bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved performance of fluorescent/phosphorescent hybrid white organic light-emitting diodes with enhanced chromatic stability utilizing bipolar interlayer
Authors: Chen Ruilin , Zhu Wenqing , Huang Lu , Luo Yun , Li Jun
Published: February 2021

Fluorescent/phosphorescent hybrid white organic light-emitting diodes (WOLEDs) employing a bipolar CBP interlayer with different thickness have been fabricated. The two-color hybrid WOLEDs at the optimal interlayer thickness exhibit a maximum efficiency of 11.47 cd A−1. Compared with the device without a CBP interlayer, the efficiency of the device is increased by 38.5% and the chromatic stability is also improved. This work found that appropriate thickness of the CBP interlayer with a higher triplet energy level can effectively block the Dexter energy transfer between the phosphorescent and fluorescent layers, which can reduce the energy of triplet in the exciton recombination zone and improve the efficiency of the device. Furthermore, the introduction of the bipolar interlayer with a suitable energy level adjusts the balance of exciton distribution, thereby achieving white light emission with good color stability. The present research could provide valuable strategy in developing hybrid WOLEDs with stable chromaticity.
Defect states and passivation mechanism at grain boundaries of zinc-blende semiconductors
Authors: Gu Hong-Yang , Yin Wan-Jian , Gong Xin-Gao
Published: February 2021

Grain boundaries (GBs) are significant in determining the electrical properties of polycrystalline semiconductors. However, the electronic structures and passivation mechanisms of polycrystalline semiconductors remain poorly understood. In this study, we systematically investigated the Σ3 (112) GB properties of several typical zinc-blende semiconductors via first-principles density functional calculations. We found significant differences of Σ3 (112) GB structures and properties between IV/III and V types, where dangling atoms formed new covalent bonds, and II–VI/I–VII types, where dangling atoms formed no new bonds. These different bonding configurations lead to different origins of defect states at GBs. We successfully designed a targeted doping approach to passivate such defect states for different types of semiconductors. We demonstrated the validity of the proposed approach in Σ3 (112) GB of the zinc-blende semiconductors. This work elucidates the defect states at GBs in common zinc-blende semiconductors, rationalizes diverse post-treatment approaches reported in previous experiments, and provides general guidance for defect passivation at the GBs of polycrystalline semiconductors.
Investigation of the non-equilibrium state of strongly correlated materials by complementary ultrafast spectroscopy techniques
Authors: Hedayat H , Sayers C J , Ceraso A , van Wezel J , Clark S R , Dallera C , Cerullo G , Da Como E , Carpene E
Published: February 2021

Photoinduced non-thermal phase transitions are new paradigms of exotic non-equilibrium physics of strongly correlated materials. An ultrashort optical pulse can drive the system to a new order through complex microscopic interactions that do not occur in the equilibrium state. Ultrafast spectroscopies are unique tools to reveal the underlying mechanisms of such transitions which lead to transient phases of matter. Yet, their individual specificities often do not provide an exhaustive picture of the physical problem. One effective solution to enhance their performance is the integration of different ultrafast techniques. This provides an opportunity to simultaneously probe physical phenomena from different perspectives while maintaining the same experimental conditions. In this context, we performed complementary experiments by combining time-resolved reflectivity and time and angle-resolved photoemission spectroscopy. We demonstrate the advantage of this combined approach by investigating the complex charge density wave (CDW) phase in 1T-TiSe2. Specifically, we show the key role of lattice degrees of freedom to establish and stabilize the CDW in this material.
Refractive index gas thermometry between 13.8 K and 161.4 K
Authors: Ripa D Madonna , Imbraguglio D , Gaiser C , Steur P P M , Giraudi D , Fogliati M , Bertinetti M , Lopardo G , Dematteis R , Gavioso R M
Published: February 2021

We have measured the refractive index of helium using a quasi-spherical copper microwave resonator at five different temperatures in the interval between the triple point of hydrogen at 13.8 K and the triple point of xenon at 161.4 K for pressures up to 380 kPa. From these results and additional measurements of the refractive index of neon near 54.4 K, 83.8 K and 161.4 K we determine the differences (T − T
90) between the thermodynamic temperature T and its approximation T
90 by the International Temperature Scale of 1990 (ITS-90). We have estimated the isothermal compressibility of copper and the effective compressibility of our microwave resonator by different methods including resonant ultrasound spectroscopy (RUS) and microwave measurements with helium at 273.16 K. We compare the results of these compressibility estimates and discuss the associated uncertainty. From the refractive index measurements, we estimate the second density virial coefficient of helium and neon which are found in good agreement with the ab initio calculations of the same properties.
Performance of two redundant quantum channels for single qubits under indefinite causal order
Authors: Cardoso-Isidoro Carlos , Delgado Francisco
Published: February 2021

