bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optical control of high-density alkali atom vapor in antirelaxation coated cells
Authors: Tsvetkov S , Gateva S , Mariotti E 
Published: April 2021

Abstract
In this work, we report on our investigations on LIAD (Light Induced Atomic Desorption) aimed at achieving high density of alkali atoms vapor in a coated cell at room temperature. The experimental results show the possibility to reach a density up to the limit when the medium becomes optically thick by applying highly efficient homogeneous illumination. The photon reabsorption mechanism prevents the precise evaluation of the density by measuring the absorption of a probe laser beam, but there is clear evidence that densities can be achieved higher by two orders of magnitude than the thermodynamic equilibrium value.
High-beam-quality sealed-off master oscillator – power amplifier system oscillating in the visible spectral range on atomic copper transitions for micromachining in research and technology
Authors: Kostadinov I K , Astadjov D N , Yankov G P , Popova L T , Slaveeva S I , Fedchenko Yu I , Temelkov K A 
Published: April 2021

Abstract
High-beam-quality sealed-off master oscillator – power amplifier system is developed and studied based on a copper bromide vapor laser and oscillating on atomic copper self-terminating transitions. A detailed study on the laser beam divergence is carried out demonstrating the capability of CuBr vapor laser systems to emit diffraction-limited laser beams. Precise microprocessing of various materials, such as optical grade fused quartz, Si, stainless steel, is also implemented.
Longitudinal radiation force of laser pulses and nonlinear optics of moving neutral particles
Authors: Kovachev L M 
Published: April 2021

Abstract
For a long time, transverse and longitudinal optical forces have been used for non-contact manipulation of small individual particles. The following question arises: What is the impact of these forces on an ensemble of a thousand particles in continuous media? The aim of this work is to find analytical expressions of the radiation force and potential densities arising from a laser pulse propagating in dielectric media. This allows us to find an effective averaged longitudinal real force at the level of the laser pulse spot. The force obtained is proportional to the initial pulse energy and inversely proportional to its time duration. In the femtosecond region, the force becomes strong enough to confine neutral particles into the pulse envelope and translate them with the group velocity in gases. In solids, as silica for example, the longitudinal force of a femtosecond pulse is significantly greater than the molecular forces. Thus, the fine ablation in silica with short pulses may be due to this longitudinal force, which breaks down the molecular bonds. Additionally, after confinement into the pulse envelope, the moving particles produce new linear and nonlinear effects.
Enhancing fault tolerance of wireless sensor networks by changing topology
Authors: Xin Qiangwei , Guo Xinming 
Published: April 2021

Abstract
Wireless sensor networks work by multi hop mode. The increase of intermediate forwarding nodes will bring the packet loss rate increase. In addition, a node which has fault will affect the entire link work. The average number of forwarding node influences the fault tolerance of wireless sensor networks. In order to ensure that wireless sensor networks can work smoothly and prolong the lifetime, the reducing of average number of forwarding node is necessary, namely reducing the average number of hops. The reducing of average hop number of whole network also reduces the packet loss rate and the energy consumption of nodes. The proposed algorithm in this paper cuts down the hops and improves the fault tolerance of wireless sensor networks.
A Study on Brand Visual Design Based on Internet Technology
Authors: Yang Zhe , Zhang Ziyan 
Published: April 2021

Abstract
The Internet era has brought great changes to people’s lives, it promotes the continuous development of the economy, but also promote the overall upgrade of the brand, so that the brand visual image communication has more media. Under the era of the Internet technology development, the requirement of brand visual design is higher than before, it can not only limited to the visual level, at the same time it has to combine with modern new way of media communication based on the brand culture, and to enhance brand experience. Brand visual design can be feasible, at the same time promoting the development of brand implementation. This paper will mainly discuss the development path of brand visual design under the background of Internet.
Research on Online Platform of Multimedia Teaching Based on Network Engineering
Authors: Chen Xiaoxu , Li Nan 
Published: April 2021

