bluuebunny/crossref_metadata_2025 huggingface dataset:
Phase mixing of lower hybrid modes in cold plasmas
Authors: Pramanik Sourav , Maity Chandan , Karmakar Mithun
Published: August 2019

In a fluid approach, nonlinear evolution of electrostatic lower hybrid modes is studied in a cold magnetized electron-ion plasma. The background magnetic field is assumed to be constant. In the frequency range of interest Ωci ≪ ω ≪ Ωce, the massive ions are treated as unmagnetized, and the electron inertia in the x-component of the momentum equation is neglected. The quasineutral plasma approximation is also relaxed. The dispersion relation for such low frequency modes reads as ω2=ωpi2/(1+ωpe2/Ωce2). Spatiotemporal evolution of such modes is analyzed by employing a simple perturbation technique. Our results show that an initially excited lower hybrid mode gradually loses its coherent nature due to phase mixing and eventually breaks even at an arbitrarily low amplitude. An estimate of the phase mixing time is also given, and it is found to increase as the strength of the magnetic field is enhanced. These results will be of relevance to space plasma situations and laboratory experiments.
Strong strain hardening in ultrafast melt-quenched nanocrystalline Cu: The role of fivefold twins
Authors: Zahiri Amir Hassan , Chakraborty Pranay , Wang Yan , Cao Lei
Published: August 2019

Nanocrystalline and nanotwinned metals exhibit ultrahigh strength but suffer from low ductility due to the absence of the strain hardening effect. Here, we report sustained strong strain hardening up to 20% compressive strain in a melt-quenched nanocrystalline Cu structure, which contains numerous fivefold twins, stacking faults, and twin boundaries. Our molecular dynamics simulations reveal that the strong strain hardening results from the synergistic effect of constant nucleation and impedance of dislocations, restricted twin boundary migration, and abundant dislocation reactions in fivefold twin networks. Specifically, we find that fivefold twins both nucleate and impede dislocations, and the migration of fivefold twin boundary is restricted by the core of fivefold twins. Moreover, we observe a new migration mechanism, in which fivefold twin boundary migrates by two atomic planes directly, enabled by the gliding of two different Shockley partial dislocations in the opposite directions. Finally, dislocation transmission, which is adverse to strain hardening, occurs very scarcely in fivefold twins. This is caused by the large misfit strains in fivefold twins and abundant immobile dislocations generated by frequent dislocation reactions in fivefold twin networks. This work reveals the advantage of fivefold twins over nanotwins to overcome the strength-ductility trade-off.
Experimental studies on the conduction mechanism and electrical properties of the inverted Ba doped ZnO nanoparticles based memristor
Authors: Liu Mengren , Borowiec Joanna , Sun Lin J. Jun , Konop Marek , Rahman Mohammad M. , Taallah Ayoub , Boi Filippo S. , Gillin William P.
Published: August 2019

In this study, the determination of the charge carrier conduction in a memristor based on barium doped zinc oxide (Ba0.95ZnO1-x) nanoparticles (NPs) in an indium tin oxide (ITO)/Ba0.95ZnO1-xNP/aluminium (Al) configuration, as well as an explanation of the resistance switching mechanism, was successfully carried out. The ITO/Ba0.95ZnO1-xNP/Al memristor charge carrier conduction was found to be dependent on the bulk charge conduction model of the space charge limited current. Furthermore, comprehensive quantitative analysis of the electrical properties of the memristor, such as the density of the trap states (Nt), capture cross section of trap (σt), frequency of escape (f), available charge carrier density (no), effective density of states in the conduction band (NC), maximum of dielectric relaxation time (τd), and charge carrier mobility (μ), was conducted based on the experimental data fitting. The switching behavior was found to be governed by an oxygen vacancy migration, and conducting filaments grow and rupture. The nonideality of the memristance of the ITO/Ba0.95ZnO1-xNP/Al device points to an excellent prospect of its application as an active platform for the development of novel nonlinear memristor based security systems.
Combining the mapping Hamiltonian linearized semiclassical approach with the generalized quantum master equation to simulate electronically nonadiabatic molecular dynamics
Authors: Mulvihill Ellen , Gao Xing , Liu Yudan , Schubert Alexander , Dunietz Barry D. , Geva Eitan
Published: August 2019

