bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal spacecraft asymptotic velocity for the high inclined orbits formation using gravity assists in the planetary systems
Authors: Grushevskii Alexey , Golubev Yury , Koryanov Victor , Tuchin Andrey , Tuchin Denis
Published: February 2021

The inclination changing of the celestial bodies’ orbits is one of the most energy-consuming procedures. Nevertheless, for a number of promising space projects, leaving the main plane of the planetary or satellite system is fundamentally necessary and demanded. In this case only the possibility of realizing the formation of maximally inclined spacecraft (SC) orbits using gravity assist maneuvers (GM) is remained. However, the dynamic possibilities of GM using are also limited. The maximum possible value of the SC orbital inclination (which gives the extreme point inclination pole – IncPole) depends on the modulus V
inf of the SC asymptotic velocity on the flyby hyperbola and is directly proportional to it (GM geometric limitation). However, the magnitude of the change in inclination at one GM is inversely proportional to V
inf (GM dynamic limitation). As a result, a compromise value of V
inf is existing, which varies for each specific case of the flyby planet. Since in order to achieve a significant inclination a whole series of GMs may be required, in addition, each GM must ensure the “resonance” of the orbital periods of the planet and SC after the GM in order to guarantee their new meeting. According the Jacobi integral in the restricted three body problem the V
inf is an invariant during in all interplanetary flights using GMs. It’s possible to construct the series of increasing GM in form of “jumps” along the

V

inf
⁡

r
e
s

resonance levels from the initial inclination to the maximum possible point IncPole. This point may not belong to any isoline of one of the main resonances itself in the general case. This reduces the GMs effectiveness and necessarily increases the mission time of flight. A heuristic consideration consists in choosing (in the “resonant tuning”) the such value of the design V
inf, which ensures the localization of the inclination pole in the vicinity of some resonance curves between the orbital periods of the spacecraft and the planet (resonant

V

inf
⁡

r
e
s

). Thus it is possible to get to the IncPole by jumping along the resonance curve thru the minimum amount of GM. The paper describes the

V

inf
⁡

r
e
s

formulas and gives its estimates for the Solar system and for the planets satellite systems.
On Abrupt Changes of the Approximate and Sample Entropy Values in Supercomputer Power Consumption
Authors: Tomčala Jiří
Published: February 2021

When calculating the Approximate Entropy (ApEn) and the Sample Entropy (SampEn) of a time series, representing the course of supercomputer power consumption, seemingly unexplained abrupt changes sometimes appear. These sudden changes occur when calculating the course of ApEn and SampEn values using a floating time window and can be manifested also in other discrete-valued time series, such as the development of prices on stock exchanges, time series from birth-death models, count-data time series, etc. These abrupt changes are not continuous and it is clear that they do not reflect real changes in the complexity degree of the analyzed time series. This can cause erroneous detection of a change in the degree of complexity of the time series, which can trigger a false alarm that something unusual is happening in the complex system being monitored (human brain, engine gearbox, financial market, supercomputer infrastructure, etc.). This work reveals in detail the mechanism of this phenomenon and also proposes measures to prevent its occurrence.
Exploring the exact differential equations with GeoGebra software
Authors: Funes Jorge Olivares , Valero Kari Elvis R.
Published: February 2021

In this paper, we will show the solutions of certain exact differential equations that are obtained through the interactive GeoGebra software. GeoGebra software has been of great motivational support in the processes of teaching and modeling in mathematics in various universities and colleges, especially in the engineering careers of the University of Antofagasta in the courses of differential equations and calculation of several variables
First-order linear partial differential equations using the GeoGebra and GeoGebra 3D graphing calculator
Authors: Funes Jorge Olivares , Martin Pablo , Valero Kari Elvis R.
Published: February 2021

In this paper we will show, through various examples of how are the animated solutions of certain linear partial differential equations of the first order with constant homogeneous coefficients through GeoGebra applets designed for a first course of Partial Differential Equations for the engineering careers of the University of Antofagasta-Chile
Bifurcation diagram of stationary solutions of the 2D Kuramoto-Sivashinsky equation in periodic domains.
Authors: Evstigneev Nikolay M. , Ryabkov Oleg I.
Published: February 2021

The solution tree of the 2D stationary Kuramoto-Sivashinsky equations in the periodic domain is analized using the analytical and numerical methods. The evolution of stationary solutions is considered by constructing the bifurcation diagram. Some bifurcation points on the main solution are found analytically, where secondary bifurcations are analized numerically. The bifurcation diagram is constructed using the deflated pseudo arc-length continuation process that allows one to find both connected and disconnected branches of solutions. The resulting bifurcation diagram is analized and subdivided according to characteristic properties of the solution. Different solution branches are visualized in the physical space.
Spatiotemporal localizations of light in quadratically nonlinear media with transversal inhomogeneity
Authors: Chuprikov Ivan , Kalinovich Aleksey , Komissarova Maria , Sazonov Sergey , Zakharova Irina
Published: February 2021

