bluuebunny/crossref_metadata_2025_split huggingface dataset:
Jeans analysis with κ-deformed Kaniadakis distribution in f (R) gravity
Authors: He Ke-Rong 
Published: January 2022

Abstract
The influence of the κ-deformed Kaniadakis distribution on Jeans instability in the background of

f

R

gravity is investigated, the dispersion relation considering the κ-deformed Kaniadakis distribution is derived by exploiting the kinetic theory. The cases of high and low frequency perturbations are analyzed, respectively, it is found that the range of the unstable modes and the growth rates decrease with the increased distribution index κ in both of high and low frequency regime. Finally, based on the derivation of effective temperature, the relation between Jeans mass and temperature is studied, it is found that lower Jeans mass means that the system is more likely to collapse due to gravitational instability, the system is unstable for lower distribution index κ.
Kinematical modeling of circular and elliptical polarization of the polarized light via screw transformation
Authors: Özdemir Zehra 
Published: January 2022

Abstract
In this paper, it is shown that the motion of a polarized light wave in an optical fiber has a special meaning that relates the scalar and vector part of the dual quaternion to the kinematics concept of a screw. We describe the circular and elliptical polarization of a light wave according to how the electric field vector varies in a projection onto a plane perpendicular to the propagation direction using the Clifford algebra of the dual quaternion. Also, we determine the circular-Rytov curve (C

R
) and elliptical-Rytov curve (E

R
), which described the paths traced out by the tip of the electric field vector as it propagates in space, related to the polarization state of the electric field

E

. The elliptical and circular polarization states are expressed by using the four Stokes parameters and their matrix form. Furthermore, some motivating examples are given and visualized their images with the help of the MAPLE program.
The synergies between displacement damage creation and hydrogen presence: the effect of D ion energy and flux
Authors: Markelj Sabina , Pečovnik Matic , Schwarz-Selinger Thomas , Kelemen Mitja 
Published: January 2022

Abstract
In this work the synergism between displacement damage creation and presence of hydrogen isotopes was studied. Tungsten samples were irradiated by 10.8 MeV W ions with or without the presence of D ions with two different energies of 300 eV/D and 1000 eV/D and different temperatures. In order to compare the results obtained with different exposure parameters the samples were afterwards additionally exposed to D ions at 450 K to populate the created defects. By increasing the W irradiation time, ion flux and energy, the increase of D concentration and D retention was observed as measured by nuclear reaction analysis and thermal desorption spectroscopy. By fitting the D depth profiles and D desorption spectra by the rate equation code MHIMS-R we could see that additional fill-levels were populated with higher flux and ion energy which ends up in higher final D concentration and retention as compared to experiments with lower D flux and energy.
Stability and global sensitivity analysis of the transmission dynamics of malaria with relapse and ignorant infected humans
Authors: Mangongo Yves Tinda , Bukweli Joseph-Désiré Kyemba , Kampempe Justin Dupar Busili , Mabela Rostin Matendo , Munganga Justin Manango Wazute 
Published: January 2022

Abstract
In this paper we present a more realistic mathematical model for the transmission dynamics of malaria by extending the classical SEIRS scheme and the model of Hai-Feng Huo and Guang-Ming Qiu [21] by adding the ignorant infected humans compartment. We analyze the global asymptotically stabilities of the model by the use of the basic reproduction number R_0 and we prove that when R_0≦1, the disease-free equilibrium is globally asymptotically stable. That is malaria dies out in the population. When R_0>1, there exists a co-existing unique endemic equilibrium which is globally asymptotically stable. The global sensitivity analysis have been done through the partial rank correlation coefficient using the samples generated by the use of latin hypercube sampling method and shows that the most influence parameters in the spread of malaria are the proportion θ of infectious humans who recover and the recovery rate γ of infectious humans. In order to eradicate malaria, we have to decrease the number of ignorant infected humans by testing peoples and treat them. Numerical simulations show that malaria can be also controlled or eradicated by increasing the recovery rate γ of infectious humans, decreasing the number of ignorant infected humans and decreasing the average number n of mosquito bites.
The electric dipole response of even-even 154–164Dy isotopes
Authors: Quliyev H , Saygı N Demirci , Guliyev E , Kuliev A A 
Published: January 2022

