bluuebunny/crossref_metadata_2025 huggingface dataset:
Isospin triplet A=14: search for states with enhanced radii
Authors: Demyanova A S , Danilov A N , Ogloblin A A , Starastsin V I , Dmitriev S V , Leonova T I , Goncharov S A , Belyaeva T L , Trzaska W H , Gurov Yu B
Published: December 2020

This article is devoted to study of isobar-analogue states 1− in triplet A=14: 14C-14N-14O. Previously signs of neutron halo in the 1−, 6.09 MeV state of 14C were obtained by two independent groups. In this article we propose to study neighbouring nuclei 14N and 14O using the Modified diffraction model (MDM) method and the method of Asymptotic normalization coefficients (ANC). Methods were applied to experimental differential cross sections of 14C(α,α)14C scattering and reactions 13C(3He,d)14N and 14N(3He,t)14O. MDM and ANC gave practically similar within errors radii for the studied 1− states: the 6.09 MeV state in 14C – 2.7±0.1 fm, the 8.06 MeV state in 14N – 2.7 ± 0.1 fm, the 5.17 MeV state in 14O – 2.6 ± 0.2 fm. Moreover, the signs of proton halo in the 1− state of 14N were obtained for the first time.
Gas gain study of a large-size multilayer Micromegas
Authors: Dubinin F , Iengo P , Sekhniaidze G , Zhukov K
Published: December 2020

The results of the study of a large-size, multilayer Micromegas detector with X-ray source Cd-109 are discussed. The detector consists of 4 layers, each of them is a Micromegas detector with resistive anode and strip-based read-out structure. The gas volume of the chamber is split to 4 parts interconnected in 6 points. Results of the measurements of the gas gain curves and the 1st Townsend coefficient as well as the E_amp/E_drift characteristics are presented and discussed. The detector presented a non-negligible gas leak, affecting the reported results. The influence of the leak on operation of the module is estimated.
Development of Si Beam Position Detectors for NA61/SHINE experiment
Authors: Makhnev A , Guber F , Serebryakov D , Pulawski S , Kowalski S
Published: December 2020

The NA61/SHINE experiment at the CERN SPS is undergoing a major upgrade during the LHC Long Shutdown 2 period (2019-2021). The upgrade is essential to fulfil the requirements of the new open charm measurement program. In this program detector will operate at a beam intensity increased by a factor of 10, which requires an upgrade of current Beam Position Detectors (BPDs). New BPDs should monitor lead and proton beam intensities with up to 105 Hz rate. In this paper, progress on design and development of the new BPDs based on Si strip detectors, its front-end and readout electronics, as well as integration with the NA61/SHINE DAQ is presented.
Firmware developments on the TileCoM for the Phase-II Upgrade of the ATLAS Tile Calorimeter
Authors: Gololo M G D , Carrio Argos F , Martins F , Garcia B M
Published: December 2020

The Tile Computer on Module (TileCoM) mezzanine board is one of the auxiliary boards of the Tile PreProcessor (TilePPr) for the Phase-II Upgrade of the readout electronics of the ATLAS Tile Calorimeter (TileCal). This board will be responsible for system monitoring and configuration by interfacing the Trigger Data Acquisition (TDAQ) system and the TilePPr. Features include configuration and monitoring of the Advanced Telecommunications Computing Architecture (ATCA) carrier and Compact Processing Module (CPM) onboard sensors through I2C and Gigabit Ethernet. This contribution presents firmware developments on an embedded Linux for the ZYNQ System-on-Chip (SoC) targeting an Avnet Ultra96-V2 ZYNQ UltraScale+ MPSoC evaluation board. This test bench will serve as a basis for the development of the main functionalities of the TileCoM mezzanine board to interface the TilePPr with the Detector Control System (DCS) and the TDAQ-I system of the Tile Calorimeter.
A gamma-ray imaging camera for NORM radioactivity detection
Authors: Altomare C , Di Venere L , Fanchini E , Giordano F , Loparco F , Morichi M , Pantaleo F R , Spinelli P , Swiderski L
Published: December 2020

Naturally occurring radioactive materials (NORM) and technologically enhanced naturally occurring radioactive materials (TENORM) consists of materials enriched with radioactive elements, found in the environment, with concentrations over the ambient natural radioactivity average, such as industrial wastes and extraction byproducts. We designed a camera for gamma-ray imaging and radionuclide identification based on the coded mask technique. The camera proposed is a compact, lightweight instrument, ideal for real-time analysis, with a low power consumption, suitable for industrial process and ambient monitoring. We built a prototype consisting in 16 CsI(Tl) scintillators coupled to photo-multiplier tubes (PMTs) with a digital readout. We used a 7 × 7 mask composed by transparent and opaque tiles to encode radioactive gamma-rays sources image and use a reconstruction algorithm for decoding. The system was first tested using free gamma-ray radioactive sources placed at a fixed distance from the mask and than, the same sources, was placed inside an industrial nuclear waste drum to test shielding and detection limit. We will also show the results with a NORM igneous rock sample and we will try to identify the radioactive sources after a estimation of the count rate over the background, the test was carried out in lead chamber to shield the natural laboratory background. The performance of the prototype camera in terms of energy and spatial resolution with respect the detection time will be shown.
Time response simulation of a “shashlyk”-type calorimeter as applied to ECAL MPD / NICA
Authors: Skobliakov A V , Kulikov V V , Bulychjov S A , Kantsyrev A V , Martemianov M A , Matsyuk M A , Tyapkin I A
Published: December 2020