In the current study with the help of mathematical modeling we investigate the generation of second optical harmonic provided various group velocity dispersion (GVD) and third order dispersion effects taken into account. Varying values and signs of the third-order dispersion at quasi-zero values of GVD we reveal the conditions of light bullet formation and stable propagation. To this end we use a generalization of the well-known system of the second harmonic generation in quasi-optical approach. Particular attention is paid to the case when GVD at the second harmonic is close to zero. In addition, we investigate these cases using both focusing and defocusing waveguide which means the presence of transversal inhomogeneity.
Modeling a calibrator for laser interferometer gravitational wave detectors
Authors: Frajuca C , Bortoli F S , Prado A. R. , Magalhaes N. S. , Da Silva W. C. , Souza R. C.
Published: February 2021

Laser Interferometers Gravitational Wave detectors have more than 100 active attenuator systems, the normal methods for calibration seems to not work properly. This work aims to model a calibrator for this detector in the form of a resonant-mass Nb gravitational detector thar has the same sensitivity of LIGO detector bur operating in a narrow band. This detector will detect the same gravitational wave and use this signal to calibrate LIGO. A simulation in a lumped model is made and it shows that the calibration is achievable.
Design of comb-based decimation filter with low power consumption, increased alias rejection and decreased passband droop
Authors: Fernandez De Lara Sosa A , Jovanovic Dolecek G , Salguero Luna S
Published: February 2021

This paper presents design of a low power comb-based decimation filter for decimation of 100. The proposed structure is composed of two stages. The first and second stages are decimated by 4 and 25, respectively. The first stage is a non-recursive structure composed of two cosine filters each decimated by 2. The second stage is a Cascaded-Integrator-Comb (CIC) structure, decimated by 25. Since CIC structure is moved to lower rate, we get a low power structure. The alias rejection is improved increasing the number of cascaded filters by 1 or 2, at second stage. The passband droop is decreased by introducing the compensator at lower rate, i.e. after downsampling by 100. The parameters of compensator are obtained using method of intervals.
STOCHASTIC SIMULATION TO PROJECT SUPPLY OF THE KAZAKHSTAN GENERAL PRACTICE WORKFORCE TO 2030
Authors: Koichubekov Berik , Kharin Azamat , Sorokina Marina , Korshukov Ilya , Omarkulov Bauyrzhan , Ukubayev Temirlan
Published: February 2021

Our aim was to explore the effect of the uncertainty of some parameters on total General Practitioner (GP) supply in Kazakhstan to 2030. System dynamics simulation was used to develop model for General Practitioner workforce forecasting. Sensitivity analysis (Monte Carlo method) was performed to account for changes in the number of GP as each and set of model parameters is varied. Three key input parameters were explored: retirement rate, attrition rate, recruitment rate. For each parameter relative sensitivity was calculated. Created model is the least sensitive to the parameter of the retirement rate, but is very sensitive to attrition rate. Staff turnover is one of the main problems of primary health care. The attrition rate parameter has great potential for controlling the flow of labor. The level of recruitment (other than new GPs) is another factor that has a significant impact on forecasting GPs. The sensitivity to this parameter is comparable to the sensitivity to the rate of depletion. We believe that measures taken to reduce dropouts will simultaneously increase recruitment. We also evaluated the effect of all three parameters simultaneously. If the most likely scenario is realized, then the proposal of a General Practitioner will almost completely cover the needs with a small deficit of 68 to 305 doctors. If the high limit or low limit scenarios in the labor market are implemented, Healthcare Ministry will face serious problems.
Modelling militantism and partisanship spread in the chain and square lattice opinion structures by using q-XY opinion model
Authors: Prenga Dode , Kushta Elmira , Ifti Margarita
Published: February 2021

In this work we have studied the spreading of militantism in 1D and 2D square lattice by operating the q-XY opinion and its utility optimization as control mechanism for the update process. The average utility and opinion value for a pair of opinion nodes are calculated in the framework of standard statistical mechanics using the q-opinion utility

U
=
−

J

2

(

O

→

2

−
2
)

−

F
O

→

+
q

(

J

2

(

O

→

2

−
2
)

∗

F
O

→

)

. The agreement with an incoming zealot opinion is modelled as one by one dialogue starting from a random opinion configuration. Each time step one node updates its opinion value according to a set of rules and by specific probabilities based on local utility improvement. New opinion values are accepted with a Metropolis-like probability calculated by employing q-utilities of the nodes under updating process. For the chain opinion structure in the interaction type preserving regimes where

1
−
q

J

2

(

O

→

2

−
2
)