Abstract
The excitation of pygmy dipole resonance (PDR) and giant dipole resonance (GDR) in even–even 154–164Dy isotopes is examined through quasiparticle random-phase approximation (QRPA) with the effective interactions that restore the broken translational and Galilean invariances. In each isotope, an electric response emerges by showing ample distribution at energies below and above 10 MeV. We, therefore, study the transition cross-sections and probabilities, photon strength functions, transition strengths, isospin character, and collectivity of the predicted E1 responses.
Use of symmetric sagnac dual-ring scheme for tunable single-mode erbium fiber laser
Authors: Chen Yi-Chi , Yeh Chien-Hung , Lin Wen-Piao , Liu Li-Hung , Ko Han-Shin , Lai Yu-Ting , Chow Chi-Wai 
Published: January 2022

Abstract
In this paper, an erbium-doped fiber (EDF) laser with symmetric Sagnac dual-ring scheme is experimentally studied to achieve tunable and stable continuous-wave (CW) single-longitudinal-mode (SLM) presentation. In the measurement, the obtained wavelength-tuning bandwidth can be reached from 1518.0 to 1578.0 nm based on a C-band EDF gain-medium. Moreover, the corresponding output power, optical signal to noise ratio (OSNR) and wavelength linewidth of the designed EDF laser are studied and performed simultaneously.
A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements
Authors: Zhao Jiamin , Yu Yang , Wang Xu , Ma Shihan , Sheng Xinjun , Zhu Xiangyang 
Published: January 2022

Abstract

Objective. Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements. Approach. Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted. Main results. The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes. Significance. The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.
FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification
Authors: Chen Jiaming , Yi Weibo , Wang Dan , Du Jinlian , Fu Lihua , Li Tong 
Published: January 2022

Abstract

Objective. Motor imagery-based brain–computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding. Approach. A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method channel group attention (CGA) to build a lightweight neural network Filter Bank CGA Network (FB-CGANet). Accompanied with FB-CGANet, the band exchange data augmentation method was proposed to generate training data for networks with filter bank structure. Main results. The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment. Significance. This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.
A Study on the Application and the Advancement of Deep Neural Network Algorithm
Authors: Song Jiulin , Chen Yansheng 
Published: January 2022

Abstract
Deep neural network is a new type of learning algorithm, which has both global and local aspects and performs well in pattern recognition and computational speed. In recent years, deep neural network algorithm has been widely used in scientific research and real life, but its complexity, parallelism and other characteristics lead it to be a very challenging and innovative research area. This study briefly introduces the basic principles and theoretical knowledge of deep neural network algorithms, and mainly discusses their applications and Advancement of feature extraction in the field.
Study on the BL Early Warning Model of Transmission Line Tower Based on KNN-SVM Algorithm
Authors: Hong Huawei , Wu Kaibin , Zhang Yunfeng 
Published: January 2022

Abstract
With the expansion of China’s power grid construction scale, the transmission line span are gradually improved, which also increases the risk of BL stroke on the transmission line. However, the traditional passive BL protection has many problems, such as weak pertinence and high investment cost, which can not meet the needs of social development. KNN can well describe the similarity measure between the two, which can effectively reduce the training samples. SVM can find the best compromise between model complexity and learning ability in small samples, which is a good sample training method. Through KNN - in-depth learning of the historical data of BL activities accumulated in the power grid, a supervised BL early warning model (hereinafter referred to as EWM) of transmission line can be trained. At the same time, the BL strike of transmission line tower (hereinafter referred to as TLT) has complex meteorological conditions, which requires comprehensive confirmation of various monitoring point parameters. Therefore, it is of great significance to study the BL EWM of TLT based on KNN-SVM algorithm. Firstly, this paper analyzes the KNN-SVM algorithm. Then, this paper establishes an EWM. Finally, this paper is verified.
Application Research of BP Neural Network Algorithm in Power Line Loss Calculation
Authors: Dai Su , Ma Yunlong , Wang Kai , Liu Bowen , Liu Changming 
Published: January 2022