In the frame of the GEANT4 toolkit, a numerical simulation of light collection from the tower of the EMC MPD / NICA detector was carried out. The influence of reflective coatings of the scintillator surface, the propagation of light in fibers, taking into account their multilayer structure and light reflection from their ends, as well as the effects of joining a fiber bundle with an MPPC photodetector are considered. The temporal forms of the light signal at the MPPC input were obtained taking into account only geometric effects.
Investigation of the EAS neutron component with the URAN array: first simulation and experimental results
Authors: Izhbulyakova Z T , Bogdanov A G , Bogdanov F A , Gromushkin D M , Pochestnev A D , Shulzhenko I A , Yurin K O
Published: December 2020

The URAN array was created in the Scientific and Educational Centre NEVOD (MEPhI). The URAN facility was designed to register neutrons that accompany EAS at the energies of the primary cosmic rays around and above the “knee”. It includes 72 detectors based on a thin inorganic scintillator for registration of the charged and neutron components of the EAS. The total area of the facility is about 103 m2. For the correct interpretation of the experimental data of the URAN, the response of the URAN facility to the passage of EAS was simulated using the CORSIKA7.6900 program and Geant4.10.5 software package. It gave a possibility to study neutron generation in the experimental complex building. Experimental data were processed and analyzed. Results of simulation and experimental data are presented.
A stratospheric and satellite CubeSat format probe for detecting relativistic runaway electron avalanches
Authors: Kurmasheva T A , Briukhanov K A , Arkhangelskaja I V , Arkhangelskiy A I
Published: December 2020

In planetary atmospheres, runaway electron avalanches could happen due to large scale electric fields, which accelerate electrons to energies about 0,1 – 10 MeV. This phenomenon is not fully understood. Nowadays, most of the satellite data is obtained on low orbits. However, runaway breakdown can also occur at altitudes less than 30 km. In this case, most of the radiation is scattered without reaching the satellites on high orbits. The formation of charged particles in the atmosphere can affect the results of numerous experiments. Therefore, it is important to have the most proper model of this phenomenon. Project goal is to create a stratospheric CubeSat format probe capable of detecting these events at an altitude of about 30 km and above. The purpose of the experiment is to observe changes in the fluxes of both high-energy electrons and radiation, as well as an analysis of possible correlations of the measured parameters. We developed a concept of the probe and created a detector prototype, consisting of a thick polystyrene scintillation counter wrapped in mylar and two SiPM SensL MicroSB-30035-X13 readout.
Domain wall thickness and deformations of the field model
Authors: Blinov Petr A , Gani Tatiana V , Gani Vakhid A
Published: December 2020

We consider the change in the asymptotic behavior of solutions of the type of flat domain walls (i.e. kink solutions) in field-theoretic models with a real scalar field. We show that when the model is deformed by a bounded deforming function, the exponential asymptotics of the corresponding kink solutions remain exponential, while the power-law ones remain power-law. However, the parameters of these asymptotics, which are related to the wall thickness, can change.
Interactions of η-meson in asymmetric nuclear matter
Authors: Kumar R , Kumar A
Published: December 2020

The interactions between η-meson and nucleons are studied by the unification of the chiral SU(3) model and chiral perturbation theory. The η meson-nucleon interactions for the next-to-leading order terms are derived by expanding the ηN interaction Lagrangian within the chiral perturbation theory. Using the chiral SU(3) model, we calculate the in-medium scalar density of protons

ρ
s
p

and neutrons

ρ
s
n

for different values of temperature T, isospin asymmetry I and nucleonic density ρN. Within the chiral SU(3) model the in-medium properties of nucleons are evaluated in terms of scalar-isoscalar fields σ, and ζ and the scalar-isovector field δ. The scalar densities of nucleons calculated using chiral SU(3) model are used as input in the equation of motion of η mesons obtained from chiral perturbation theory. We solve the equation of motion for sigma term

Σ
η
N
=
280
±
130

MeV

and scattering length aηN
= 1.025 fm for different values of asymmetry of the medium. We find an attractive mass-shift of the η meson in nuclear medium and the magnitude of this shift increases with increasing density. Finite isospin asymmetry of the medium causes less drop in the in-medium mass of η mesons. The negative mass-shift indicates the possibility of the formation of η-mesic nuclei.
Tuning of the primary vertex reconstruction algorithm in the BM@N experiment
Authors: Driuk A , Kakhanovskaya N , Merts S , Nemnyugin S , Roudnev V , Stepanova M
Published: December 2020