>
0

given 0 ≤ O ≤ 1 it resulted that that both 1D and 2D structures accept the militant offer only partially. For bounded chain the agreement level is at average values for short chain and low values for bounded long opinion chain. Bounded chain structures are found resistant toward militantism accommodation. For square opinion lattices we obtained also that the militant attitude would never cover all the community. Homogenous-nodes group resulted somewhat deterring in accepting the militant opinion with an agreement level of around 0.6. For heterogonous communities the edge agreement level closed up to around 0.7-0.8. The model reproduces the fact that there would be always a fraction of opposition whatsoever regime is applied. It produces various outcomes imitating the complexity of the social behaviour.
Numerical Modelling of the Draught Rate in a Mechanically Ventilated Climate Chamber
Authors: Angelova R A , Mijorski S , Markov D , Stankov P , Velichkova R , Simova I
Published: February 2021

The thermal environment in an indoor space is determined by the thermal state of the human body, and the local thermal discomfort. The draught rate (DR) is one of the indices for thermal discomfort. The achievement of air distribution without draught is one of the goals of the ventilation methods. It is especially important in the design of climate chambers, where the volume is small, and the research studies may require prolonged occupants’ exposure. Our study shows results from the CFD simulations of a mechanically ventilated climate chamber, performed in the design stage of the chamber’s construction. Velocity profiles distribution, temperature distribution and DR are used to assess the thermal comfort of the person in the chamber. The results obtained allowed designing of the proper indoor environment with desired characteristics for air distribution and human exposure.
Model of the spectral dependence of changes in the polarization state of laser radiation in magneto-optical crystal in the presence of multiple reflections from its faces
Authors: Seleznev A V , Rodionov D A , Kovalenko N V , Shaidullin R I , Ryabushkin O A
Published: February 2021

In this work we investigated the phenomena in the Faraday rotator that can affect the polarization state of transmitting laser radiation. We have found that multiple reflections of radiation from the faces of the terbium gallium garnet (TGG) crystal can cause deterioration of radiation extinction ratio and, therefore, isolation properties of Faraday cell. We were able to calculate the impact of this factor on polarization extinction ratio of monochromatic and quasimonochromatic laser radiation transmitting through the crystal.
A compact 3-dB coupler with stubs
Authors: Letavin Denis A , Alaev Danil P
Published: February 2021

High wave resistance line and no-load stubs were used as a tool for reducing the area of directional coupler on microwave substrate. Replacement of all λ/4 sections (segments) that are part of the coupler with the described artificial transmission lines allowed to reduce the area of the device by 76,8% (with frequency 1.9 GHz). Mathematical calculation and analysis of devices was made using Cadence AWR DE. The manufactured layout has comparable characteristics with the characteristics obtained for the model in AWR program. Negative aspects of miniaturization are narrowing of the line and increasing losses at the coupler.
Light-front approach to relativistic electrodynamics
Authors: Fiore Gaetano
Published: February 2021

We illustrate how our recent light-front approach simplifies relativistic electrody-namics with an electromagnetic (EM) field F

μν

that is the sum of a (even very intense) plane travelling wave

F

t

μ
ν

(
c
t
−
z
)

and a static part

F

s

μ
ν

(
x
,
y
,
z
)

; it adopts the light-like coordinate ξ = ct – z instead of time t as an independent variable. This can be applied to several cases of extreme acceleration, both in vacuum and in a cold diluted pasma hit by a very short and intense laser pulse (slingshot effect, plasma wave-breaking and lasera wake-field cceleration, etc.)
On the Connection Between Deutsch-Jozsa Algorithm and Bent Functions
Authors: Marinho Eraldo Pereira
Published: February 2021

It is presently shown that the Deutsch-Jozsa algorithm is connected to the concept of bent function. Particularly, it is noticeable that the quantum circuit used to denote the well-known quantum algorithm is by itself the quantum computer that performs the Walsh transform of a Boolean function. Consequently, the output from the Deutsch-Jozsa algorithm when the hidden function is bent corresponds to a flat spectrum of quantum states.
A mathematical model for the description of the electrical conductivity of graphene/polymer nanocomposites
Authors: Öz Yahya
Published: February 2021

The dispersion of nanomaterials such as graphene has a major influence on the electrical conductivity in nanocomposites with a polymer matrix. For the description of this effect a continuum model is presented. Within this model, the percolation threshold is determined. Moreover, a distributional analysis is used for the purpose of taking electron hopping between graphene into account. Hence, interfacial conductivity enhanced by tunneling is also included in this study. Recent measurements of nanocomposites that are used for aerospace applications are compared with the results of the presented model and show good agreement between model and experiment.
Frequency response of a microcantilever immersed in a fluid
Authors: Quiñones Manuel , Camargo Luz Helena , Baquero Hugo Aya
Published: February 2021

The micromechanical systems include devices and technology such as actuators and electronic elements on a micrometric scale. A key piece in the development of these systems are the micro cantilevers, which mechanical and dynamic features allow to design sensors and actuators, among others. However, the dynamic response of a microcantilever is altered when it is immersed in a fluid, such as water or even air. This work presents the physical models that describe the behavior of the microcantilevers in fluids (water and air) through the analysis of finite elements. The results show that the density and viscosity of the fluid alter both the oscillation amplitude of the microcantilever and modify the oscillation frequency. Nevertheless, the behavior of the microcantilever in vacuum and air is quite similar.
Development of the sharing economy in Russia
Authors: Galeeva G M.
Published: February 2021