Abstract
With the rapid development of the Internet, the business models of all walks of life have changed a lot, and the application of multimedia technology and network technology has become more and more extensive. In the current educational environment, the traditional one-to-one teaching model no longer exists. Multimedia technology and network technology have been widely used in the teaching process. It has established a new teaching environment for distance education institutions, making the online teaching platform more convenient and realizing more possibilities.
Traffic Flow Forecasting Analysis based on Two Methods
Authors: Liu Yingxin , Zhang Na , Luo Xinggang , Yang Min 
Published: April 2021

Abstract
Along with the rapid growth of China’s economy and the continuous improvement of the national living standard, the number of private cars traveling on the road is gradually increasing, and the traffic congestion problem has become the focus of people’s attention. In order to solve this big problem, the most important thing is to improve the development of public transportation, conduct scientific and reasonable public transportation scheduling, and realize the green transportation. The prediction of public transportation information is the key point of public transportation scheduling. In this study, the part of public transportation information, traffic flow, is selected for research, and the collected historical information is preprocessed and characterized. Using the weather factor as the input characteristic, the historical traffic flow is divided into 15-minute interval and 1-hour interval data sets, and two models, gradient boosting decision tree and wavelet neural network, are used to predict the traffic flow of road sections. The experimental results show that the gradient boosting decision tree model based on the 15-minute interval has high prediction accuracy and good generalization performance.
Research on Disambiguation Attributes Based on Semantic Hierarchical Model
Authors: Yu Linlin , Wang Zhenxia , Su Li , Wei Dan 
Published: April 2021

Abstract
Feature extraction concepts and statistical classifiers began to be used in computer-aided detection and diagnosis, and many different feature learning techniques such as principal component analysis, image block clustering and dictionary methods are very popular. However, in the case of less labeled data, the application and effect of deep learning are restricted. The research content of this paper is based on items such as nodule multi-semantic and fine-grained classification and rating research of WGAN synthetic oversampling learning, multi-semantic assisted disambiguation research based on multi-level association transfer learning, and multi-label incremental learning research for small sample semantic hierarchical structure. Moreover, based on the synthesis of various semantic samples, multi-semantic association transfer, and multi-label incremental learning methods, a set of effective methods and application mechanisms for intelligent auxiliary semantic hierarchical structure disambiguation are formed by describing the problem definition, proposing research methods, and discussing the experimental effects of the proposed methods in detail.
Automatic segmentation of retinal edema in optical coherence tomography based on deep neural networks
Authors: Lu Rongrong , Wang Lidan , Duan Shukai 
Published: April 2021

Abstract
A multi-task network framework based on convolutional neural networks improvements is proposed for automatic segmentation and classification in three different lesion areas of retinal OCT images. We propose a dilated convolutions module to capture fine-grained semantic information and introduce dense skip connections to integrate features of multiple scales and different semantic levels. Furthermore, we use a joint loss function that balances the relative size of the data labels and the improved generalization of the network. The validity and reliability of our proposed method are trained and verified on OCT datasets. Extensive experimental results demonstrate that the average Dice coefficient of the three lesion areas is 2.61% higher than the most commonly used U-net model in the field of medical image segmentation and reaches 0.81501.
Sememe-based Topic-to-Essay Generation with Neural Networks
Authors: Luo Dan , Ning Xinyi , Wu Chunhua 
Published: April 2021

Abstract
Topic-to-Essay generation task aims to generate topic related and coherence text based on user input. Previous research generates text solely based on the input words and the generation results in not satisfactory. The traditional methods using semantic relationships of words from corpus to guide the model, which made the model rely heavily on the corpus. In this paper, we propose a sememe-based topic-to-essay generation model(S-TEG), which integrates sememes from external knowledge graph HowNet with input topic words to guide the model. In order to prevent introducing noise, we elaborately devise measuring the similarity of the non-current topic words to filter sememes information. The experiment results demonstrate that our approach achieved 4.10 average score in subjective evaluation and a 3.60 BLEU score, which shows that our model is able to generate text that is more coherence, topic-related and fits the daily logic.
Evaluation and Emotional Analysis of Mobile Phone Sales of JD E-commerce Platform Based on LDA Model
Authors: Xue Jingfeng , Li Jing , Han Yujia 
Published: April 2021