The generalized quantum master equation (GQME) provides a powerful framework for simulating electronically nonadiabatic molecular dynamics. Within this framework, the effect of the nuclear degrees of freedom on the time evolution of the electronic reduced density matrix is fully captured by a memory kernel superoperator. In this paper, we consider two different procedures for calculating the memory kernel of the GQME from projection-free inputs obtained via the combination of the mapping Hamiltonian (MH) approach and the linearized semiclassical (LSC) approximation. The accuracy and feasibility of the two procedures are demonstrated on the spin-boson model. We find that although simulating the electronic dynamics by direct application of the two LSC-based procedures leads to qualitatively different results that become increasingly less accurate with increasing time, restricting their use to calculating the memory kernel leads to an accurate description of the electronic dynamics. Comparison with a previously proposed procedure for calculating the memory kernel via the Ehrenfest method reveals that MH/LSC methods produce memory kernels that are better behaved at long times and lead to more accurate electronic dynamics.
Tuning ZnO nanorods photoluminescence through atmospheric plasma treatments
Authors: You Shujie , Patelli Alessandro , Ghamgosar Pedram , Cesca Tiziana , Enrichi Francesco , Mattei Giovanni , Vomiero Alberto
Published: August 2019

Room temperature atmospheric plasma treatments are widely used to activate and control chemical functionalities at surfaces. Here, we investigated the effect of atmospheric pressure plasma jet (APPJ) treatments in reducing atmosphere (Ar/1‰ H2 mixture) on the photoluminescence (PL) properties of single crystal ZnO nanorods (NRs) grown through hydrothermal synthesis on fluorine-doped tin oxide glass substrates. The results were compared with a standard annealing process in air at 300 °C. Steady-state photoluminescence showed strong suppression of the defect emission in ZnO NRs for both plasma and thermal treatments. On the other side, the APPJ process induced an increase in PL quantum efficiency (QE), while the annealing does not show any improvement. The QE in the plasma treated samples was mainly determined by the near band-edge emission, which increased 5–6 fold compared to the as-prepared samples. This behavior suggests that the quenching of the defect emission is related to the substitution of hydrogen probably in zinc vacancies (VZn), while the enhancement of UV emission is due to doping originated by interstitial hydrogen (Hi), which diffuses out during annealing. Our results demonstrate that atmospheric pressure plasma can induce a similar hydrogen doping as ordinarily used vacuum processes and highlight that the APPJ treatments are not limited to the surfaces but can lead to subsurface modifications. APPJ processes at room temperature and under ambient air conditions are stable, convenient, and efficient methods, compared to thermal treatments to improve the optical and surface properties of ZnO NRs, and remarkably increase the efficiency of UV emission.
Saturation of cross-beam energy transfer for multispeckled laser beams involving both ion and electron dynamics
Authors: Yin L. , Albright B. J. , Stark D. J. , Nystrom W. D. , Bird R. F. , Bowers K. J.
Published: August 2019

The nonlinear saturation of crossed-beam energy transfer (CBET) for multispeckled laser beams crossing at arbitrary angles is examined using vector particle-in-cell simulations. CBET is found to saturate on fast (∼10s of picosecond) time scales involving ion trapping and excitation of oblique forward stimulated Raman scattering (FSRS). Ion trapping reduces wave damping and speckle interaction increases wave coherence length, together enhancing energy transfer; ion acoustic wave (IAW) breakup in the direction transverse to the wavenumber increases wave damping and contributes to CBET saturation. The seed beam can become unstable to oblique FSRS, which leads to beam deflection at a large angle and a frequency downshift (by the plasma frequency). FSRS saturates on fast ∼picosecond time scales by electron plasma wave self-focusing, leading to enhanced side-loss hot electrons with energy exceeding 300 keV. This may contribute to fuel preheat but FSRS can be mitigated by the presence of a density gradient. Such growth of FSRS contributes to the saturation of CBET. Scaling simulations show that CBET, as well as FSRS and hot electrons, increases with beam average intensity, beam diameter, and crossing area, but that CBET is limited by the excitation of FSRS and IAW breakups in addition to pump depletion. FSRS deflects the seed beam energy by greater than 40% of the incident beam energy and puts a few percent of the incident beam energy into hot electrons. FSRS limits the efficacy of CBET for symmetry tuning at late stages in the implosion and may account for a large portion of the “missing energy” in implosions that use gas-filled hohlraums.
Perfect control of photoelectron anisotropy for randomly oriented ensembles of molecules by XUV REMPI and polarization shaping
Authors: Goetz R. Esteban , Koch Christiane P. , Greenman Loren
Published: August 2019

We report two schemes to generate perfect anisotropy in the photoelectron angular distribution of a randomly oriented ensemble of polyatomic molecules. In order to exert full control over the anisotropy of photoelectron emission, we exploit interferences between single-photon pathways and a manifold of resonantly enhanced two-photon pathways. These are shown to outperform nonsequential (ω, 2ω) bichromatic phase control for the example of CHFClBr molecules. We are able to optimize pulses that yield anisotropic photoelectron emission thanks to a very efficient calculation of photoelectron momentum distributions. This is accomplished by combining elements of quantum chemistry, variational scattering theory, and time-dependent perturbation theory.
Localization model description of diffusion and structural relaxation in superionic crystalline UO2
Authors: Zhang Hao , Wang Xinyi , Douglas Jack F.
Published: August 2019