The article discusses the main approaches to defining the sharing economy in Russia and abroad. The factors of development of sectors of the sharing economy in Russia are highlighted. The analysis and assessment of the prospects for the development of the sharing economy in Russia is presented.
Effect of temperature on the energy gap in a superconductivity model using U-centers.
Authors: Apushkinskaya D. , Apushkinskiy E. , Popov B. , Saveliev V. , Sobolevskiy V. , Krukovskya L.
Published: February 2021

According to the theory of U-centers, a strong electron-lattice interaction can lead to the fact that the binding energy of two electrons at certain temperatures is higher than the energy of their Coulomb repulsion and, therefore, the possibility of the formation of Cooper pairs appears. If these pairs move coherently without falling apart, then superconductivity occurs. In this work using the Hubbard Hamiltonian and applying the secondary quantization method, we calculate the temperature dependence of the superconducting gap Δ = Δ(T). A temperature dependence of the HTSC resistance in the normal state demonstrates the features of both semiconductor and metallic behavior. It is known that high-temperature superconductors exhibit such properties only in the normal state. In the work presented now, using mathematical calculations, we discuss the presence of four regions of the HTSC phase diagram T = T(ν), where ν is the relative concentration of U-centers. The formation mechanisms of negative U-centers are not considered here. We only assume that they are present in HTSC.
Recent scaling properties of Bitcoin price returns
Authors: Takaishi T
Published: February 2021

While relevant stylized facts are observed for Bitcoin markets, we find a distinct property for the scaling behavior of the cumulative return distribution. For various assets, the tail index μ of the cumulative return distribution exhibits μ
≈ 3, which is referred to as ”the inverse cubic law.” On the other hand, that of the Bitcoin return is claimed to be μ
≈ 2, which is known as ”the inverse square law.” We investigate the scaling properties using recent Bitcoin data and find that the tail index changes to μ
≈ 3, which is consistent with the inverse cubic law. This suggests that some properties of the Bitcoin market could vary over time. We also investigate the autocorrelation of absolute returns and find that it is described by a power-law with two scaling exponents. By analyzing the absolute returns standardized by the realized volatility, we verify that the Bitcoin return time series is consistent with normal random variables with time-varying volatility.
Stochastic thermostats and temperature expressions
Authors: Samoletov A , Vasiev B
Published: February 2021

Molecular dynamics (MD) is in the core of fundamental research for a range of disciplines in natural sciences and is known for its applications in the design of new functional materials and the drug discovery. MD simulations are performed under certain thermodynamic conditions, typically at fixed temperature and pressure. The thermodynamic variables in the MD are modeled using equations that are called thermostats. Many different thermostats have been proposed. Recently (Samoletov A and Vasiev B 2017 J. Chem. Phys. 147 204106), we have shown that a range of thermostats can be derived in the framework of a unified approach based on the fundamental principles of statistical physics, so that the relevant dynamic schemes are based on the concept of temperature expression (in short, ϑ-expression). However, only a few specific ϑ-expressions have been used so far and reported in the literature. In this paper, we are using a wider set of ϑ-expressions and their mathematical properties that allow us to modify the known and offer new thermostats with improved computational efficiency and ergodicity. We focus on the Nosé-Hoover-Langevin stochastic scheme and extend it with additional temperature control tools. Simultaneous thermostatting of all phase space variables with minimal additional computational costs is an advantage of the modified dynamics.
How many shades of grey? On the proximity of density functional approximation to ab initio method via calculations of electric multipole moments
Authors: Xenides Demetrios , Karamanis Panaghiotis
Published: February 2021

The wide spreading of the application of Density Functional Approximation via its represtnatives, i.e. the Density Functionals (DFs), in almost all the fields of computational chemistry is globally witnessed. Therefore, it is believed that an evaluation of their performance in tedious calculations will offer useful insights to both users and developers. As such calculations we chose the prediction of electric multipole moments of first to fourth order, namely dipole (µ

α

/ea
0), quadrupole

(

Θ

α
β

/
e

a

0

2

)

, octupole

(

Ω

α
β
γ

/
e

a

0

3

)

and hexadecapole

(

Φ

α
β
γ
δ

/
e

a

0

4

)

. Values at both ab initio up to the highly accurate couple cluster with singles and doubles (reference values) as well as different flavors of DFs: hybrid, GGA, double hybrid, long range corrected, including dispersion correction, both hybrid and long range corrected and some from the Minessota family. The target set is a group of 10 molecules of C
2

ν
symmetry. The results were further analyzed with the root mean square deviation (RMSD) method as to find the method that is the most proximal to reference CCSD. The functionals PBE1PBE, CAM-B3LYP and B2PLYP seem to survive the proximity test, whereas B97D, M05, M06 and M08 provide results that are the least proximal to CCSD ones.
Mathematical modeling of elastic wave radiation during infinite screw dislocation bending vibrations
Authors: Dezhin V V
Published: February 2021