Abstract
In recent years, it is still a difficult problem to calculate the line loss of distribution system. The main reason is that China’s power system data is not perfect, the components are huge, the management function does not exist, the network structure is directly connected. Therefore, reducing the loss rate of power system has become an urgent problem for energy companies to solve. Developing effective methods for line loss reduction evidence and line loss assessment requires careful calculation and analysis of line loss, which ultimately contributes to and guides line loss management.
Research on New Virtualization Security Protection Management System Based on Cloud Platform
Authors: Li Zhihong , Liu Guangxu , Dang Yijie , Shang Zhijie , Lin Nan 
Published: January 2022

Abstract
As an emerging product under the condition of informatization, the utilization of cloud platform in many industries has brought fundamental changes to the production and business model in related fields. The cloud platform provides rich and diverse utilization services to terminals through multi-dimensional integration of different IT resources. With the in-depth utilization of cloud platform, the security problems it faces are becoming more and more prominent. The traditional network security protection means have been difficult to effectively adapt to and deal with the security threats under the new situation of cloud platform utilization. As a prominent part of building cloud platform, the construction level of virtualization security protection system will have an intuitive impact on the security of cloud platform. At present, the virtualization security protection management system under cloud platform is facing direct threats from virtual machine deployment, virtual machine communication and virtual machine migration. Based on this, this paper studies the virtualization security protection management system of cloud platform from the perspective of virtualization security tech, so as to ameliorate the stability, reliability and security of cloud platform.
RETRACTED: Smart City Rail Automatic Fare Collection System Under the Background of Big Data
Authors: Xie Shurun 
Published: January 2022

Abstract
With the rapid development of Internet technology, the process of urban construction is accelerating and the level of urbanization is further improved. In the smart city rail automatic fare collection system, there are a large number of data information and data that need to be processed manually. The traditional manual method not only consumes human, material and financial resources, but also has low efficiency. Therefore, this paper proposes a smart city rail automatic fare collection system based on big data design. Firstly, this paper expounds the concept of smart city rail transit and studies the function of automatic fare collection system. Then it studies the definition and characteristics of big data, designs the method of system development, and tests the performance of the system. The test results show that the system runs smoothly, accounts for a relatively small amount of memory, has a fast response speed and low delay. Most passengers are satisfied with the system.
RETRACTED: Knowledge Verification Method Based on Artificial Intelligence-based Knowledge Graph Construction
Authors: Yang Ziting 
Published: January 2022

Abstract
The knowledge graph connects real-world entities and concepts through their relationships, connects all different types of information to obtain a relationship network, and can analyze “relationship” issues. Creating a knowledge graph is a continuous process, and it needs to continuously learn new knowledge and update existing knowledge in the library as time and events change. However, since the accuracy of the updated new knowledge cannot be guaranteed, the new knowledge must be verified. This paper aims to study the knowledge verification method based on artificial intelligence-based knowledge graph construction. Based on the analysis of the knowledge graph construction process, the knowledge graph construction method and the knowledge verification method, knowledge verification is realized by constructing a probabilistic soft logic model. The experimental results show that the recall rate, F1 value, and AUC value of the candidate knowledge set are verified by the knowledge verification model proposed in this paper. Therefore, it can be inferred that the knowledge verification model proposed in this paper is effective.
Power System Data Application Based on Data Association Rules
Authors: Zou Yuelin , Wang Tao , Jaynes Ayidos , Ma Rong 
Published: January 2022

Abstract
With the continuous development and progress of society, electricity has been integrated into people’s lives. However, power transmission is a very complex process that requires power transmission and power system conversion. A large amount of data will be generated during the operation of the power system. Through these data, we can use electricity better and more efficiently. This paper aims to study the power system data application based on data association rules. Based on the analysis of data association rules related algorithms and the application of data association algorithms in the power system, a power failure prediction system is designed and the performance of the system is analyzed. The test results show that the system has a very high transaction success rate, the longest response time does not exceed 20 seconds, and the CPU is operating normally, reaching the expected expectations.
Visual Data Analysis Technology Based on Data Center
Authors: Wang Tianjun , Wang Cengceng , Guo Jiangtao , alim dildar 
Published: January 2022