Indefinite causal order has introduced novel procedures to improve the quality of quantum communication. The superposition of paths on a set of consecutive quantum channels has demonstrated enhancement in communication on some of them. This work models a generic quantum channel for single qubits in terms of Kraus operators in the form of Pauli operators. The output state going through the channel can be obtained analytically to then analyse the quality by means of the quantum fidelity.
Einstein field equations for Bose-Einstein condensates in cosmology
Authors: Roitberg Alice
Published: February 2021

In this work we consider the Gross-Pitaevskii equation for Bose-Einstein condensates (BECs) in a general Riemannian metric. Given initial conditions dictated by an external potential, we consider the free expansion of the condensate when the external potential is turned off. Focusing on the forces associated with the geometry of the initial configuration, we show how these are related to the Ricci curvature tensor and the Ricci scalar and we find an Einstein field equation governing the steady flow. Some important correlations between the study of defects in BECs and the appearance of cosmological singularities will be addressed, in particular the emergence of an effective Lorentzian spacetime geometry, which is what is needed to obtain Hawking radiation effects.
gravity with scalar field viscous fluid
Authors: Myrzakul S R , Imankul M , Arzimbetova M , Myrzakul T R
Published: February 2021

We focus on a viable teleparallel cosmological model with a scalar field in a flat Friedman-Robertson-Walker universe. The Lagrangian and equations of motion are obtained. The case of an inhomogeneous viscous dark energy is considered and the cosmological parameters associated with the viscosity parameter are determined. It is shown that when considering constant viscosity and state parameter, the Universe expands exponentially
Influence of clustering round magnetic nano-dots on magnetization reversal
Authors: Blachowicz T , Ehrmann A
Published: February 2021

Square and round magnetic nano-dots of varying dimensions exhibit a large amount of possible magnetization reversal processes, from domain wall nucleation and propagation to multi-vortex states. Clustering such single nano-dots, however, may strongly modify these magnetization reversal processes due to the interactions between neighboring particles. Here we thus investigate the difference between magnetization reversal processes in clusters of hexagonally arranged round nano-dots under different orientations of the external magnetic field in comparison with single particle behavior.
New open computational resource for plasma processes modelling
Authors: Zhilkin A S , Sychugov D Yu , Vysotsky L I , Zotov I V , Soloviev S Yu , Sadykov A D
Published: February 2021

The paper describes a new open access computing resource nfusion.cs.msu.ru. The resource includes modules for calculating equilibrium, vertical stability, plasma evolution, simulation systems for magnetic diagnostics, as well as a new algorithm for constructing three-dimensional tetrahedral meshes in areas of complex structure. The modules are integrated into a unified software environment designed for numerical support of experiments on tokamak installations. The possibility of accessing the calculation modules on the server via the Internet, data exchange and the issuance of calculation results in the form of files, figures, and tables has been implemented. The resource supports the simultaneous operation of several users and has an information support system in Russian and English.
Multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression and least angle regression
Authors: Xiao Dong , Ferlauto Michele , Song Liming , Li Jun
Published: February 2021

Polynomial chaos (PC) expansion meta-model has been wildly employed and investigated in the field of uncertainty quantification (UQ) and sensitivity analysis (SA). However, the majority of the multi-fidelity polynomial chaos expansion (MF-PC) models in the literature are still focused on using high-fidelity (HF) PC model to correct low fidelity (LH) model directly, without cross-correlation between PC models of different fidelities. To address this shortcoming, a multi-fidelity sparse polynomial chaos expansion (MF-sPC) model is proposed based on least angle regression (LAR) and recursive Gaussian process regression (GPR) in this paper. From low to high degree of fidelity, the autoregressive scheme in MF GPR is employed to construct MF-sPC model, in which the sparse polynomial chaos (sPC) model of each fidelity is built iteratively coupling with GPR, LAR and cross validation (CV), as gradually expanding the design of experiment (DoE) to reach a given CV error. This recursive scheme finally yields a MF-sPC model with highest fidelity which takes advantage of all sPC models of the lower fidelities. And the proposed MF-sPC model is validated by a test example in detail, and the results reveal that this MF meta-model performs outstanding both in convergence speed and model accuracy.
About Identifiability of Oil and Water Relative Permeability Curves and Reservoir Heterogeneity through Integrated Well Test Study
Authors: Zakirov E S , Anikeev D P , Indrupskiy I M , Tsagan-Mandzhiev T N , Alekseeva Yu V
Published: February 2021

Relative permeabilities (RP) play an important role in reservoir engineering. RP functions together with the permeability anisotropy coefficient predetermine waterflooding efficiency and oil/water ratio in production forecast. Traditionally multiphase flow parameters are estimated from core analysis data. But such measurements suffer from small representative elementary volume (REV) and limited characterization of reservoir properties. Therefore using core data in 3D reservoir modelling could lead to distorted description of actual flow conditions. Despite those functions (RP) could be history matched with assimilated production data, such a procedure would require long history of waterflooding. So the authors’ idea is to apply integrated well test study to estimate the displacement efficiency and/or relative permeability functions at downhole conditions. Well testing is traditionally applied in reservoir engineering for single-phase reservoir parameters estimation. Our approach is extended to multi-phase flow and is based on data collection at well bottomholes and subsequent data assimilation in flow model.