In this paper, we consider elastic waves radiation (acoustic emission) by infinite screw dislocation performing small bending vibrations under action of external influences. Braking of the dislocation vibrations by medium was not taken into account. To solve this problem, inverse generalized susceptibility of an infinite screw dislocation is written down. It is found that condition for the emission of elastic waves by a screw dislocation is satisfied for two ranges of wave vector component values along the dislocation line. For average value of energy emitted per unit time per unit length of the screw dislocation, corresponding expressions are obtained. Internal friction caused by radiation losses during bending vibrations of the screw dislocation is found.
Mathematical modeling of elastic waves radiation during screw dislocation segment oscillations
Authors: Dezhin V V
Published: February 2021

In this paper, we consider elastic waves radiation (acoustic emission) during oscillations of screw dislocation segment under action of external influences. The dislocation oscillations braking by medium was not taken into account. Elements of generalized susceptibility inverse matrix for the screw dislocation segment in nondissipative crystal are written. The energy emitted by the dislocation oscillator has been found. Using numerical calculations of the generalized susceptibility matrix elements for the screw dislocation segment, estimate of the radiated energy is obtained.
Astrophysical magnetohydrodynamical outflows in the extragalactic binary system LMC X-1
Authors: Papavasileiou Th V , Papadopoulos D A , Kosmas T S
Published: February 2021

In this work, at first we present a model of studying astrophysical flows of binary systems and microquasars based on the laws of relativistic magnetohydrodynamics. Then, by solving the time independent transfer equation, we estimate the primary and secondary particle distributions within the hadronic astrophysical jets as well as the emissivities of high energy neutrinos and γ-rays. One of our main goals is, by taking into consideration the various energy-losses of particles into the hadronic jets, to determine through the transport equation the respective particle distributions focusing on relativistic hadronic jets of binary systems. As a concrete example we examine the extragalactic binary system LMC X-1 located in the Large Magellanic Cloud, a satellite galaxy of our Milky Way Galaxy.
Research of Water Body Turbidity Classification Model for Aquiculture Based on Transfer Learning
Authors: Zheng Jianhua , Yang Gaolin , Huang Yanxuan , Liu Leian , Hong Guihuang , Qiu Zitao , Liu Shuangyin
Published: February 2021

Fodder, fish manure, and pond sludge will seriously affect the turbidity of the water body in aquaculture. How to quickly and online judge the turbidity of the water body is very important for realizing efficient, low-cost, and accurate control of aquaculture. In view of the shortcomings of traditional detection methods, a transfer learning method based on ResNet deep learning network model is proposed to realize water body turbidity classification, and two transfer learning methods of parameter partially frozen and completely unfrozen are designed based on ResNet18.Subsequently, the turbidity data set of aquaculture water quality was constructed, and the data set was enhanced by image cropping, image flipping, random scaling, and other methods. The experimental results show that when all parameters are not frozen, the transfer learning method can achieve the best class effect, and the accuracy rate is 0.9686, which can provide an effective method for online detection of aquaculture water body turbidity.
Research on document similarity calculation and detection based on deep learning
Authors: Xing Cui , Yang Yan , Luo Jian
Published: February 2021

In view of the complexity of traditional document similarity calculation methods and the problem of large error, a document similarity calculation and detection method based on deep learning is proposed. The effective subtree matching method is used to calculate the similarity of document feature sequences, and the frequency of feature items is obtained. In order to reduce the complexity of document similarity detection, a deep learning method is used to classify documents. The keywords in the document are extracted, and the similarity calculation and detection results are obtained by calculating the similarity between keywords. The experimental results show that the calculation error of the proposed method is low and the results are reliable, which fully shows that the method is effective and feasible.
Fog Removal Algorithm for Geographic Images Using Generative Adversarial Nets
Authors: Li Dong , Nie Ying , Liu Yang Cheng
Published: February 2021

In order to improve the imaging effect of geographic images in a complex weather environment. This paper designs a geographic image defogging algorithm by using the generation countermeasure network to improve the clarity of the image. Firstly, the imaging mode and degradation process of the foggy image is studied in this paper, and the atmospheric scattering model is established to simulate the degradation of the image. Secondly, according to the data characteristics of image defogging, an optimized image defogging model is generated based on Conditional Generation Adversarial Nets (CGAN), so that the image output by the model is closer to the real image. The experimental results show that the image defogging model designed in this paper has good contrast and color richness, which can effectively solve the problem of image degradation in fog. Moreover, the reduction of paired data sets will reduce the measurement index of the geographic image defogging model. This paper provides a reference for the study of geographic image defogging.
Evaluation Grade Prediction Method with Limited Information from Experts
Authors: Wei Xiao , You Hongliang , Cheng Jiajun , Gao Qiang , Tang Shanhong
Published: February 2021