Abstract
With the rapid development of the Internet economy, the online transaction volume of e-commerce platforms has soared, and the purchase experience shared by users has become particularly important. How to quickly and effectively extract the required information from a large number of user reviews plays an important role in the decision-making of both consumers and businesses. This article conducts sentiment analysis on JD business platform Huawei mobile phone user comment information. First, this paper use crawler methods to collect user evaluation text information, and perform data preprocessing such as deduplication and cleaning of the collected raw data; then perform word segmentation on the processed text content, and filter stop words and worthless words; use the method based on emotional dictionary matching to analyze the sentiment tendency of the text. Finally, using the LDA topic analysis model, LDA topic analysis is performed on the sentiment classification text, and the topic words are extracted to obtain valuable internal information, and reasonable marketing strategies are proposed for the business.
Scientific Measurement and Visualization Analysis of International Omni-channel Retailing Research (2011-2020)-Research on Knowledge Graph-Based on Co-word Analysis
Authors: Chen Xiaoxia , Li Zhongbin 
Published: April 2021

Abstract
With the development of digital technologies such as cloud computing, big data, and 5G, omni-channel retailing has become the direction of the retail industry reform. To understand the research context, hot spots, and future development trend of global omni-channel retailing in the past decade and provide a reference for relevant research, this paper collects relevant articles in the WOS database from 2011 to 2020, uses the co-word analysis method to make statistics and analysis on keywords, and uses Bicomb, Spss and Excel software to conduct cluster analysis and strategic coordinate analysis on omni-channel retailing research. The results show that the research on omni-channel retailing logistics and supply chain, the research on omni-channel integration and management, and the research on omni-channel digital marketing and consumer behavior are the three hot areas of omni-channel retailing research. In the future, the mixed methods combining quantitative researches (such as machine learning, artificial neural network, and quantitative model) with qualitative research (such as case research and interviews) can be used from a mixed perspective of consumers and retailers to conduct in-depth research on the operation mechanism and the decision-making mechanism of omni-channel retailing and the interdisciplinary research combining omni-channel and organizational management.
Detection of Hysteroscopic Hysteromyoma in Real-Time Based on Deep Learning
Authors: Zhao Aihua , Zhang Jian , Wang Shixuan , Wang Yan , Zhu Xin , Shen Wenfeng , Wang Wenwen 
Published: April 2021

Abstract
Hysteromyoma is the most common benign tumor in women. By the age of 50, 70% of women have one or more uterine fibroids, and about 30% of them have symptoms and need treatment [1]. In hysteroscopic surgery, doctors’ inexperience and fatigue will reduce the accuracy of hysteromyoma diagnosis. In this paper, a hybrid model based on YOLOv3(YOLO) Network and DCGAN network(DCGAN) is proposed to detect hysteromyoma in real time to assist doctors in diagnosis and reduce subjective randomness. The real-time detection speed of the hybrid model reaches 25FPS, and the accuracy rate reaches 91.73%, which meets the requirements of clinical application and improves the diagnosis efficiency of hysteromyoma.
Experimental research on mechanical performance of the lightweight composite slabs
Authors: Cai Ruiyan , Du Jingjing , Yang Li , Pan Chengsheng 
Published: April 2021