In this paper we summarize both features of mathematical problem statement and identifiability of multiphase parameters through inverse problem solution, but also discuss 10+ -year experience in interpretation of two-phase well testing using our technologies. To estimate multiphase parameters, we have to jointly consider complex data of well logging and dynamic data of well testing. Both data sets are used in the inverse problem quality criteria, where the least squares method has been applied. Forward problem corresponds to a 3D multiphase fluid flow model in porous media. The latter consists of continuity equation with multiphase Darcy equation instead of moment balances. For data assimilation modern methods of optimal control theory were successfully applied. For all synthetic test cases real reservoir parameters were accurately recovered through the use of forward simulation data.

In order to estimate reservoir heterogeneity, we applied a single-phase model to get the ratio of vertical to horizontal permeability. Data of well self-interference testing were used for vertical permeability estimation. Depending on the depth of reservoir pressure perturbation, reservoir properties could be properly inferred from observations. Two other options of 3D interference testing using vertical and horizontal wells are also presented.

In other words, all the problems considered are identifiable, and the level of their correctness is completely predetermined by the amount and quality of observed data. And transition to digital (intelligent) oil-and-gas production and closed-loop reservoir management [1] provides a possibility to remove discussed restrictions and all inverse problems considered should be accounted as fully identifiable.
Numerical studies of the saturated absorption resonances in a nonlinear spectroscopy of the degenerate atomic transitions
Authors: Saprykin E G , Chernenko A A
Published: February 2021

The physical processes forming the saturated absorption resonance spectra at a number of degenerate atomic transitions at their interaction with coherent optical fields and incoherent ield of spontaneous radiation of excited atoms are studied numerically. The features of behavior of nonlinear resonance spectra depending on the degree of degeneration and relaxation parametersof level transitions, character of polarizations and intensities, as well as direction of propagation of optical fields are analyzed.
Application of Convolutional Neural Networks in Neutrino Physics
Authors: Novotný Adam , Franc Jiří
Published: February 2021

Convolutional neural networks (CNNs), as a deep learning algorithm, have successfully been used for analyzing visual image data over the past years. As some of the physical experiments can produce image-like data, it is more than fitting to combine the interdisciplinary knowledge between high energy physics and deep learning. Especially in the domain of neutrino physics, the particle classification problem has played an important role and CNNs have shown exceptional results for the image classification. In this paper, results of application of CNN called SE-ResNET on Monte Carlo simulated image data is presented. These visual images are tailored to fit measured data from future Deep Underground Neutrino Experiment. The image classification focuses primarily on neutrino flavor classification, namely on classification of charged current (CC) electron νe
, CC muon νμ
, CC tauon ντ
and neutral current (NC); and secondarily on other characteristics of the image, such as whether the observed particle is neutrino or antineutrino. The results are important for further physical analysis of the neutrino experiment event, e.g. for study of neutrino oscillation.
Expected Performance of a Mobile e-nose platform for Real Time Victim Localization
Authors: Blionas S.
Published: February 2021

In this paper it is presented an analysis for the performance of the sensors of a mobile e-nose system that measures the atmospheric air, gas components’ concentrations (actually CO2 and O2), into a rubble void, where humans are entrapped, after a disastrous event that caused the collapsing of a building/construction. In this case, the entrapped humans in the void, they affect the air composition and temperature into the void. The existence of human(s) creates CO2 source(s), O2 sink(s), and heat source(s) as well (from body temperature). The existence of the entrapped humans constantly affect the air composition and temperature into the void and makes gas components’ concentrations dynamic. There will be presented the human CO2 source(s), and O2 sink(s) inside a cavity. The final goal is to estimate the concentrations of CO2 and O2 into the cavity as a function of time, having as parameters the number of entrapped humans. Estimating the concentrations it will enable the operator of a mobile e-nose system with sensors of a specific sensitivity and specificity, to determine the possibilities of detecting the presence of humans in a rubble void, nevertheless without considering the effect of weather conditions.
A new decomposition law for inverses modular multiplicative on (ℤ/ϱℤ)
Authors: Cortés–Vega Luis A.
Published: February 2021

In modular arithmetic, we find a valuable concept, the so-called “modular multiplicative inverse” (symbolized by MMI). In precise words, if ℤ/ϱℤ denoted the residue system modulo ϱ, the (MMI) of a
∈ ℤ/ϱℤ, if it exists, is a