With the development of artificial intelligence, using machine learning methods to build evaluation models has attracted more and more attention. However, training anevaluation model often needs a lot of labeling samples annotated by experts. It is very difficult to get enough labeling data with a limited group of experts. This paper proposes a method to learn the evaluation model with limited information from experts. This method has two stages. In the first stage, we build a large training set with ordinary people by comparing every two samples. After that, we train a Siamese Network with the paired comparison data set to get a score for each sample. In the second stage, we map the scores to evaluation grades with the help of experts. In the experiments, we use the UCI wine quality data set to evaluate our method. Experimental results demonstrate that we get a basically equivalent accuracy (0.5% decrease) with only1.45% samples labeled byexperts.
Application Development of Dance Pose Recognition Based on Embedded Artificial Intelligence Equipment
Authors: Zhou Qian , Wang Jun , Wu Peng , Qi Yue
Published: February 2021

With the development and popularization of artificial intelligence and machine vision, human posture recognition has become one of the most important research directions in the field of machine vision, but its application research in dance movement recognition is in its infancy, especially in the field of Chinese classical dance. In view of this situation, the application of artificial intelligence in basic hand position recognition of Chinese classical dance. Including analysis of basic hand position of Chinese classical dance, comparison of human posture recognition models, recognition and comparison of basic hand position of Chinese classical dance on NVIDIA embedded devices using TensorFlow deep learning framework, and combination of traditional culture and artificial intelligence technology.
ACGANs-CNN: A Novel Intrusion Detection Method
Authors: Zhou Qi , Tan Minsheng , Xi Hewen
Published: February 2021

In this paper, an intrusion detection model (ACGANs-CNN) method based on GAN and CNN fusion is proposed for the reasons that unknown attack sample data cannot be provided in training samples, the number of training samples is limited, and known attack sample types account for less such small sample data. The model converts network traffic data into grayscale images, generates the same proportion of attack samples by generating the counter network, ensures the uniform distribution of attack samples in the training set, and introduces the gradient penalty function to improve the stability of the training model. Secondly, CNN is used to better extract sample features. In order to prevent overfitting, the nonlinear activation function Relu and Dropout method are introduced. At the same time, the convergence speed of the model is accelerated, and the detection efficiency of the model is improved. Attention is introduced to highlight the key features and to classify samples based on these key features. In this paper, the KDDCUP99 data set is used for model evaluation. Experimental results show that this algorithm (ACGANs-CNN) has stronger model training stability, higher quality of generated fake samples, and better feature extraction effect in small sample data. Its detection rate and accuracy of attack types are significantly higher than that of traditional machine learning algorithms such as SVM, KNN, RF, and other CNN models.
Research on key Technology of Classroom Teaching Evaluation Based on Artificial Intelligence
Authors: Zhou Huatao , Lu Jie , Huang Yulan , Chen Yu
Published: February 2021

There are many defects in current classroom teaching evaluation, which is not conducive to the implementation of quality education. Therefore, the AI-based classroom teaching evaluation technology is proposed, using artificial intelligence theory, from the perspective of serving quality education, analyzing the problems in teaching evaluation, exploring effective classroom teaching evaluation, and ultimately promoting the development o f students. Starting from the theory of artificial intelligence, it discusses the necessity of constructing a multi-evaluation system for the teaching process, which improves students’ language application ability and humanistic quality, and applies artificial intelligence theory to construct teaching from the aspects of evaluation methods, evaluation methods, evaluation objects, and evaluation subjects. Evaluation System. Experimental results show that this technology can accurately evaluate teaching effects and improve teaching quality
Implementation of Artificial Intelligence Algorithm In Embedded System
Authors: Lu Jie , Zhou Huatao
Published: February 2021

The development of embedded technology directly affects the development of Internet of Things, and the development of artificial intelligence brings great convenience to people’s life. Based on this, the implementation method of artificial intelligence algorithm in embedded system is studied. Based on embedded system hardware configuration and the embedded system software running algorithm is optimized, simplifying the operation steps of the embedded system, improve the effect of embedded system, to strengthen the research of embedded technology, and increase investment in manpower and material resources in embedded system research, learning western advanced technology, and the embedded technology is applied in the Internet of things, to improve the effect of artificial intelligence algorithm in the application of the embedded system. The experimental results show that the artificial intelligence algorithm is applied to the embedded system to improve the system performance effectively.
Construction of Foreign Language Curriculum System in Engineering majors in the Age of Artificial Intelligence
Authors: Deng Chaoqun
Published: February 2021

China has a wide range of engineering students sources and high quality. but because of the low level of internationalization of education, it is unable to meet the needs of international talents in economic globalization. Artificial intelligence has stepped into the education industry, bringing opportunities for foreign language teaching to cultivate engineering talents with an international perspective. In the era of artificial intelligence, the construction of foreign language curriculum system in engineering majors should set higher-level curriculum teaching objectives, develop innovative teaching system and improve the teaching assessment of high-challenge courses guided by OBE education concept in order to improve the quality of personnel training.
Ship Classification by the Fusion of Panchromatic Image and Multi-spectral Image Based on Pseudo Siamese LightweightNetwork
Authors: Li Mengyang , Sun Weiwei , Du Xuan , Zhang Xiaohan , Yao Libo
Published: February 2021