Abstract
Aimed at reliability model in the satellite communication system, the index weight of subjective problem, this paper proposes a method based on entropy weight - the combination of cloud model method, combining the expert’s subjective analysis in the process of modelling and model their own objective data to get a more complete set of index weight, and USES the fuzzy quantitative evaluation method for satellite communication system reliability model of the final evaluation results. The validity and feasibility of the algorithm are verified by an example, and the application of entropy weight-cloud model realizes the uncertain evaluation between the evaluation index and the evaluation level, which provides a new idea and method for the evaluation of the reliability model of satellite communication system.
Experimental study on dynamic and static characteristics of Differential Relay Valve
Authors: Wang Zhe , Li Guangmin , Liu Min , Liu Jun , Hong Yin 
Published: April 2021

Abstract
This study aims to investigate the dynamic and static characteristics of a Differential Relay Valve (DRV). The fact that DRV has a complex physical structure leads to more nonlinear coupling property. Thus, theoretical analysis and simulation are not enough when studying its characteristics. In this article, the static and dynamic performance of a DRV is studied based on a novel test bench with NI Labview platform, and both the input and output gas pressure are detected in real time. The outcomes of the experiment show that both the service brake pressure and the parking brake pressure affect the output gas pressure of the DRV, and determined by the larger one. Moreover, the valve has good dynamical follow-up response performance.
Multi-objective Optimization Based Deep Reinforcement Learning for Autonomous Driving Policy
Authors: Wang Hao , Wang Zhongli , Cui Xin 
Published: April 2021

Abstract
End-to-end autonomous driving approach seeks to solve the problems of perception, decision and control in an integrated way, which can better adapt to the new traffic scene. Due to the diversity of traffic scenes and the uncertainty of the interaction among surrounding vehicles, the design of autonomous driving policy is challenging. Many current methods manually design the corresponding driving policy for different traffic scene, resulting in suboptimal solutions and the maintaining is hard. Most of the existing deep reinforcement learning (DRL) methods can’t work well in the complex urban traffic scenes because of the sensing and simple driving policy. In this paper, to extend the adaptability of the SAC-based method, we proposed to take the multiple sensor data as input, and a VAE network was used to enhance the quality of training data for SAC-based DRL method. A multi-constraints reward function for SAC-based driving policy training is designed, which account for the errors of transverse distance, longitudinal distance, heading, velocity and the possibility of collision. The multiple sensor data include the original RGB image captured by forward-view camera, a 3D LiDAR and the bird’s-eye view map resulted from the perception processing, the mixed inputs could enrich the capability of scene perception. The proposed approach is validated with the multi-vehicle traffic simulation built with CARLA[1]. The results showed that the simulated vehicle could adapt to more challenging traffic scenes, like passing intersections and turning in crowded urban scenes, etc with the driving policy generated by the proposed method, and its performances are obviously outperformed against the existing similar methods.
Constructing the Electronic Countermeasures System Architecture Based on Case-Based Reasoning and Bayesian Inference
Authors: Xu Jia , Chen Jia Jun , Hou Yang Xiao 
Published: April 2021

Abstract
In order to promote the quality of electronic countermeasures system architecture, this paper present a methodology for constructing the electronic countermeasures system architecture based on case-based reasoning and Bayesian inference. The methodology described history cases by CPN model, established the largest similarity case and feasibility space by combining the CPN color set and k-means algorithm, and then proposed the optimizing orderings of variables of Bayesian by the LFS (Legal Firing Sequence) of CPN model and maximum information entropy, learned the Bayesian networks structure by K2 algorithm using the result of orderings of variables. At last, this paper concluded the electronic countermeasures system architecture tactics and discussed the way of reasoning for constructing the electronic countermeasures system architecture.
Implicit generalized predictive control method for high-speed train
Authors: Sun Tao , He Lijuan 
Published: April 2021