−1
∈ ℤ/ϱℤ, such that

a
×

a

−
1

≡
1

mod
ϱ

, where p ≡ q mod ϱ is the usual modular representation of q
∈ ℤ/ϱℤ. This very special element it is one of the most wideley used mathematical concept in science, engineering and subject areas include the particle physics, the analysis and design of algorithms, data structures, databases, and computer architecture. In this paper, we establish a promising decomposition law for (MMI). In this respect, the main purpose of this paper it is shown that it is possible to express the (MMI) in terms of certain modular multiplicative inverse operators (MMIO) all, well-defined on Group of units pre-established. The main point to note here is that the result of this paper, which expands a result originally presented in [3], does not include any special conditions on the parameters involved.
On transient heat analysis of a two-step convective reactive cylinder
Authors: Lebelo R S , Waetzel M , Mahlobo R K , Moloi K C , Adesanya S O
Published: February 2021

This article looks at the analysis of heat transfer due to combustion of reactive materials in a stockpile modelled in a cylindrical domain. The transient heat analysis is studied through the investigation of effects kinetic parameters, embedded on the differential equation governing the problem, have on the temperature of the system. The temperature patterns due to the parameters effect, are depicted in graphs generated through Maple software. The complicated nonlinear partial differential equation is solved numerically using Finite difference method (FDM). The results show that parameters such as the order of reaction slow down the exothermic chemical reaction, while parameters like the reaction rate enhance the combustion process.
Summary of Target Detection Algorithms
Authors: Li Wei , Feng Xiang Sheng , Zha Kaiwen , Li Shuning , Zhu Hua Sheng
Published: February 2021

In recent years, the soaring development of CNN has facilitated the maturity of the Computer Vision Algorithm. This paper will briefly introduce some representative Target Detection Algorithm, and systematically analyze the underlying problems, the modified methods, and the prospective direction of the algorithm in accordance with its merits and demerits. It is generally divided into a single-stage detection model and a double-stage detection model in terms of whether candidate areas need to be extracted during target detection for further tasks. Featured with scale in the double-stage detection model, the algorithm is divided into single-scale detection and multi-scale detection based on whether it can appropriately integrate with a network structure, which enhances the accuracy of the network model towards small targets. Meanwhile, it can also be divided into anchor-base and anchor-free in a single-stage detection model on the basis of the anchor bolt. A predictable development of the Target Detection Algorithm will show to us in the future.
A Temporal Dual Graph Convolutional Network for Social Unrest Prediction
Authors: Jiang Yijie , Zhou Bin , Tu Hongkui , Gao Liqun
Published: February 2021

Social unrest is endemic in many societies. It is a vital task to predict social unrest events be-cause these events can lead to societal changes and endanger public security. We combine two data sources, using social media data as the historical texts and news media data as the ground truth of unrest events. We propose a temporal dual graph convolutional network (TDGCN), which extracts the contextual semantic information and the communication relationship between social network users from the historical texts and constructs two dynamic graphs to capture the implied semantic and temporal features. The TDGCN can predict the occurrence of unrest events. Experimental results on a specific data set of unrest show that the proposed method has better performance than other state-of-the-art social unrest event predictions.
The Management of Internet Content Products in the Era of Artificial Intelligence
Authors: Yu Lin , Lai Zhenli
Published: February 2021

Artificial intelligence affect the management of internet content products. This paper defines the internet content products, figures out the model of internet content production process, proposes a profit model of Internet content operation. It also summarizes the impact of artificial intelligence on the operation of Internet content products.
An Application of Automatic Text Revision for Power Defect Log
Authors: Li Hao , Deng Feng , Lu Jixiang , Zhang Tao , Li Hong
Published: February 2021

There are a large number of unstructured texts without data cleaning in the field of electric power. It is expensive to rely on manual ways to process the large amount of text data. In order to reduce the workload of data cleaning, we propose an intelligent method of automatic revision of power defect logs in this paper by adopting natural language processing technologies. We utilize entity recognition technology to recognize electrical equipment words on the text and utilize word similarity calculation to find out words with similar meaning in the standard vocabulary, which is the main process to revise Abnormal text. With the outstanding performance of entity recognition, the workload of data cleaning is reduced approximately 70% through our proposed method, which greatly improves the efficiency of unstructured data processing.
Merging Naive Bayes and Causal Rules for Text Sentiment Analysis
Authors: Luo Yongjian , Yang Xiaohua , Ouyang Chunping , Wan Yaping , He Sixi
Published: February 2021