The BM@N (Baryonic Matter at the Nuclotron) is a working experiment at the NICA (Nuclotron-based Ion Collider fAcility) complex. The first physics runs with beams of argon and krypton (BM@N setup) as well as with beams of carbon (SRC setup - extension of the BM@N physics program) were carried in 2018. One of the prerequisites for physics analysis of experimental data is the existence of the primary vertex position estimation. Current report describes the proposed algorithm to reconstruct the primary vertex using the virtual planes method. The results of this algorithm for different targets, beams and trigger conditions are presented. The sensitivity of presented method to its parameters is considered.
Machine learning techniques for optimisation of track selection criteria
Authors: Altsybeev Igor , Andronov Evgeny , Prokhorova Daria
Published: December 2020

Application of machine learning (ML) algorithms in high-energy physics is evolving rapidly. In particular, they could be used for the optimization of track selection criteria in the analysis of experimental data on hadronic collisions. Using Monte Carlo simulations, one can train ML classifiers to separate correctly reconstructed primary tracks from secondary and fake tracks based on their features such as a number of clusters in TPCs, distance of closest approach to an interaction vertex etc. In this paper, we present the procedure of track selection optimization based on ML techniques and applied to EPOS1.99 simulations of proton-proton interactions obtained via Shine Offline Framework. With this approach, an increase of a fraction of the selected primary tracks and reduced contamination by the secondary tracks is obtained. In case of a complex geometry of an experimental facility like NA61/SHINE, improvement of track selection leads also to a widening of the kinematical acceptance.
The mechanism of domain walls and strings formation in the early Universe
Authors: Kirillov A A , Murygin B S
Published: December 2020

A classical dynamics of two real scalar fields within a model with a saddle point potential in (2+1)-space-time is discussed. We show that in this model, solitons may be formed both as domain walls and strings in three dimensional physical space. The formation and evolution of these field configurations are considered.
Measurement of single-spin asymmetry for charged pions in the SPASCHARM experiment at U70 accelerator
Authors: Abramov V V , Kalugin N K , Mochalov V V , Moiseev V V , Morozov D A , Novikov K D , Nurusheva M B , Oreshkov A O , Rykov V L , Semenov P A , Vasiliev A N
Published: December 2020

The SPASCHARM experiment at U70 aims an exploration of a fundamental problem in modern particle physics, the role of spin in strong interactions. At present, the first physics data have been accumulated at a negative 28 GeV beam, interacting with a frozen polarized proton target. The SPASCHARM wide-aperture spectrometer can detect both charged and neutral particles in the large solid angle with 2π azimuthal coverage in the fragmentation region of beam-particles. It covers the kinematic region of non-perturbative quantum chromodynamics (QCD), where the theoretical predictions are difficult and unreliable due to the full strength quark confinement at low and medium momentum transfers. In this report, the current status of the experiment is presented. The algorithm for reconstructing charged tracks is discussed along with its results on real data and the comparison with the Monte Carlo (MC) simulations. Based on a real track reconstruction, the statistical errors for the measurements of single-spin asymmetries in inclusive charged pion production are estimated. The data analysis is in progress.
Research on routing method of Multipath D2D Communication based on forward cellular network
Authors: Qu Huijie , Zhao Zhi
Published: December 2020

Traditional communication route selection methods find the shortest communication path based on graph theory, ignoring the influence of communication parameter changes on routing selection, resulting in excessive consumption of communication resources. In order to solve the above problems, a multi-channel D2D communication routing method based on Forwarding cellular network is proposed. Based on the establishment of D2D cooperative forwarding mechanism in cellular network, the mathematical model of multi-channel D2D routing is constructed by adding QoS index. In D2D, the multi-channel routing of D2D is calculated by mathematical model. The results show that the proposed routing method not only has low transmission delay, but also can control the resource loss within 15% with good reliability.
OTG: A Gateway for Cybersecurity in the Context of OPC UA PubSub Pattern
Authors: bu Tianyu , Liu Jiahao , Huang Jinfeng , Sun Zhigang
Published: December 2020

Under the background of industrial intelligence, OPC UA PubSub mode is strongly supported by Industry 4.0 as a protocol designed to meet the communication requirements of industrial control level. With the gradual opening of industrial control network, the PubSub security model in OPC UA protocol alone cannot meet the new security requirements under the background of OT and IT integration. According to the specification, we analyze the possible threats, impacts and countermeasures that PubSub may face in OPC UA deployment environment, and emphasized that PubSub security model is difficult to protect resource-constrained industrial field equipment from the harm caused by DoS and other attacks. In view of the limited resources of industrial control network, this paper proposes that OTG gateway provides protection for PubSub service. Compared with traditional security gateway, OTG gateway can greatly reduce the consumption of industrial control network resources by network attacks. In addition, according to the characteristics of PubSub protocol, a DoS detection algorithm for this architecture is proposed. Compared with the traditional DoS detection algorithm, it has better applicability to PubSub protocol and can detect DoS attacks more accurately to reduce the impact on device performance. Experiments show that the DoS detection algorithm has 100% accuracy and 0.13% false positive rate, and can detect DoS attacks faster than the traditional detection algorithm.
Strategies for Improving the Effectiveness of English Translation Teaching in Higher Vocational Colleges Based on Data Mining
Authors: Chen Jintian
Published: December 2020