We test the Localization Model (LM) prediction of a parameter-free relationship between the α-structural relaxation time τα and the oxygen ion diffusion coefficient DO with the Debye-Waller factor ⟨u2⟩ for crystalline UO2 under superionic conditions where large anharmonic interactions lead to non-Arrhenius relaxation and high ion mobility. As in a previous study of structural relaxation in Cu–Zr metallic glass materials having a range of compositions, we find that the LM relationship between the picosecond atomic dynamics (“fast” beta relaxation) and the long-time structural relaxation time and oxygen ion diffusion coefficient holds to an excellent approximation without any free parameters over the full range of temperatures and pressures investigated in our simulations.
Multicomponent coupled cluster singles and doubles and Brueckner doubles methods: Proton densities and energies
Authors: Pavošević Fabijan , Hammes-Schiffer Sharon
Published: August 2019

The nuclear-electronic orbital (NEO) framework enables computationally practical coupled cluster calculations of multicomponent molecular systems, in which all electrons and specified nuclei, typically protons, are treated quantum mechanically. In addition to energies, computing accurate proton densities is essential for the calculation of reliable molecular properties, including vibrationally averaged geometries and vibrational frequencies. Herein, the Lagrangian formalism for the multicomponent coupled cluster with single and double excitations (NEO-CCSD) method is derived and implemented. The multicomponent coupled cluster with double excitations method using optimized Brueckner orbitals, denoted as NEO-BCCD, is also developed. Both of these methods are used to compute the proton densities for two molecular systems. The results illustrate that orbital relaxation effects, which can be included either indirectly with the NEO-CCSD method or directly with the NEO-BCCD method, are critical for computing even qualitatively accurate proton densities. Both methods are also able to provide accurate proton affinities and vibrationally averaged optimized geometries. This Lagrangian formalism will enable the calculation of other properties such as analytical nuclear gradients and Hessians with NEO coupled cluster methods. Moreover, the accuracy of these methods may be improved systematically by the inclusion of higher-order excitations. Thus, this work provides the foundation for a wide range of future methodological developments and applications within the NEO framework.
Improving the electron mobility of polycrystalline InN grown on glass substrates using AlN crystalline orientation layers
Authors: Sakamoto Masumi , Kobayashi Atsushi , Fukai Yoshino K. , Ueno Kohei , Tokumoto Yuki , Fujioka Hiroshi
Published: August 2019

Herein, polycrystalline InN films with flat surfaces and improved electron mobility were grown atop AlN orientation layers on glass substrates by pulsed sputtering deposition. X-ray diffraction and transmission electron microscopy revealed that the InN films comprise dense c-axis-oriented grains. The electron mobilities of these c-axis-oriented InN films were higher than those of the InN directly grown on the glass substrate, reaching as high as 427 cm2 V−1 s−1. To demonstrate a practical application of the developed InN film, a thin-film transistor was fabricated on a 5-nm-thick c-axis-oriented InN film on an AlN orientation layer and operated successfully with a field-effect mobility of 60 cm2 V−1 s−1.
Stretchable batteries developed for potential use in a wide range of applications
Authors: Bandari Anashe
Published: August 2019

A new paper introduces a novel design for a stretchable-sliding lithium ion battery and characterizes its mechanical and electrochemical properties.
Phosphine-free synthesis of FeTe2 nanoparticles and self-assembly into tree-like nanoarchitectures*
Authors: Wang Hongyu , Wu Min , Wang Yixuan , Wang Hao , Huang Xiaoli , Yang Xinyi
Published: August 2019

Manipulating the self-assembly of transition metal telluride nanocrystals (NCs) creates opportunities for exploring new properties and device applications. Iron ditelluride (FeTe2) has recently emerged as a new class of magnetic semiconductor with three-dimensional (3D) magnetic ordering and narrow band gap structure, yet the self-assembly of FeTe2 NCs has not been achieved. Herein, the tree-like FeTe2 nanoarchitectures with orthorhombic crystal structure have been successfully synthesized by hot-injection solvent thermal approach using phosphine-free Te precursor. The morphology, size, and crystal structure have been investigated using transmission electron microscopy (TEM), high-resolution TEM (HRTEM), and powder x-ray diffraction (XRD). We study the formation process of tree-like FeTe2 NCs according to trace the change of the sample morphology with the reaction time. It was found that the FeTe2 nanoparticles show oriented aggregation and self-assembly behavior with the increase of reaction time, which is attributed to size-dependent magnetism properties of the samples. The magnetic interaction is thought to be the driving force of nanoparticle self-organization.
Dynamic and inner-dressing control of four-wave mixing in periodically-driven atomic system*
Authors: Li Yuan-Yuan , Li Li , Zhang Yun-Zhe , Zhang Lei
Published: August 2019