Trad itional machine learning sentiment analysis models are d ifficult to achieve good classification results from small sample data. This paper proposes to merging naive Bayes and causal rule(MNBACR) for small sample data sentiment analysis scenarios. This model is based on the causal analysis theory, and introduce the causal inference algorithm into the field of text sentiment analysis. The causal inference algorithm extracts the causal ru les of Chinese texts, and the causal rules can be used as the features of the naive Bayes algorithm to predict the sentiment polarity of small sample texts. In experiments, the model in this paper is evaluated on financial news datasets which have a small number for sample, and the results show that the proposed method achieves the best performance compared to the existing state-of-the-art models on the small sample data onto sentiment analysis
An Overview on Fine-grained Text Sentiment Analysis: Survey and Challenges
Authors: Guo Xiaoting , Yu Wei , Wang Xiaodong
Published: February 2021

Among various natural language process tasks, sentiment analysis has always been a research hotspot. From the initial sentence-level and document-level coarse-grained sentiment analysis to recent fine-grained sentiment analysis on the aspect word level, researchers are committed to applying diverse methods to obtain better sentiment analysis results, ranging from lexicon-based, statistical machine learning methods to deep learning models. In the change of technology, several benchmark datasets that can be used for model performance comparison are gradually yielded. This article summarizes the current research status of aspect-level text sentiment analysis from multiple dimensions such as dataset, mainstream methods, and evaluation indicators, finally, it puts forward the challenges facing and potential research directions from a unique perspective.
Research on Personalized Learning Based on Collaborative Filtering Method
Authors: Hu Xingguang , Wang Yingchao , Chen Qi Bo , Liu Qian , Fan Xiaoping
Published: February 2021

With the popularization of the Internet, the rapid development of intelligent education is promoted. They are a new platform for students to learn, and they can quickly help students improve and consolidate knowledge, as well as unlimited time, place, and space. However, due to a large number of learning resources, how to find the resources you need, and suitable from the massive resources is a problem that needs to be solved in this field. The individual characteristics of each learner are different, so when meeting their own needs, they have different requirements for individualization. The personalized learning resource recommendation system was born, according to the different characteristics of students to recommend corresponding learning resources. Since most of the recommendation systems currently recommend a large number of test questions or a large number of books, but each person’s needs are different, this article focuses on the above problems and selects a small piece of content course knowledge points in the learning resources, using course knowledge Come as a recommendation point and solve the above problems by designing a personal personalized learning mechanism. The main research contents of this article are as follows: (1) It is proposed to take curriculum knowledge points as the recommended objects. (2) Use collaborative filtering methods and cognitive diagnosis methods to design individual learning mechanisms. (3) Experiment. Through related experiments, the use of collaborative filtering method and cognitive diagnosis method based on curriculum knowledge points recommendation to confirm whether it is feasible.
Research on Intrusion Detection Method Based on Pearson Correlation Coefficient Feature Selection Algorithm
Authors: Chen Pengtian , Li Fei , Wu Chunwang
Published: February 2021

The current era is the era of big data and 5G. The network security data in the network is different from the past, and the network security data is growing exponentially. As an important line of defense for network security, intrusion detection technology can efficiently detect and process massive amounts of security data has become an important factor restricting its development. The feature selection method of intrusion detection data directly affects the efficiency of intrusion detection. Therefore, this paper proposes a feature selection algorithm based on pearson correlation coefficient, which performs feature specification on many features, which greatly reduces the amount of security data that needs to be processed, and effectively reduces the dimensionality of the data to increase the intrusion. Detection efficiency.
Exploration and Practice of Dance Teaching Mode under the Internet Background
Authors: Chu Jun , Feng Aiyun
Published: February 2021

In early 2020, the new crown pneumonia epidemic swept in, in order to block the spread of the epidemic to the campus, the Ministry of Education issued the “spring semester delayed school opening” request, “stop classes do not stop” the voice immediately rang. At one time, how to combine modern information technology with the teaching content of various subjects to maximize the efficiency of online teaching has become the primary problem for every teacher to think about, and in this environment, the dance educators who are mainly based on the traditional teaching form of “oral teaching” also ushered in a great test. By analyzing the advantages and disadvantages of traditional dance teaching methods and based on the author’s online teaching practice activities for one semester, this paper makes a comprehensive analysis of the application value and significance of modern information technology in Chinese dance teaching, in order to further enrich the Chinese dance teaching mode, optimize the dance teaching resources, make the dance teaching methods and contents more in line with the requirements of China’s modern technology, and provide theoretical basis and practical operation guidance for the future modern dance teaching mode.
Application of Convolutional Neural Networks in Radio Station Link Establishment Behaviors Recognition
Authors: Wu Zilong , Chen Hong , Lei Yingke
Published: February 2021