At present, most vocational colleges have set up English majors accordingly, and translation is a compulsory course for English majors. Although some concrete measures have been taken to implement the teaching mode and method of translation teaching, there are still some problems in the practice of translation teaching. Higher vocational students have some misunderstandings in English translation, which requires teachers to improve teaching methods and promote students’ initiative to participate in teaching. How to change the position of translation teaching in higher vocational English teaching and improve the level of English translation teaching is a hot issue for all English teachers. Based on an in-depth analysis of the characteristics of English translation teaching in higher vocational colleges, this paper expounds in detail the strategies for improving the effectiveness of English translation teaching in higher vocational colleges based on data mining.
Detection of Spike-type Stall of Axial Compressors Based on Dilated Causal Convolutional Neural Networks
Authors: Zhao Hongyang , Quan Fuxiang , Xia Weiguo , Sun Ximing
Published: December 2020

An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as spike-type stall inception in axial compressors is observed in aero-engine. In this paper, a deep dilated causal convolutional neural network(CNN) named as WaveNet is applied to spike-type stall inception detection and prediction in time-series data of axial compressors. WaveNet can implement fast anomaly detection and spike-type stall prediction in long-time term series data. Furthermore, a single WaveNet can be trained to capture and learn the time-domain statistical characteristics of different spike-type stall inception training data with equal fidelity. The trained WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of spike-type stall detection approach based on WaveNet.
Economical CMM Universal Minimal I++DME System Design
Authors: Liu Kai , Mu Hua , Pang Changtao , Bai Yuewei
Published: December 2020

With the development of motion control technology in recent years, more and more low-priced motion control cards have been adopted by small and medium-sized CMM manufacturers due to their cost-effective quality. However, most of these cards are not packaged as the standard I++DME. As a result, the CMM equipment has a disadvantage of compatibility. In addition, I++ is a tremendous superset instruction, that can support from basic measurement to scanning measurement and rotary table measurement and many other complex commands. So packaging the economical CMM to support all I++ standard instructions is entirely unnecessary for a cost-effective reason. So this article wants to pick out the necessary I++ instructions to build a minimal I++DME that can support manual or automatic measurement.
A topology-based optimization method for software-defined satellite network control
Authors: Qiao Wenxin , Lu Hao , Li Zhiwei , Chen Xingkai , Shi Runze
Published: December 2020

Satellite network is gaining significant attention, and software-defined networking architecture is more and more popular as applied in many traditional networking fields. In order to meet the diversified user service requirements and the continuously expanding application scenario requirements, the research of software defined satellite network combining satellite network and software definition has gradually become a research hotspot. Aiming at the problem of how to optimize the control function of software-defined satellite network, this paper proposes a control optimization method based on topological structure. By quantifying the controllability of the network structure and eliminating the special characteristic structure, the controllability of the network is improved, and then the control function of software-defined satellite network is optimized. The simulation results show that the controllability of the network is improved by the method.
Energy-Efficient Joint Computation Offloading and Resource Allocation in Multi-User MEC Systems
Authors: Meng Yao , Dai Janxin
Published: December 2020

Mobile edge computing (MEC) is envisioned as an emerging paradigm to enable energy-constrained and computation-limited user equipments (UEs) to offload various computation tasks to the edge of mobile networks in order to save energy consumption and prolong the battery life of UEs. In this paper, we consider a multi-user MEC system where each UE has a computation task to be processed locally or offloaded for remote processing. Specifically, we investigate the task offloading problem with the aim of minimizing the energy consumption of UEs via jointly optimizing the task admission decision, the transmission power, local computing and edge computing capacities. To solve this nonconvex problem, we transform it into four subproblems and propose a low-complexity algorithm by solving subproblems in an iterative manner. Simulation results demonstrate that the proposed algorithm requires lower energy consumption than the existing benchmark schemes.
An effective method for computer network anomaly detection
Authors: Wang Dongpo , Zhao Chengli , Zhang Xue , Liu Boyu , Li Xiang , Yi Dongyun
Published: December 2020

With the increasing network threat, network anomaly detection has become a very challenging and indispensable task. In this article, we propose an anomaly detection algorithm through modeling computer network as temporal network. The active subnetwork is extracted from the original computer communication network and then projected to an undirected weighted network series, finally the abnormal behavior patterns of network series are detected based on eigenvectors. Data test experiment shows that the proposed algorithm has achieved very good results.
Research on the Application of Deep Learning in Text Generation
Authors: Zhou Shuohua
Published: December 2020