The current rapid development of the remote sensing satellite industry provides a large amount of image data for ship classification tasks. Aiming at the problem of insufficient feature extraction of single source image, this paper designs a lightweight ship classification model based on the fusion of panchromatic image and multispectral image of pseudo Siamese network to extract image features more fully. First, establish a multi-source remote sensing image ship target classification dataset MPFS (MS and PAN Ship image Fusion Classification Dataset); secondly, send panchromatic images and multispectral images to the network through different convolutional layers, thendesign a multi-level feature extraction network for panchromatic images and an adaptive feature extraction network for spectral imagesrespectively; finally, concatenate the features along the channel dimension and send them to the classification network.
Multi-Turn Response Selection in Retrieval Based Chatbots with Hierarchical Residual Matching Network
Authors: Zhang Zhuo , Zheng Danyang , Gong Ping
Published: February 2021

Response selection in retrieval-based chatbot aims to find the most relevant response in a candidate repository given the conversation context. A key technique to this task lies in how to measure the matching degree between conversation context and response at rich semantic information. In this paper, we propose a hierarchical residual matching network (HRMN) to fully extract and make use of the rich semantic information in the conversation history and response for themulti-turn response selection task. We empirically verify HRMN on two benchmark data sets and compare against advanced approaches. Evaluation results demonstrate that HRMN outperforms strong baselines and has a distinct improvement in response selection.
Translation between High- and Low-frequency SAR Images using Cycle-Consistent Conditional Adversarial Network
Authors: He Daquan , Jin Tian , Song Yongkun , Wu Chen
Published: February 2021

The bi-frequency (high- and low) synthetic aperture radar (SAR) images cannot be directly compared due to their distinct statistical properties. To diminish their statistical difference, we manage to translate the bi-frequency SAR images into one another. Therefore, we propose a cycle-consistent conditional adversarial network to achieve the goal. The cycle-consistency criteria in the Cycle GAN and the conditional generation adversarial networks in the Pix2Pix are integrated to construct the cycle-consistent conditional adversarial network. Experiments on Ku-band and P-band SAR images validate that our method outperforms Cycle GAN and Pix2Pix.
Goal-Directed Navigation with A Hybrid Planning Strategy Through Learning from Vision
Authors: Zhou Xiaomao , Du Bing , Huang Tao
Published: February 2021

The existing hippocampal modeling approaches rarely span the wide functionality range from processing raw sensory signals to planning and action. This paper presents a goal-directed navigation system consisting of two planning strategies. The first one is a biologically inspired neural planning and navigation model that is related to learned representations of place and HD cells. It is responsible for generating spatial trajectories leading to the neighboring area of the target. The place and HD cells are trained unsupervisedly from visual images using a modified slow feature analysis (SFA) algorithm. To interpret their functional role in navigation, a planning network is trained to predict the neural activities of place and HD cell representations given selected action signals. Recursive prediction and optimization of the action signals generate goal-directed activation sequences, in which the continuous states and action spaces are represented by the population of place-, HD- and motor neuron activities. Furthermore, a second planning strategy relying on visual recognition is proposed and performs target-driven reaching on a local scale for finer accuracy. Experimental results show the effectiveness of the proposed system.
Knowledge Graph Completion Based on Graph Representation and Probability Model
Authors: Liu Luwei , Zhu Cui , Zhu Wenjun
Published: February 2021

Knowledge Graph provides an effective scheme for unstructured knowledge on the Internet. but, to a large extent, the lack of information in the knowledge graph restricts the application of the knowledge graph. Therefore, knowledge graph completion has been an attractive research area. In recent years, many researchers apply graph convolutional neural networks to knowledge graph embedding so as to solve this problem, including R-GCN, SACN et al. The current state-of-the-art SACN uses graph convolutional neural as an encoder to make more accurate embeddings of graph nodes, and uses a convolutional network as a decoder, leading a good performance of link prediction task. In this work, we produce a novel graph representation model based on SACN---property graph convolutional network called PGCN. PGCN treats the knowledge graph as a property graph, regarding the initial embedding vector of entities and relations as the property of nodes and edges. What makes the model different is that it introduces the node clustering before convolving the nodes, so that the graph takes into account the importance of neighbors when aggregating the neighbors of nodes. We adopt the probability model Conv-TransE proposed by SACN for the modeling of relation of graph, Conv-TransE takes advantages of ConvE and use probability method which greatly improves the efficiency of the experiment and avoids the construction of the corrupted triplet. We conduct experiments on the standard datasets WN18RR and FB15k-237, and demonstrate that our new model achieves better performance.
A Method For Selecting Command Nodes Oriented To Succession Command Scenarios
Authors: Li Yawen , Dong Baoliang
Published: February 2021