Four-wave-mixing (FWM) process is examined by using density matrix formalism in a periodically-driven atomic medium. Numerical result shows that FWM signals can be controlled by selecting different dynamic parameters of the probe field and strengths of the inner-dressing fields. It is also shown that the controllable FWM process is dominantly influenced by the evolution of atomic population difference and two-photon coherence. This dynamic and inner-dressing control of FWM is probably used for optimizing the optical nonlinear process and information processing.
Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization
Authors: Farahat Amr , Reichert Christoph , Sweeney-Reed Catherine M , Hinrichs Hermann
Published: August 2019

Objective. Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain–computer interfaces (BCI). Artificial neural networks, however, are considered black boxes, because they usually have thousands of parameters, making interpretation of their internal processes challenging. Here we systematically evaluate the use of CNNs for EEG signal decoding and investigate a method for visualizing the CNN model decision process. Approach. We developed a CNN model to decode the covert focus of attention from EEG event-related potentials during object selection. We compared the CNN and the commonly used linear discriminant analysis (LDA) classifier performance, applied to datasets with different dimensionality, and analyzed transfer learning capacity. Moreover, we validated the impact of single model components by systematically altering the model. Furthermore, we investigated the use of saliency maps as a tool for visualizing the spatial and temporal features driving the model output. Main results. The CNN model and the LDA classifier achieved comparable accuracy on the lower-dimensional dataset, but CNN exceeded LDA performance significantly on the higher-dimensional dataset (without hypothesis-driven preprocessing), achieving an average decoding accuracy of 90.7% (chance level  =  8.3%). Parallel convolutions, tanh or ELU activation functions, and dropout regularization proved valuable for model performance, whereas the sequential convolutions, ReLU activation function, and batch normalization components reduced accuracy or yielded no significant difference. Saliency maps revealed meaningful features, displaying the typical spatial distribution and latency of the P300 component expected during this task. Significance. Following systematic evaluation, we provide recommendations for when and how to use CNN models in EEG decoding. Moreover, we propose a new approach for investigating the neural correlates of a cognitive task by training CNN models on raw high-dimensional EEG data and utilizing saliency maps for relevant feature extraction.
Vector Circle Clipping Algorithm Based on Polygon Window of Hexagonal Grid System
Authors: Cao Minghua , Zhang Heng , Zhou Changjie , Sun Yi , Yu Hui
Published: August 2019

This paper proposes an algorithm for cropping vector circles in polygon window under hexagonal grid system for the problem of long time consuming and low efficiency of polygon window clipping algorithm in existing Non-Cartesian coordinate system. First, the algorithm converts Cartesian coordinates into hexagonal grid coordinates, then performs intersection detection by vector operation, and uses geometric relations to judge the virtual real intersection points, thus obtaining the coordinates of the real intersection points, and thus cutting the arc results. Finally, the final cropping result is obtained. The experimental results show that the algorithm is an effective vector circle clipping algorithm based on the hexagonal grid system polygon window, and the algorithm has high efficiency and stability.
A Quantitative Study on the Colour of City Landmark Landscape Architectures
Authors: Zhang Lei , Cheng Yuning , Ma Li
Published: August 2019

City landmark landscape architecture is a production of urban development to a certain historical stage, delivering dual functions: identification and sightseeing, which is a significant component of urban construction. Colour is the main media for the landscape architecture to identify itself and is often subjectively experienced by the public and designers. However, the subjective perception cannot lead to a more scientific, quantitative, and repetitive research showing its limitations. In this paper, a quantitative study on the colour of landmark landscape architecture is undertaken and demonstrated via a case study: Sacred Heart Cathedral Jinan. The colour data of five representative components (circle window, apex window, wall decorative symbol, decorative stone carving, and decorative stone beam column) on the main façade of the cathedral are quantitatively collected including HSB, RGB, CMYK and CIELab in which the HSB data that responds to human eyes are used to explore the colour patterns at different time. With different identification degrees, it objectively shows whether a component can be explicitly viewed and whether a specific time is good for sightseeing. The methods in this paper are scientific-based, quantitative, and repetitive, thus providing the methodological support for designing and experiencing the future city landmark landscapearchitecture.
Application of Multi-dimensional Linear Fitting Method in the Establishment of the Semi-autogenous Grinding Mill Model
Authors: Zhang Chunming , Wang Zhimin , Sun Yiyue , Ma Xiang , Guan Changliang
Published: August 2019