In recent years, deep learning technology has made great progress, and text generation based on deep learning has received extensive attention. In the form of complex and diverse information, text information is a mainstream form of data, and its number is growing very fast. How to quickly and accurately locate and use effective information from massive text data has become an urgent research problem in the field of text information extraction. The deepening of deep learning technology has broken through the dependence of traditional natural language generation technology on templates. It can automatically learn the input to output mapping from the data to form an end-to-end solution and reduce the degree of human participation. It enables the generation system to generalize in a wider field, and can generate more free text under the given conditions according to the needs. This paper explores the text generation method based on deep neural network, conducts research work on the text summary task, designs a generative summary generation method based on improved cluster search, and conducts experiments. Experimental results show that this method is effective.
A Distilled BERT with Hidden State and Soft Label Learning for Sentiment Classification
Authors: Wei Shuyong , Yu Defa , Lv Chenguo
Published: December 2020

BERT is a pre-trained language model. Although the model is proven to be highly performant in a variety of natural language understanding tasks, its large size makes it hard to implement in practical situation where computing resource is limited. In order to improve the model efficiency of BERT for sentiment analysis task, we propose a novel distilled version of BERT. It distills knowledge from the full-size BERT model, which serves as the teacher model. The distilled model efficiently learns the last hidden state and soft label of the teacher model, which are different from previous models. We use distillation learning objective that is able to effectively transfer knowledge from the original big model to the compact model. Our model reduces BERT model size by ∼40%, but retains ∼98.2% of performance in sentiment classification task. Our model achieves promising results in SST-2 sentiment analysis, and outperforms previous distilled model.
AFCGDroid: Deep Learning Based Android Malware Detection Using Attributed Function Call Graphs
Authors: Lu Tong , Liu Xiaoyuan , Chen Jingwei , Hu Naitian , Liu Bo
Published: December 2020

With the rapid development of the mobile network, large numbers of Android malware emerge and pose a serious security threat to Android users. The function call graphs (FCG) extracted from Android application involves permissions, API calls, and structure semantics. Leveraging FCGs has great potential for Android application detection. In this paper, we propose AFCGDroid, an approach based on attributed function call graph (AFCG), to detect Android malware. The nodes of FCGs are divided into internal nodes and external nodes in our work. AFCGDroid extracts FCGs through static analysis and then generates AFCGs by labeling its internal nodes with the external nodes. To handle the graph data, graph embedding based on deep learning is proposed to embed the AFCGs into low dimension vectors. We evaluate AFCGDroid in malware detection achieving a 99.6% accuracy in a publicly available dataset, Drebin.
Improved SVM classification algorithm based on KFCM and LDA
Authors: Zhang Xiaoyan , Wang Mengjuan
Published: December 2020

To address the problem that SVM is sensitive to outliers and noise points, in order to improve the classification accuracy of SVM, this paper introduces fuzzy theory and intraclass dispersion theory, proposes an improved SVM classification algorithm, uses KFCM and LDA to filter the data set, and selects reasonable training samples, thereby reducing the number of wild points and noise points in the training sample, and thus reducing its impact on the classification effect of the classification model. Compared with the traditional SVM, the algorithm in this paper considers the impact of training samples on the classification effect, introduces fuzzy theory and intra-class dispersion, and eliminates the wild points and noise points in the training samples that affect the classification accuracy of the classification model. Experimental verification shows that the classification accuracy of the SVM classification model trained by the filtered training samples is higher than that of the SVM classification model without the trained training samples.
The Impact of Artificial Intelligence on Vocational Education and Countermeasures
Authors: Hui Feng
Published: December 2020

Under the influence of manual functions, vocational education is facing unprecedented challenges and opportunities, and it also increases the employment pressure of vocational education graduates. Due to the emergence of artificial intelligence, the labor market has stricter requirements for employees, and graduates with high professional quality and high comprehensive level will be selected. Therefore, as a vocational education college, it should correctly understand the trend of artificial intelligence, attach great importance to the influence and role on the development of vocational education, connect artificial intelligence with the reform and innovation of vocational education, and scientifically and rationally use artificial intelligence teaching methods. It is used in daily teaching to cultivate high-quality talents for the country and society in line with the development of the times. This article will analyze the impact of artificial intelligence on vocational education and countermeasures, hoping to improve the teaching quality and level of vocational education.
Research on Medical Image Registration Based on Graphic Neural Network Reinforcement Learning
Authors: Dong Zhejia
Published: December 2020

