systems.
Unsupervised MRI Images Denoising via Decoupled Expression
Authors: Zhang Jiangang , Pan Xiang , Lv Tianxu 
Published: July 2022

AbstractMagnetic Resonance Imaging (MRI) is widely adopted in medical diagnosis. Due to the spatial coding scheme, MRI image is degraded by various noise. Recently, massive methods have been applied to the MRI image denoising. However, they lack the consideration of artifacts in MRI images. In this paper, we propose an unsupervised MRI image denoising method called UEGAN based on decoupled expression. We decouple the content and noise in a noisy image using content encoders and noise encoders. We employ a noising branch to push the noise decoder only extract the noise. The cycle-consistency loss ensures that the content of the denoised results match the original images. To acquire visually realistic generations, we add an adversarial loss on denoised results. Image quality penalty helps to retain rich image details. We perform experiments on unpaired MRI images from Brainweb datesets, and achieve superior performances compared to several popular denoising approaches.
Segmenting of the Sonar Image from an Undersea Goal Using Two Dimensional THC Entropy
Authors: Liu Yu , Wang Ruiyi , Guo Haitao 
Published: July 2022

AbstractSonar image segmentation is the basis of undersea goal detection and recognition. The THC (Tsallis-Havrda-Charvát) entropy can describe the statistical properties of the non-extensive systems and has a wider range of applications. There are multiple choices for the two features of the two-dimensional histograms, such as the gray value, the average gray value within a neighborhood, the median gray value within a neighborhood, the mode of gray values within a neighborhood, and so on. This paper investigates the segmentation results of a sonar image from an undersea goal using THC entropies of a variety of two-dimensional histograms, and gives the evaluation indexes for the segmentation results.
A High-Efficiency Knowledge Distillation Image Caption Technology
Authors: Li Mingxiao 
Published: July 2022

AbstractImage caption is wildly considered in the application of machine learning. Its purpose is describing one given picture into text accurately. Currently, it uses the Encoder-Decoder architecture from deep learning. To further increase the semantic transmitted after distillation by feature representation, this paper proposes a knowledge distillation framework to increase the results of the teacher section, extracting features by different semantic levels from different fields of view, and the loss function adopts the method of label normalization. Handle unmatched image-sentence pairs. In order to achieve the purpose of a more efficient process. Experimental results prove that this knowledge distillation architecture can strengthen the semantic information transmitted after distillation in the feature representation, achieve a more efficient training model on less data, and obtain a higher accuracy rate.
Crayfish Quality Analysis Based on SVM and Infrared Spectra
Authors: Ye Zijian , Mou Yi 
Published: July 2022

AbstractDifferent algorithms combined with Near-infrared spectroscopy were investigated for the detection and classification of crayfish quality. In this study, the crawfish quality was predicted by partial least square-support vector machine, principal component analysis-support vector machine, BP neural network and support vector machine after pre-processing the NIR spectral data of crawfish. The result shows that the accuracy of near-infrared spectroscopy technology combined with SVM to classify crayfish quality can reach 100%, and the prediction can guide the sampling of crayfish food safety in practice, thus improving food safety and quality.
Complete collection of one-loop triple-collinear splitting operators for dimensionally-regulated QCD
Authors: Czakon Michał , Sapeta Sebastian 
Published: July 2022

Abstract
We provide results for the one-loop triple-collinear color/spin-space splitting operators for the five possible processes, q → $$ {qq}^{\prime }{\overline{q}}^{\prime } $$

qq
′

q
¯

′

, q → $$ qq\overline{q} $$
qq

q
¯

, q → qgg, g → $$ gq\overline{q} $$
gq

q
¯

and g → ggg. The expressions are exact in dimensionally-regulated massless QCD up to a single integral, which we expand to second order in the dimensional-regularisation parameter. We also evaluate the related splitting functions. Our results are both sufficient and indispensable for the construction of subtraction and integrated-subtraction terms for triple-collinear singularities of one-loop double-real-emission cross-section contributions as part of a next-to-next-to-next-to leading order subtraction scheme.