In order to build the semi-autogenous grinding mill model more accurately and efficiently, a large-scale concentrating mill in China was studied and its daily production data were analyzed. By combining the field experts experience and utilizing the multi-dimensional linear fitting methods, the semi-autogenous grinding mill axial pressure prediction model was built through various methods. Meanwhile, the advantages and disadvantages of different multi-dimensional linear fitting methods were compared to determine the most appropriate method. Then the multi-dimensional linear fitting model for the axial pressure of semi-autogenous mill was successfully achieved, via analyzing the influence of different parameters on the axial pressure model of semi-autogenous mill, and comparing the predicted results of the model with the measured values of the field production. It is demonstrated that this method has high accuracy and calculation speed simultaneously, which is significant to the establishment of semi-autogenous grinding model.
Design and Implementation of a Weapon System Detector for Air-to-ground Missile
Authors: Zhang Zhihu , Liu Xiaoqin , Huang Shan , Yang Le , Zhang Lixin , Lv Zhaofu
Published: August 2019

Based on the theories of modular design, single-chip microcomputer system and ARINC429 bus communication technology, a feasible design scheme of a detector circuit is put forward. The design we proposed can meet the urgent demand of testing and maintaining new military equipment for the forces. The proposed detector we have designed can meet all the technical requirements of certain air-to-ground missile installed on certain aircraft. The detector is portable and convenient to be operated. The detector can test in-flight fire control system, the launcher and the whole air-to-ground missile system. The design scheme has some reference value to other detectors of air-to-ground missile system.
Development of Tibetan Multifunctional Digital Clock Based on FPGA
Authors: Fan Huimei , Wang Shuqun , Mao Ning , Dai Kexin
Published: August 2019

This paper presents an digital clock design based on FPGA which implements the function of displaying the year, month, day, week and temperature in a Tibetan language. The whole system is designed and compiled on vivado2017.1 with verilog hardware description language, and programed on FPGA development board. The results show that the design of the Tibetan digital clock is successful and feasible.
Research on Prediction the Development Trend of Weibo Topics Based on Fuzzy Neural Network
Authors: Miao Ruijie , Yang Kongyu
Published: August 2019

The people who use the Weibo application are widely available, and Weibo has the characteristics of instant openness, strong interaction, fast transmission speed and wide influence range. Therefore, predicting the development trend of Weibo topics quickly and accurately is a topic worth studying. In this paper, the total number of posts in a Weibo topic is used as a quantitative indicator of the development the Weibo topic. Fuzzy neural network is used to predict the hot trend of Weibo topic, and the improved annealing algorithm is used to optimize the membership function of the fuzzy neural network and its quantity to improve the prediction accuracy. The superiority of the proposed method is verified by the comparison experiment of the collected Sina Weibo dataset. The method proposed in this paper effectively solves the problem that the parameters in the membership function are complex and easy to fall into the local optimum, which improves the accuracy of the development trend of Weibo public.
Molecular Communication Experiment Based on Nano-Internet of Things
Authors: Shi Sufeng , Chen Zhen
Published: August 2019

At present, most of the molecular communication systems have strict constraints on the environment in nano-network, such as light, molecular concentration, environment and so on. Under these conditions, these systems can well complete the simulation of molecular communication in internet of things. But in reality, more application scenarios can not meet these harsh conditions, so these systems can not be widely used; we use sensors to collect molecular environment information, upload it to the single chip computer, use Arduino to detect sensor values, and use MATLAB software to make theoretical simulation system to achieve the task of theoretical simulation of molecular communication between targets.
A Sparsity-constraint Capon Beamforming Algorithm
Authors: Wang Wenjing , Zhao Hongyu , Zhang Ying
Published: August 2019

In the presence of the steering vector of the signal of interest mismatches, nulls are formed in the direction of the signal of interest, which affects the performance of the beamforming. In this paper, a sparsity-constraint Capon beamforming based on iterative robust minimum variance beamforming is proposed. Using iteractive robust minimum variance beamforming, the mismatched steering vector is corrected. To achieve sidelobe level and suppress iterference, sparse constraints on beam response is added in the angel of sidelobe domain. Computer simulations have been conducted to verify validity and superiority of the proposed algorithm. It is shown that in the presence of mismatched steering vector, the proposed algorithm is more robust and has lower sidelobe level than the existing methods.
Discussion for Identifying H-matrices
Authors: Han Guichun , Zhang Chunsheng , Gao Huishuang
Published: August 2019