On the basis of traditional medical image registration methods, aiming at the shortcomings of rigid body images, sequential rigid body images and non-rigid body images registration methods, this paper proposes a new method of medical image registration application based on graphic neural network reinforcement learning. Reinforcement learning adjusts the strategy from the actual system learning experience, and it is a process of gradually approaching the optimal strategy, mapping the image from high-dimensional gray space to low-dimensional feature category space; Then, the elastic registration of mutual information images is carried out in the feature class space. The advantage of this method is that it does not need extra supervision information, which effectively saves manpower and material resources. So as to make an accurate diagnosis or make a proper treatment plan.
The Role of Emotion in Designing Self-tracking Visualization: Where Avatars are Better Choice than Dashboard
Authors: Wu Xue , Liao Han-Teng , Wang Zijia
Published: December 2020

The interface design of self-tracking technology still has a lot of room for improvement. At present, self-tracking products with the dashboard as the main interface face challenges in negative user experience and high abandon. In this paper, we try to improve the user experience of such technologies by paying more attention to the role of the user’s emotions on the visualization and enhance their willingness to continue to use. Based on our previous study on people-centered self-tracking system design, we think the avatars should be a better choice than dashboards of self-tracking technologies. In order to detect whether avatars can bring more positive emotions than the dashboards, and to reduce abandon, we designed and implemented three phases of usability testing: a questionnaire of popular self-tracking applications in the market, a self-tracking product emotional research based on avatar and dashboards, and a questionnaire based on self-tracking APP prototype with avatar template. Through these usability testing, we obtained results that users are more tend to adopt the avatar one. This paper then proposed results that 9 emotions are significantly different when using these two different interface designs of self-tracking visualization. With such results of research, two implication for principles of sustainable interaction design of self-tracking applications had been proposed in this paper as 1) Understandability; 2) Performance optimizations. We hope to provide a new form of visualization for the interface design of self-tracking technology and complement usability research results of such design.
Fast 3D reconstruction based on UAV image
Authors: Pang Qiaoyu , Gui Jianjun , Li Jing , Deng Baosong
Published: December 2020

3D reconstruction for outdoor large environment, especially with high efficiency and robustness, has triggered increasing attention in both civilian and military scenario. This paper proposes a rapid method to perform 3D reconstruction using openMP multithreading strategy. The GPS information will be adopted to determine the adjacent relationship between images. The map will be processed through mesh blocks to save the computational time, and will be finally optimized by the mesh dissecting algorithm to remove holes. The experimental results show that the processing speed has been increased as five times faster than the methods based on openMVG and openMVS with better reconstruction outputs.
An Island Remote Sensing Image Segmentation Algorithm Based on A Fusion Network with Attention Mechanism
Authors: Chen Tianyuan , Wang Hongfei , Liu Hao , Wu Peng
Published: December 2020

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, in view of the problems that remote sensing images contain richer ratio information and complex background interference, we propose an island remote sensing image segmentation algorithm based on a fusion network with attention mechanism, called AFU-Net. The network is built on the basis of FC_U-Net[1]. An attention mechanism is added to pre-weight the shallow features before deep-shallow layer feature fusion, in order to enhance the response capability of the target features, suppress the background interference and improve the segmentation accuracy of the network. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of AFU-Net are greatly improved compared to FC_U-Net, and are also superior to other three state-of-the-art methods, U-Net, FCN, SegNet, which indicates the effectiveness of our method.
Synthesis of 3D pronunciation trajectories of lips in Mandarin Based on HMM model
Authors: Ye Yuanzhi , He Xiangzhen , Zhang Yihao , Hu Yerong , Zeng Jia
Published: December 2020

In the era of information technology, human-computer interaction tendings to be multi-modal, and lip animation simulation driven by pronunciation actions has attracted more and more attention. Starting from Mandarin Chinese, this paper establishins a model of the trajectory of the lips during the pronunciation process. The model is based on a motion capture technology, proposes a database collection a processing method, extracts acoustic parameters and text information to establish an HMM prediction model of pronunciation action parameters, and synthesizes the pronunciation movement trajectory, and the average percentage error with the real trajectory is less than 3.42%. The results show that the predicted pronunciation action parameters can effectively synthesize the lip pronunciation movement trajectory, and the use of lip animation can enhance language understanding.
Knowledge aided expert system for failure analysis of electronic components
Authors: Yaru Cui , Kui Zhang , Lijing Yin , Shengbiao An , Jie Huang
Published: December 2020

Aiming at the importance of failure analysis of electronic components, the complexity of the knowledge system and the high dependence of experts, this paper conducts in-depth research on the application of expert systems. According to the characteristics of component failure analysis, the reasoning fault tree analysis engine is innovatively designed, and the active knowledge assistant technology is introduced to assist analysts to obtain failure mechanism information in real time. The low threshold and high output of failure analysis of electronic components are realized, which fills the gap at home and abroad.
A Robust Facial Landmark Detection in Uncontrolled Natural Condition
Authors: Wang Ping , Zhou Qianyu , Zhao Yucong , Zhao Shumin , Ma Jiquan
Published: December 2020