Abstract
Today, people are in an information explosion society, and visualization technology(VT) is an inevitable product of the development of the information society. With the emergence of multimedia products such as computers, networks, and communications, humans are paying more and more attention to data processing. Many countries in the world have already begun research in this area and have achieved remarkable results. VT is a core part of data analysis, also known as information processing and storage technology. It has a very extensive and important application in the field of data management. However, because the key information hidden in the data is often immersed in the massive data, it is necessary to filter the data information efficiently, and the visualization data analysis technology is a crucial part. This article adopts the experimental analysis method, which aims to provide a new method to solve the problems of traditional technology and the challenges that may arise in the future by further understanding the existing visual data analysis technology and development trend. According to the research results, the recognition rate of the optimized color visualization features under different classifiers is higher than that of the original emotional features. It can be seen that visual analysis technology is not limited to data sets with physical meaning, but can also be applied to abstract feature sets such as emotional features.
Application of Data Mining Technology Based on Data Center
Authors: Zhang Longjun , Liu Kun , Ilham Ilyar , Fan Jiaxin 
Published: January 2022

Abstract
Data mining technology refers to the use of mathematics, statistics, computer science and other methods to process a large amount of information to obtain useful conclusions and provide valuable decisions for people. With the rapid development and popularization of the Internet era and the more and more extensive application of computers in various fields, data mining technology has become a hot research field in today’s society. Based on the data center, this paper studies the data mining technology. Firstly, this paper expounds the definition of data mining, and studies the process of data mining and the steps of processing data. Then, this paper also designs and studies the framework of data mining, and tests the performance of the algorithm. Finally, the test results show that data mining technology can well meet the target requirements.
Exploration of Artificial Intelligence Security in Multi Unmanned System Cooperation
Authors: Li Shanshan , Zhang Liang , Li Zongpu 
Published: January 2022

Abstract
In modern science and technology, artificial intelligence has become one of the most important and promising technologies in today’s society and plays a very important role in people’s life. In artificial intelligence, cooperation is a very important research direction, which includes the cooperation between sensors, coordinated man-machine interface and actuators on multiple UAVs. Therefore, based on the exploration of artificial intelligence security, this paper studies artificial intelligence in multi unmanned system cooperation. Firstly, this paper expounds the development of cooperative system, and then describes the purpose of multi unmanned system cooperation. Then, this paper studies the intelligent algorithm applied to the cooperation of multiple unmanned systems in the field of artificial intelligence. Finally, aiming at the existing security problems of artificial intelligence, this paper tests the functions of multiple unmanned systems. The test results show that when multiple unmanned systems work together, the accuracy of artificial intelligence in dealing with things is basically more than 90%. At the same time, it can be nearly 100% scientific, and can budget a variety of treatment schemes. This shows that in the multi unmanned system cooperation, artificial intelligence can almost meet its needs, but it still needs to be further improved.
Rapid Assembly Modeling in Computer Aided Design System
Authors: Hong Chenghua , Ge Han , Fang Cunzhong , Zhao Xuyang , Liu Ning , Wang Bin , Zhao Yang 
Published: January 2022

Abstract
Rapid assembly modeling system is a tool that needs to be used in the design process. Because the three-dimensional modeling is intuitive, powerful and can be used in actual engineering, it is more and more used in manufacturing production. In the rapid development of computer-aided design technology, CAD/CAM software is the most commonly used and most widely used modeling tool. Therefore, the purpose of this article to study the rapid assembly modeling in the computer-aided design system is to improve the performance and accuracy of the assembly system and promote the high-quality production of products. This article mainly uses experimental method and case analysis method to test the assembly system designed in this article. The experimental results show that, under low temperature conditions, the relative error and absolute error of the assembly size are in a small space, which meets the actual requirements. Therefore, the system designed in this paper can be used in practice.
Digital TV Network Management System Based on WEB
Authors: Qi Wei , Ying Chun , Yong Sheng , Zhao Guizhi , Wang Lihua 
Published: January 2022

Abstract
With the development and popularization of computer artificial intelligence technology, more and more intelligent machines are gradually produced. These intelligent machines have brought great convenience to people’s lives. This paper studies the control method of snake robot based on environment adaptability, which mainly explains the construction and stability of multi-modal CPG model. In addition, this paper also studies the trajectory tracking and dynamic obstacle avoidance of mobile robot based on deep learning.