H-matrices are special matrices and have widely applications in many fields such as numerically solving the problems of integral equations, partial differential equations, control theory, and also artificial neural networks, but it is not easy to judge that whether a matrix is an H-matrix or not. In this paper, some practically numerical methods to determine H-matrices are proposed.
Conflict Decision Method based on Quadratic Combination
Authors: Guan Xin , Zhao Jing , Liu Haiqiao
Published: August 2019

There are many unsatisfactory situations in the existing improvement methods of evidence theory, such as a large amount of calculation, the normalization process is unreasonable, the evidence combination effect is not ideal in the conflict evidence decision-making process, and so on. This paper proposes a method based on quadratic combination of conflict evidence to improve the above situations. Firstly, a new flow chart of conflict evidence decision method based on quadratic combination is proposed. Secondly, a new multiplicative normalization rule is proposed, and the new rule is analyzed to verify its rationality. Thirdly, the shortcomings of the existing conflict measurement methods are analyzed, a new conflict measurement function is proposed, and the rationality of the new function is analyzed. Finally, through the analysis of the example and comparison with the existing evidence combination rules, the effectiveness of the method of this paper is verified.
Research on Virtual and Identity Identity, Correlation and Analysis Technology of Internet-related Economic Crimes Based on Big Data
Authors: Yuan Jing , Tu Min
Published: August 2019

In recent years, social activities on the Internet have been frequent, and people have carried out network social activities by registering different IDs on various platforms, which has formed a new identity - Internet virtual identity. A large number of virtual identity exchanges form a social network relationship diagram, but the network virtual identity does not require real-name authentication, which increases the security maintenance and supervision of the network. Therefore, the investigation of network-related economic crimes under big data faces new challenges. In response to this situation, a combination of virtual and real identity, association and analysis techniques based on big data is proposed. Firstly, through the integration and processing of heterogeneous data, the data resources integration under the condition of decentralized data source and multi-database data source is realized. Secondly, the verification and analysis platform of Internet information is built by Solr which is an open source framework. Finally, using full-text search technology to achieve target search and virtual identity association analysis under big data.
Simulation and 3D Visualization of Mission Scheduling for Imaging Satellites
Authors: Deng Baosong , Tang Rongfu , Li Jing , Yin Erwei
Published: August 2019

Simulation, 3D visualization and analyses of satellite imaging tasks are capable to display orbital maneuver and ground cover scheme, which are much helpful to ensure the feasibility and reliability of the mission planning and to improve the efficiency of the satellite utilization. A system of 3D visualization and simulation for satellites is proposed and presented in the current work. Multi-resolution mass terrain data, images and vector data are processed and seamless stitched, and dynamic roaming of large range scenes based on digital earth is built as well. Then, the satellite flight status, maneuver, onboard sensor states and ground coverage ranges are visualized by using data of the satellite mission scheduling. Moreover, the system provides the visualization analyses of multi-dimension, high fidelity, free observation and so on. The proposed system is evaluated by practical satellite data and the experiments show that the system is capable to offer an intuitive, convenient and effective way for the space exploration and engineering design of satellites.
Unmanned Aerial Vehicle’s Formation Control-Based on Variable Structure
Authors: Zhang Xiyong , Chen Yun , Liu Yuan
Published: August 2019

The paper deduces the state equation of line-of-sight angle based on the relative motion equation between the host and the wing plane. On this basis, a Formation-keeping controller is designed with variable structure control strategy, which controls the line-of-sight angle between the host and the wing man in a certain setting, in order to ensure that multiple UAVs fly in a fixed formation without collision. Fixed values are used to achieve the goal of UAV flying in a fixed formation. The effectiveness of the controller is proved in the framework of Lyapunov stability theory. The simulation results show that the designed controller can control the line-of-sight angle at a fixed value, and is robust to maneuvering and disturbance, thus achieving the goal of UAV flying in a fixed formation.
Research on Hot Topics Discovery Based on Short Texts of Online Reviews
Authors: Liu Zhenyan , Qiu Yueshi , Zhang Zhe , Mao Limin , Zheng Xiaohan
Published: August 2019

The two crucial issues for hot topics discovery based on online reviews are that the sparsity of short text features and the “long tail” phenomenon of hot topics. This paper focuses on these two key issues, and proposes an improved similarity calculation method to calculate the similarity of short texts, and a novel clustering algorithm based on the time factor and dynamic adjustment of comparison times to automatically discard a large number of outliers. Moreover, the validity and advancement of the new method are presented by comparative experiments using real data sets.
Accurate Positioning of the Substation Instruments in Images by Using a Method Based on Convolutional Neural Networks
Authors: Yang Caiyi , Huang Kun , Li Yujie , Tang Yue , Yuan Jianying
Published: August 2019