For the problem that it is difficult to recognize the type of link establishment (LE) behaviors of radio station whose communication protocol is unknown, a method is proposed to solve the problem by using convolutional neural network (CNN) to recognize LE behaviors. Themethod processes physical layer signals directly and breaks through limitation of unknown protocol standard. Three classical CNN models are optimized through experiments so that they become more suitable for recognition of one-dimensional time series signals. Experimental results show CNN can effectively recognize the different link establishment behaviors with a large number of training samples. Moreover, DenseNet whose recognition accuracy can reach 96% when the signal-to-noise ratio (SNR) is 0dB has the best performance compared to GoogLeNet and ResNet.
From Data to Evidence: Multi-Source Evidence Association Model Based on Bayesian Network
Authors: Zhang Li , Xu Feng , Liu Ming , Cui Tong
Published: February 2021

Recent years have witnessed the boom of artificial intelligence. With the advancement of judicial information, using artificial intelligence (AI) technologies to mine judicial big data is of great significance in smart courts. However, the reasoning of the evidence chain mainly relies on the judge’s manual work in the litigation process. How to model the multi-source evidence association (MSEA) and reason credible evidence chains (CEC) automatically is largely unexplored. In this paper, we propose an MSEA model based on the Bayesian network. Firstly, we construct an MSEA network in which each evidence element serves as a node, and the node correlation probability is calculated via the association relationship among the evidence elements. Subsequently, with the guidance of the event judgment chain, the MSEA model is constructed based on Bayesian networks. In the end, we use a genetic algorithm to optimize the Bayesian network and select credible evidence chains. To the best of our knowledge, this is the first time that using a probability graph model to mining the association of multi-source evidence. Experiments and the case analysis prove the effectiveness of our method.
Research on Improved YOLOv3 Fire Detection Based on Enlarged Feature Map Resolution and Cluster Analysis
Authors: Yue Conghan , Ye Jun
Published: February 2021

Fire detection can effectively prevent the occurrence of fire. For the current fire detection methods, traditional image processing techniques such as grayscale image processing and feature extraction processing have poor anti-interference ability, weak generalization ability, and the detection results are more sensitive to data fluctuations. At present, based on the concept of deep learning, the proposed convolutional neural network processing of extracted image features has been widely used. On this basis, an improved YOLOv3 fire detection algorithm is proposed in this paper: The K-Means++ algorithm is used for clustering analysis to obtain the corresponding anchor boxes dimension, which reduces the error detection rate caused by the bounding boxes not matching the label; secondly, the resolution of the feature image is improved and the receptive field is enlarged; the image data are sharpened and the contrast is enhanced to make the data features more prominent. The experimental results show that the detection precision of the method is 97.7%, the recall rate is 98.5%, and the fps is 19, effectively solving the problem of high error detection rate and high missing rate of traditional image processing and general CNN networks for suspected flame objects.
Mumps prediction based on improved support vector machine
Authors: Li Yongna , Song Huamin , Jiang Jiulei , Jiang Shaoping
Published: February 2021

The prevention of d isease, has the important guiding significance to safeguard the health of the people. In order to improve the accuracy rate of mumps disease forecast of Yinchuan City, Ning xia, joined the combined kernel function idea based on regression prediction model of the traditional Support Vector Machine, the polynomial and radial basis kernel function combination instead of the traditional single kernel function model, proposed a combination of kernel function of support vector machine algorithm, with mumps Ning xia disease for the practice of Yinchuan City, to conduct research and forecast for the pathogenesis of rules combined with the local weather changes. The meteorological data and the number of diseases using correlation analysis method for processing, select the factor related coefficient ordering large from, then these factors should be selected using a single kernel and mixed kernel function for processing, training and prediction using support vector machine. The experimental results show that the mixed kernel function of Support vector machine prediction model results than the single kernel function regression is better
Narrow Density Window and Risk Assessment Based on Big Data
Authors: Li Pengfei , Guan Zhichuan , Han Chao , Hu Sicheng , Xiao Fan , Guan Yunyun
Published: February 2021

For the deep strata with fracture zone, fault zone and various permeability and fracture loss, the geological conditions are particularly complicated, which makes it particularly difficult to predict the safety density window and engineering risk, through investigation and research, combined with the actual situation of a deep oil and gas reservoirs in northwest China, for the formation of three pressure section in the regions was determined, and through the actual leakage occurs correction of formation fracture pressure of drilling fluid density as a standard of lost circulation project risk evaluation, the author combined with big data, based on a certain algorithm, got the regionalization of the region narrow safe density window, on narrow density window combination of generalized stress and strength interference theory, based on the real-time drilling fluid density in the region one of the key Wells project risk evaluation, the project risk is match with the actual project risk situation
A Study of Big Data Application in Agriculture
Authors: Chen Jia , He Shouwu , Li Xiaoying
Published: February 2021