Abstract
The running process of the train which can high speed running is a typical nonlinear process, and with the higher and higher velocity of the train which can be driven extremely fast, the characteristics of the nonlinear will be stronger than before, which leads to higher requirements for the design of the “Automatic Train Operation” driving system. Aiming at the characteristics of high-speed train running speed fast, complex operation process and strong nonlinearity, the mathematical model establishment and the speed tracking control method are studied. This article uses a data-driven modeling method. Firstly, the actual operation data of high-speed trains based on traction and braking forces are divided into two parts: traction and braking. Then, the linear models of the traction and braking models of the train, which can be driven very fast, are established respectively. These two models adopt the recursive least square method. Then use implicit generalized predictive control to achieve tracking control of high-speed train speed. The simulation results based on the operating process data of CRH380AL is given that this method can make the requirements that belong to high-speed train operation.
Complex dynamics behavior analysis of a new chaotic system based on fractional-order memristor
Authors: Qi Yongwei , Wu Chaojun , Zhang Qi , Yan Kai , Wang Haohan 
Published: April 2021

Abstract
In this paper, we propose a fractional-order active memristor based on fractional-order calculus, and its characteristics are analyzed. Combined with the new memristor, a fractional-order chaotic system is constructed. We study the complex dynamic behavior of the chaotic system by phase diagram, bifurcation diagram and Lyapunov exponential spectrum. The results show that with the change of fractional order and system parameters, the system will exhibit complex dynamic behaviors such as period, double period and chaos. With the change of initial values, the fractional system also exhibits the multistable characteristics of coexistence of different attractors. The research results show the complex dynamic behavior of fractional-order chaotic system.
On the Asymptotic Expansion of Monomial in Singular Partial Differential Equations
Authors: Zhao Xuan 
Published: April 2021

Abstract
People find that formal solutions to a type of doubly singular ordinary differential equations (systems) summable in a monomial of two variables, and that formal solutions to many singular partial differential equations were multisummable. So summability theory of formal power series in several variables is of great importance to the research of formal solutions to partial differential equations. This enriches the achievements in the research of formal solutions to differential equations. It is a application of monomial summability theory of formal power series.
Detection of False Data Injection Attacks Based on Kalman Filter and Controller Design in Power System LFC
Authors: Zhu Rujun , Huang Chongxin , Deng Song , Li Yichen 
Published: April 2021

Abstract
False data injection attacks pose a great threat to the safe and stable operation of power systems. Therefore, the detection and defense of false data injection attacks in LFC systems is becoming more and more important. First, based on the equivalent model of the LFC system and the Kalman filter algorithm, this paper proposes a false data injection attack detection method; secondly, a robust controller is used to reduce load disturbances according to the operating characteristics of the LFC system; then the idea of switching links is proposed to reduce the impact of cyberattack on the system; finally, MATLAB/Simulink software is used to carry out simulation tests. The test results verify the effectiveness of the false data injection attack detection method and switch link defense method proposed in this paper.
Effective entanglement recovery via operators
Authors: Ming Fei , Shi Wei-Nan , Fan Xiao-Gang , Ye Liu , Wang Dong 
Published: April 2021

Abstract
Entanglement is one of essential quantum resources in quantum information processing, playing an important role during quantum communication and computation. While any system unavoidably interacts with its surrounding environment, this thus will result in decoherence or dissipation effect. With this in mind, a natural question arises: how to recover quantum entanglement under decoherence inducing by the environmental noises. In this work, our attempt is to propose an effective strategy to optimally achieve entanglement recovery. Specifically, we derive a nontrivial condition satisfied by any operation U, i.e.

(

U
⊗
I

)

†

(

U

̃

⊗
I

)

=
f

(

U

)

(

I
⊗
I

)

, which will definitely recover the entanglement of an arbitrary two-qubit system, and more importantly offer an explicit physical explanation towards the intrinsic mechanics of the entanglement recovery. By means of derivations, we obtain the appropriate operational strength to realize optimal entanglement recovery through taking advantage of our proposed methods. As illustrations, we apply some local operations complying with our presented strategy on validly protecting entanglement, including

PT

-symmetry operation, quantum weak measurement and quantum reversal measurement.