Aiming at solving the problems of sensitivity to instrument shape and low accuracy of positioning by using the traditional feature matching points based methods, a novel positioning approach based on convolutional neural network is proposed in this work. The designed convolutional neural network consists of two convolutional layers, two pooling layers and two fully connected layers. The objective function adopts cross-entropy and the optimization method adopts Adam algorithm. We used 7000 images collected at different time and situations as test samples. We discussed the performance of the proposed algorithm under different amount of training data, different convolution kernel sizes and different number of convolution kernels in the experiments. Compared with the traditional feature matching point based method, the proposed method has higher recognition accuracy and lower false positioning rate.
Vulnerability Parser: A Static Vulnerability Analysis System for Android Applications
Authors: Chang Yingxian , Liu Bin , Cong Lianri , Deng Hua , Li Jiaming , Chen Yuanzhe
Published: August 2019

In the case of user information leakage, the security problem of Android applications is of great importance. How to quickly and efficiently detect Android application security vulnerabilities has become an urgent research topic in security field. Aiming at the key technologies such as vulnerability detection in Android application, architecture is proposed for static analysis of android applications, namely the Vulnerability Parser. The architecture is composed of the following components, such as APK Decompressor, Manifest.xml Parser, Vulnerability Vector, and DexParser etc. Vulnerability Vector is extensible and detection rules can be configured for different vulnerabilities. The implementation of primary components is described in detail. Preliminary experimental results demonstrate its effectiveness.
Survey on Multi-Objective Evolutionary Algorithms
Authors: Huang Wenlan , Zhang Yu , Li Lan
Published: August 2019

Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. This paper surveys the development of MOEA and its research status, classifies it into four categories, analyzes the advantages and disadvantages of these algorithms, and summarizes the main application fields of MOEA. Finally several viewpoints for the future research of MOEA are presented.
Research on Real-Time Prediction of Quality of the Slag Overflowing From Furnace Door Based On Image Processing
Authors: Xie Xin , Yang Lingzhi , Guo Yufeng , Yang Zeshi , Wang Shuai , Ni Jiamiao
Published: August 2019

As an important substance (slag) with the absorption capacity to the harmful elemental phosphorus, saturated slag demanded discharging for both the requirement of the generation of foaming slag in the furnace and slag replacement to strengthen the effect of dephosphorization. This paper reviewed during the electric arc furnace (EAF) steelmaking process for real-time quality of the slag overflowing from furnace door based on image processing. Existing methods, due to high temperature and the on-site production environment, were not considered to weigh real-time spilled slag and the total mass. The real-time composition and quality of the slag in the furnace, which affected the steelmaking process and the quality of products, were significantly influenced by addition of various metallurgical raw materials such as ladle, scrap and lime. The real-time oxidation products and additives could be calculated through the PLC data in the furnace to learn the composition and quality of the slag but it suffered the amount of slag continuously decreasing, due to real-time slag overflowing from the door whose composition was consistent with ingredient in the furnace at that time. The image processing slag from the door serves as a calculating means for the area of the big and small slag areas by the three primary colors of the light and the adjusted pixel channel value. Based on the comprehensive analysis of the situation of the slag from the door, the area of the big and small slag areas were analyzed and extracted to explore the relationship between the area and mass. And the accuracy of calculating the real-time slag was characterized by calculating the volatility of the total mass of the slag from the door counted by each heat. The relationship was verified to calculate the mass of that accuracy was up to 90.48%, which provided a scientific method for predicting the quality of real-time spilled door slag and a scientific basis for predicting in real time the composition and quality of slag in the furnace.
An End-to-end Auto-driving Method Based on 3D Lidar
Authors: Wang Meng , Dong Han , Zhang Wei , Shu Wei , Chen Chao , Lu Yuanzhi , Li Hongfei
Published: August 2019

The development of artificial intelligence, especially deep learning engineering technology, has made auto-driving cars more and more realistic. The end-to-end auto-driving method is an automatic driving system which is different from the rule-based system of. It uses the data from the environment to output vehicle control information solutions directly, greatly reducing the system complexity. 3D Lidar is the core sensor of automatic driving system. In this paper, a deep convolution neural network is designed for the end-to-end automatic driving method. This paper uses 64-line 3D Lidar data and transformation algorithm, transforms the 3D Lidar point-cloud data into depth images which can be used directly by an end-to-end deep learning network. This paper matches 3D Lidar data with vehicle-mounted Can bus data to obtain data and tags which will be feed into the deep learning network. The output of the deep learning network is the controlling information that directly acts on the vehicle. Based on experimental verification, the end - to - end automatic driving method based on 3D Lidar is of great value and potential for further development.
Analysis and Research on Resource Management Allocation Mode in Unlicensed Bands Based on D2D Cellular Network
Authors: Zhang Heng , Zhou Changjie , Cao Minghua , Sun Yi , Yue Liang
Published: August 2019