Facial landmark detection in uncontrolled natural condition is a challenging task due to illumination, occlusion, expression, posture, blur and other factors. In order to make a stable and accurate prediction, a novel data augmentation strategy based on random mask is used to obtain an efficient training data set. Our work is motivated by the ability of artist’s spatio imagination from part to whole. The artist can find the corresponding key points according to the incomplete image, so as to realize the whole from the part. In the experiment, the corresponding incomplete image is first generated based on the random mask and the complete image, and then the incomplete image is fed into the neural network to fit the mapping between the incomplete image and the facial landmarks. In order to evaluate the performance, a simple regression convolutional neural network based on the improved VGG16 [1] was trained, and the latest technical results were obtained. Furthermore, our method is tested over both complete and incomplete face images. Extensive experiments reveal that our model can work well on different human face data set.
Multi-modal human-computer interaction system in cockpit
Authors: Ren Jie , Cui Yanyan , Chen Jing , Qiao Yuanyuan , Wang Luhui
Published: December 2020

In order to explore new human-machine interaction methods, a set of multi-modal human-machine interaction coordinated control system is proposed, which realizes the basic flight control based on the change of the pilot’s field of view, touch control, voice control and other information obtained from multi-mode coordinated control. This system introduces a new type of human-computer interaction into the cockpit application on the basis of the human-computer interaction interface for flight driving, and has carried out research on multi-mode collaborative control system, mainly including eye movement interaction, touch interaction gesture Interaction and voice interaction. Finally, this project formed a multi-mode cooperative control software and hardware system.
Automatic high-precision measurement technology of special-shaped surface parts based on robot arms
Authors: Jiang Zhiguang , Zhang Lijian , Qiu Tiecheng
Published: December 2020

In order to meet the requirements of large-scale, special-shaped surface, high-precision surface measurement of product parts, an automated, high-precision robot arm measurement system based on industrial robots is proposed. According to the D-H method, the relative posture conversion relationship between the robot arm and the elastic probe is discussed. In addition, on the premise of considering the contact deformation between the elastic probe and the measuring surface, the coordinate data of the measuring point is obtained based on the probe deformation model. At the same time, the accuracy calibration is performed by means of visual photogrammetry, and the validity of the coordinate measurement data of the profiled surface is guaranteed.
Intelligent obstacle avoidance algorithms for autonomous underwater vehicle
Authors: Liu Hui , Guo Yinjing
Published: December 2020

Real-time obstacle avoidance is the basis to ensure the safe operation of the autonomous underwater vehicle (AUV), and it also represents the intelligent level of AUV. The main factors that affect AUV obstacle avoidance are environment perception ability, obstacle avoidance decision-making ability, and trajectory tracking control ability. This paper starts from the perspective of obstacle avoidance decision-making ability, that is, obstacle avoidance algorithm. Firstly, we summarized the structure and influencing factors of AUV real-time obstacle avoidance. Then, we introduced in detail the research progress of AUV intelligent obstacle avoidance algorithms, including fuzzy logic algorithms, neural network algorithms, and reinforcement learning algorithms. And we analysed the improvement methods of each algorithm from three-dimensional underwater environment, ocean current, AUV motion characteristics, and dynamic obstacles. Finally, we prospected the development of the AUV intelligent obstacle avoidance algorithm.
Research on Load Balancing Control of Internet of Things (IoT) Link Based on Sparse Random Clustering
Authors: Wenxuan Gao
Published: December 2020

Aiming at the problems of poor channel balancing control ability and high output bit error rate in traditional IoT link load balancing control methods, an improved ant colony algorithm based load balancing control method for IoT link is proposed. This paper constructs the channel transmission model of IoT, uses sparse random clustering analysis method to detect the load characteristics of Internet of things links, and extracts the statistical characteristics of IoT link. The output balance control and interference suppression of IoT link are carried out by matching filter detection method, and the load balance of IoT link is fuzzy control combined with fuzzy PID control method. The simulation results show that the proposed method has good self-adaptability, strong channel equalization control ability and lower output bit error rate.
Modelling of particle acceleration in the pulsar wind nebulae with bow shocks
Authors: Petrov A E , Bykov A M , Osipov S M
Published: December 2020

The observations of the pulsar wind nebulae (PWNe) indicate the presence of efficient acceleration of positrons and electrons in these sources. The Fermi acceleration in the colliding shock flows can explain the observed hard synchrotron emission spectra of PWNe with bow shocks (BSPWNe). This may result in their maximal luminosities in the far ultraviolet range (FUV; 1250 — 2000 Å, ∼ 6 — 10 eV) due to the synchrotron emission of pairs rather than due to the thermal emission of the shocked interstellar matter. Fine spectroscopic observations of sufficiently bright sources with Hubble Space Telescope could be applied to distinguish between these two scenarios. In this paper we simulate BSPWNe flows structure with the relativistic magnetohydrodynamic code PLUTO, consider particle transport and their synchrotron emission for a number of BSPWNe. We calculate the synchrotron FUV luminosities of these BSPWNe and discuss the prospective of their observation in FUV. We also consider possible contribution of PSR J1741-2054 to the positron excess detected by AMS-02 and PAMELA.
Solar activity reconstructed on the basis of radiocarbon data and climate change from 17000 to 5000 BC
Authors: Kudryavtsev I V , Dergachev V A
Published: December 2020