Agricultural data is typically characterized by at least 3 characteristics: massive data volume, various data sources, and complex structure. With the popularization of next-generation information technology, precision agriculture has become an emergent development direction in agricultural informatization. This paper proposed a big data application framework for precision agriculture. The model includes data sources, data integration, and data analytics. Use of big data application provides better guidance for precision agricultural.
A continuous variable quantum key distribution protocol based on multi-dimensiondata reconciliation with Polar code
Authors: Feng Bao , Lv Chao , Liu Jinsuo , Zhang Tianbing
Published: February 2021

The continuous variable quantum key distribution protocol (CV-QKD) has the advantages of easy generation of quantum signals, convenient measurement and high communication capacity. However, the multi-dimensional data reconciliation is mainly implemented by binary LDPC code as quantum error correction code, which is greatly affected by complex environments and the bit error rate is high. In order to solve this problem, acontinuous variable quantum key distribution protocol based onmulti-dimension data reconciliation with Polar code, that can be applied to long-distance transmission in power systems, is proposed. First, we prepare an EPR quantum state, and perform heterodyne detection on one of the quantum states, and obtain the values of two orthogonal components at the same time which is the coherent state of the information carrier. Then the continuous variable quantum key distribution is realized by six steps (i.e., quantum state preparation, gaussian random detection, data filtering, calculation of bit error rate, data reconciliation, privacy amplification).In data reconciliation, we use Polar code as the error correction code and use reverse coding technology to achieve key error correction, which can effectively improve the negotiation efficiency. Numerical simulation verifies the feasibility of the protocol, and the protocol is more efficient than the multi-dimensiondata reconciliation protocol based on LDPC code under the same conditions.
Research on Python Data Visualization Technology
Authors: Cao Shengjia , Zeng Yunhan , Yang Shangru , Cao Songlin
Published: February 2021

In recent years, researchers at home and abroad have accumulated a lot of experience in the research of data visualization technology, and they have played animportant role in scientific discovery, medical diagnosis, business decision-making, and engineering applications. As a library developed using Python language, Matplotlib has a concise language, high drawing accuracy, and simple and easy-to-understand code. This article first introduces data visualization and related technologies used and then uses Python’s Matplotlib library and pyecharts library to realize data visualization. Through representative examples, combined with the use of correct charts, visual processing of data in different fields, so as to further analyze the effect of visualization.
Review of Rational Drug Use Based on Apriori Algorithm
Authors: Luo Zhangming , Cheng Binjie , Tian Sen , Hong Anding , Zhang Jin , Li Qiang
Published: February 2021

One of the important reasons for irrational drug use is that the medical industry has been facing the problem of complex and redundant information. In order to solve this problem, a multitude of researchers carried out research on rational drug use by data mining technology and tried to apply it to real medical treatment. Apriori is one of the most frequently used and valuable algorithms. Based on the Apriori algorithm, we discussed the basic theory and the improved result. We elaborated on the detail and analyzed the development of the applied scenarios in modern medicine and traditional Chinese medicine. Then we discovered the applied scenarios of the Apriori algorithm mainly included medication law, dose research, and experience inheritance. Through the analysis of the literature on the use of the Apriori algorithm in rational drug use and the current development trend, this paper pointed out the existing problems in this field and put forward the future work and research focus.
An Overview: Maritime Spatial-Temporal Trajectory Mining
Authors: Jin Jialong , Zhou Wei , Jiang Baichen
Published: February 2021

In recent years, the sources of maritime spatial-temporal trajectory data have been increasing. With the widespread application of detection equipment such as radars, ship-based platforms, and satellite, maritime trajectory data has shown feature of volume, variety, velocity and valueless. The increase in data types and amount of data has led to complexity and difficulty of data processing. Traditional data mining algorithms are facing serious difficulty. This paper reviews researches for maritime spatial-temporal trajectory mining in recent years on trajectory clustering, anomaly detection, and trajectory prediction. Difficulties current researches faced are pointed out and prospects of deep learning are discussed in this paper.
Data Correlation based Feature Selection Model for Children’s Growth and Development Assessment
Authors: Zhao Changhua , Mi Chunqiao , Shi Yuanquan
Published: February 2021

The paper focuses on children’s data present inElectronic Health Records(EHRs) starting fromraw data gathered from Southwest Medical Data Center in China. We designed a systembased on workflow to analyzefeatures that affect children’s growth. The system is able to relate health caredata to diagnosis codes and with additional information integrated and correlated to EHRs data. Finally, we propose prediction models based on Recursive Feature Elimination (RFE), which can identify features thatare important to detect children’s growthand findcorrelations among features.
Research and Implementation of Clock Synchronization Technology Based on PTP
Authors: Yuan Kexin , Guo Xuebing , Tian Jianying
Published: February 2021