Succession command is a command method to change command in the face of uncertain factors or irresistible damage. Alternative command nodes need to complete command transfer to achieve uninterrupted command. Facing the alternative scenarios of multiple command nodes, this article proposes a method for selecting the replacement command nodes, researches the command and control capability requirements, analyzes the command capability of the command organization, extracts the key performance indicators of the command and control system to construct an evaluation index system, and uses the analytic hierarchy process to determine the index rights. Based on the value, a hybrid TOPSIS method and grey relational analysis evaluation model is proposed to evaluate and sort the candidate command nodes. Experiments are carried out through the simulation data of the command system. The experimental results prove that the method can effectively select the replacement command nodes and provide preparation for the subsequent replacement command.
Application of Meta Learning in Face Recognition
Authors: Peng Sisi , Zheng Kaining
Published: February 2021

Now is the information age, artificial intelligence rises under the background of this era, but in the field of artificial intelligence, deep learning stands out in machine learning with its excellent learning ability and is rapidly applied to face recognition engineering. However, the success of mainstream deep learning often depends on a lot of training data and training time. This paper proposes to use meta-learning technology to realize face recognition. metalearning is to use the prior knowledge and experience to guide the learning of new tasks and has the ability to learn so that it can avoid the phenomenon of overfitting in the case of only a small number of samples. We regard the data set as a task, that is, meta-train task and meta-test task, using the MAML algorithm [1] fine-tuning gradient model based on prior knowledge to obtain optimization parameters to realize face recognition. Experiments show that deep learning neural networks using meta-learning technology can achieve higher accuracy and rate than ordinary face recognition neural networks. In the experiment, the recognition rate of the face can reach more than 92.6%. It has more intelligence and has made a contribution to the development of face recognition technology.
Research on The Innovation of Higher Vocational English Teaching Mode based on OBE Concept under The Background of Big Data
Authors: Li Hui , Jiao Li
Published: February 2021

This essay makes a series of explorations on Vocational school’s English teaching mode on the basis of OBE concept and on the age of big data, analyzes its research significance, research contents and main innovations, Therefore, it puts forward the innovation path of Higher Vocational English teaching mode based on OBE concept. The final conclusion is that the h igher vocational English teaching mode based on the OBE concept under the background of big data can promote college English teaching, and can promote vocational schools to be employment-oriented by using big data
Neural Image Completion Based on Label Differentiation
Authors: Zhu Xianyi , Ye Zixiao , Tan Guanghua , Duan Sheng , Liu Yaohui , Liu Dong
Published: February 2021

Deep learning based image completion methods are generally based on three technologies, namely the auto encoder based method, the generation of adversarial networks based method and the recurrent network based method. However, many methods have very single results. In order to obtain the diversity of the completion results, this paper proposes a neural image completion method based on label differentiation, called LD-PICNet (Label Differentiation PICNet). This method can not only generate a complete image with clear and good semantic information, but also actively edit the tags on the generated results to maximize the diversity of the output. Specifically, this paper introduces an auxiliary classifier, which uses a single label of the image ground truth to reconstruct the image, and uses the label to actively increase the difference of the latent vectors during image completion to achieve variability of output. In addition, this paper also introduces a depth-weighted loss function based on information entropy. Different data sets are used to conduct experiments. The model’s ability to complete different types of targets is tested. The results show that this method has the ability to generate diversified results, and it has higher clarity than the other methods.
Detection of sleep apnea syndrome by CNN based on ECG
Authors: Bai Yunxiang , Zhang Luqiao , Wan Dechao , Xie Yu , Deng Hanghang
Published: February 2021

Sleep apnea syndrome is a sleep disease that may lead to sudden death. Long term apnea syndrome can cause chronic cerebral hypoxia, hypertension, cardiovascular and cerebrovascular complications. At present, PSG is the most reliable method for diagnosis. But the diagnosis of PSG is complex and expensive. Electrocardiograph(ECG) and portable medical equipment have been widely used nowadays, which makes the acquisition of ECG signal more and more popular and convenient. In this paper, a convolution neural network based on ECG signal is proposed to predict apnea syndrome, the accuracy and sensitivity of this CNN model for apnea syndrome classification are 94% and 88% respectively. The results show that this method has the advantages of low cost and low complexity.
Research on CAN Bus Anomaly Detection Based on LSTM AndResNet
Authors: Song Jiaqi , Li Fei , Li Ruxiang , Li Yalin , Zhou Qiyang , Zhang Jiayan
Published: February 2021

With the rapid development of intelligent network vehicles, the safety of the vehicle network is taken seriously increasingly, the CAN as the most widely used car on-board network, its security problem has become one of the most important problemsin intelligent made car development, the communication of CAN bus network characteristic and the existing safety problems and analyzes the common means of attack, on the basis of this puts forward the anomaly detection model based on LSTM And ResNet, through the experimental results show that this method CAN effectively detect deception attack.