The data on the content of cosmogenic isotopes 14C and 10Be in the Earth’s atmosphere at a time interval of 15000-5000 BC were analyzed. On Earth at this time there was a transition from the ice age to the Holocene. The reconstruction of Wolf numbers based on radiocarbon data is presented, which describes solar activity and takes into account changes in the Earth’s climate during the period under consideration. The increase in global temperature during the transition to the Holocene was accompanied by a sequence of warming (for example, Meiendorf warming) and cooling. The obtained reconstruction of Wolf numbers indicates that at separate time intervals: 13600-13000, 9300-9000, 8300-8000 BC the Sun was in a state of deep minima of activity. We also note the activity minima at ≈ 10700 and 7500 BC. The periods of extremely high activity of the Sun were also distinguished, so at maxima ≈ 12350 and 6715 BC. Values of Wolf numbers could exceed 200. The duration of the first of these periods was several hundred years, and at that time Meiendorf warming took place on Earth (≈12500-11850 BC). The question arises: are these events related?
Powerful Solar Proton Events of January 2005 and their impact on intensity of the stratospheric polar vortex
Authors: Veretenenko S
Published: December 2020

In January 2005, a series of powerful Solar Proton Events (SPEs) associated with an increase of flare activity on the Sun was observed. Increases of solar proton fluxes with energies 165-500 MeV allowing particles to reach stratospheric altitudes (∼30 km and below) were registered during the events starting on 15, 16 and 17 January. The strongest event, with particle energies exceeding 500 MeV, took place on 20 January and was accompanied by an increase of the neutron monitor counting rate (Ground Level Enhancement). The events under study resulted in a considerable increase of stratospheric ionization. In this work an impact of these events on intensity of the stratospheric polar vortex playing an important part in the mechanism of solar-atmospheric links is studied. A noticeable intensification of the vortex (increase of western wind velocity) was revealed at all the stratospheric levels in the course of the SPE series under study. It was shown that auroral activity (precipitations of auroral electrons generating bremsstrahlung X-rays) is also a possible factor of the vortex intensification. The obtained results provide evidence that ionization changes associated with powerful SPEs and auroral activity may influence the state of the stratospheric polar vortex on the day-to-day time scale. A possible cause of the vortex intensification seems to be temperature variations associated with changes of chemical composition of the polar stratosphere due to ionisation changes.
The effect of electron screening on the structure of crystals in degenerate stars
Authors: Kozhberov A A
Published: December 2020

The neutron stars crust and white dwarfs consist of a degenerate relativistic electron gas and atomic nuclei arranged into a crystal lattice. The type of lattice that ions form may depend on many parameters, for example, on the external magnetic field, temperature (T), and ion number density (n). In this paper, we consider the effect of electron background polarization on the type of lattice at T = 0. Two approximations are used to describe this background. If it is described by the Thomas-Fermi model, the influence of polarization is determined only by the parameter κTFa, where κ
TF is the Thomas-Fermi wave number, a = (4πn/3)−1/3 is the radius of the ion sphere and at κ
TF
a ⋦ 1 the bcc lattice has the lowest energy. On the other hand, if we use the random-phase approximation for the electron dielectric function, the energy of the static lattice will be a function of κ
TF and the relativistic electron parameter x
r. In this case, the bcc lattice possesses the lowest energy at κ
TF
a < 0.28 and any x
r, while at higher κ
TF
a the formation of other lattice in consideration (fcc and hcp) is possible.
The creation of a regression model of the Earth’s pole motion with a feature of dynamic prediction
Authors: Khairutdinova A N , Mubarakshina R R , Andreev A O , Nefedyev Y A , Demina N Y
Published: December 2020

This work is dedicated to the modern and relevant problem of predicting the Earth’s pole motion. Using regression modelling, we form a complex model, consisting of a set of optimal mathematical structures each describing the dependence of its step’s remnant on time. The comparison between the results produced in this paper with other works on the study of North pole dynamics has shown that the models obtained using adaptive regression modelling (ARM) approach allows predicting the Y-coordinate more accurately while conserving the accuracy of the X-coordinate. Our results confirm the promise of using the so called adaptive dynamic regressions developed currently for describing the Earth’s pole position’s dynamics. The ARM-approach compared to the classic methods for analyzing time series has a number of advantages: 1) an expansion of the concept of a mathematical model’s structure describing a certain dynamics could be performed; 2) the oscillations’ harmonics stable in time are isolated; 3) the accuracy of predicting changes over a certain time period increases several times, which has an important practical value.