With the rapid development of power system, the requirement of network clock synchronization accuracy is becoming higher and higher, in many cases, it needs to reach microsecond level. In order to meet the increasing demand, based on the analysis of the basic principle of PTP clock synchronization protocol, a physical layer clock synchronization system based on STM32 MCU and DP83640 chip is proposed. The system synchronization time error is less than 1 microsecond by using the timeacc-007 time precision measuring instrument. The scheme meets the requirements of accurate clock synchronization for all distributed nodes in the power system.
Design of Forest Security Defense System Based on Beidou-Positioning
Authors: Yu Minzhe , Zhou Taoyun , Wang Xiaoya
Published: February 2021

Aiming at the problems that, it is difficult for the administrator to obtain various state information in the forest in real time, and it is impossible to make real-time and accurate judgments on the behaviors that endanger the forest and formulate effective countermeasures, a forest security guard service system based on Beidou positioning is designed. The system adopts STM32F103C8T6as the main control chip to control different types of sensors such as flame sensor, temperature sensor, humidity sensor, sound sensor and so on, the information of temperature, humidity, illumination, fire situation, animal type, personnel alarmaround the nodes in the forest environment associated with the location information of the Beidou module is transmitted to the server through the GPRS network in real-time, then the server broadcasts the normal information and the alarm information to the administrator through the control panel and the mobile client. The system adopts solar energy and can monitor the forest environment information automatically, which can greatly reduce the workload of the employees, low the costs of the network’s layout and monitoring, and provide the researchers with massive and reliable research data. It has a wide range of application in the forest management, agricultural monitoring, environmental monitoring and other fields.
Design and Implementation of Intelligent Agricultural Picking Mobile Robot Based on Color Sensor
Authors: Han Jieqiong , Liu Leian , Zeng Haojie
Published: February 2021

With the rapid development of artificial intelligence and the Internet of Things technologies, the degree of agricultural Informatization and intelligence has been further improved. Governments of every country attach great importance to agricultural modernization and intelligence. Based on this purpose, this project completely independently designed an intelligent mobile picking robot for application in agriculture. The picking mobile robot is mainly based on the STM32 development board, which is loaded with the color sensor, infrared sensor, and ultrasonic sensors, which is instead of the CCD sensor to identify maturity, thereby reducing the difficulty of the algorithm and realizing fast picking tasks. In the harvest season, the robot will be mainly used for picking fruits, which can completely, replaces farmers’ operations, liberating labor, and breaking the constraints of time and space.
Growth and Metabolism Control of Lactic Acid Fermentation Based on Internet of Things
Authors: Xu Ziwei , Ye Jun , Su Long , Liu Zhicong
Published: February 2021

With the development of Internet of Things technology, it drives the new development of most industries. Combining the fermentation industry with the LOT, fermentation is a highly nonlinear process in which operators need to measure and operate regularly. This paper proposes an efficient growth and metabolism control method for lactic acid fermentation based on the Internet of Things, using Internet of Things technology combined with cloud computing. Through neural network and expert system of predicting, the fermentation process can be controlled more accurately and effectively, and the threshold value can be changed in time according to the growth stage of the microorganism, to a certain extent, to avoid the error of manual operation. Show the advantages of combining the Internet of Things.
A Dam Performance Model Based on Rehabilitation of the Kariba Dam
Authors: Zhou Junxiao , Cheng Junwei , Li Qian
Published: February 2021

Because of the damage in its major parts such as the plunge pool and sluice gates, the Kariba Dam is now in a dangerous state that needs urgent actions of rehabilitation. In this paper, we use the multiple dam system in Kariba to verify the dam performance models we propose. First, a dam performance model is built with 3 parts which are focused on hydropower generation, water storage capacity, and the costs of a single dam respectively. Next, we simulated the geometrical characteristic of the dams based on the terrain of Zambezi river basin. The number and placements of the multiple dams system are determined to satisfy the generation and water capacity of the existing dam. Discussion of the costs-benefits difference between multiple dams system and other 2 solutions are made, the result shows that mulitple dam system is the optimal solution, and the model we proposed were proved to be reliable.
Differentially Private Geospatial streams publish for VANETs
Authors: Ding Zhe , Li Binyong , Zhao Jun , Shi Lei
Published: February 2021

With the development of vehicular Ad hoc Networks (VANENTs) technology, application in VANENTs has becomes an important role in human life. In practice, the location information of not all vehicles in VANENTs contains sensitive information. But traditional publish algorithm for geospatial stream for VANENTs based on differential privacy assumes that all the data in the geospatial stream contain sensitive information of users. As a consequence, utility of the traditional algorithm has been decreased. In this paper, a novel algorithm is proposed to publish geospatial stream, denoted as PGSV. PGSV make used of one-sided differential privacy to guarantee sensitive information. In the end, we make used of public dataset to verify efficiency of PGSV. The result show that PGSV have increased utility of published result and guaranteed security of published result.