As spectrum resources become more and more tense, the use of unlicensed frequency bands has become more and more important. The management allocation and power control optimization of D2D communication resources in unlicensed frequency bands is the current focus. In the current environment, D2D communication improves spectrum efficiency and system capacity, which causes the complexity of inter-layer interference between the cellular network and the D2D link to increase, so that the failure rate of cellular service access is too high. Aiming at the above problems, a Stackelberg game based on energy efficiency maximization is proposed. The access probability is used to help network planning and ensure the successful access of cellular services. Through the Q learning algorithm, learn the optimal strategy of resource management allocation. Derive the throughput expression of the D2D user.
A Parallel Computation and Web Visualization Framework for Rapid Large-scale Flood Risk Mapping
Authors: Wang Min , Lai Ruixun , Xia Runliang , Wang Ming , Yang Ming
Published: August 2019

For rapid flood risk mapping, a key aspect is transferring the results of flood simulations for web visualization. The challenges here include: (1) large-scale and complicated modelling; (2) the need for fast computation to solve flood numerical models; and (3) effective web visualization. This paper tackles these challenges by introducing a web-based framework that can transfer the results of parallel flood simulation to a web visualization application. Flood depth data, velocity and flood arrival time are calculated using a parallel shallow water model with a predicted input flow. This is automatically transferred into shapefile data using ArcObjects components. Other data relating to the flood risk mapping is managed and served through a REST (Representational State Transfer) interface. The web visualization is performed using the ArcGIS for JavaScript API. We applied the framework to the floodplain of the lower Yellow River in China. The results show basic geo-information about the domain, the flood depth classified by color and economic loss through inundation of villages and farmland. Although the simulated domain was large and the boundary conditions complicated, the whole process from flood risk simulation to web visualization took less than 3 hours, which is enough time to increase flood preparedness.
Opioids Propagation Model and Strategy Analysis Based on SEIR
Authors: Cui Xinyu , Wang Yujing , Chen Nan
Published: August 2019

Through the analysis of the underlying data and the creation of the model, we have specific strategies for the transmission of opioids. To begin with, we process and analyse the data by graph theory and integrity principles. Then we find that the relevant variables of drug propagation law. After that, by modelling the results, we continue using the mechanism of the infectious disease SEIR model to analyse and construct the opioid drug propagation SEIR model [ S(Susceptible nodes)-General opioid users; E(Exposed node)-Opioid addicts; I(Infected nodes)-Drug users; R(Recovered nodes)-Drug abusers ]. By setting the corresponding parameters in the differential equations, we get the transmission speed of opioid abuse is positively correlated with the degree of initial infected node. The easier the node is infected, the faster the spread of opioids will be, the earlier it will stabilize, and the more nodes will eventually recover. We analyse the constructive ways to control the spread of opioids in the end, and propose the most valuable coping strategies.
The Principle of Uyghur Vowel Harmony
Authors: Kaili Binu , Mei Reyi , Yang Deming , Wang Jianbin
Published: August 2019

This paper aims at Uyghur vowel mutation tendency by means of Economic law of articulation. The economy principle can be easy to trace thorough evaluating Uyghur mutation. And the economy principle might have been a main factor producing the vowel harmony.
Investigation on Potential Application of Artificial Intelligence in Preschool Children’s Education
Authors: Jin Ling
Published: August 2019

Artificial intelligence has been rapidly developed in the past years. The present paper aims to investigate the potential application of artificial intelligence in the preschool children’s education. First, the critical technologies of artificial intelligence are reviewed and related to the preschool children’s education. Then, the demand for artificial intelligence in preschool children’s education is analyzed for children-centered learning activities, respecting children, and learning in accordance with children’s development. Finally, some possible applications of artificial intelligence in preschool education are discussed.
Climatological Averaging and Application of Optical Vegetation Index based on MODIS
Authors: Liang Jing , Lu Hanyu , Wu Song , Ding Leiding
Published: August 2019

In view of the problem that the inversion of soil moisture is greatly affected by vegetation coverage, this paper uses MODIS vegetation products. By eliminating the data of cloud, rain, snow and other quality damage data, the global climate mean data set is compensated by the linear interpolation method after the data average processing. The results showed that NDVI vegetation index changed regularly with seasons, and the change rule was different in different regions. The single pixel interior can accurately reflect the variation trend of the vegetation index of different pixel NDVI in one year. It can describe the seasonal variation of vegetation in the global pixel, reflect the change of green degree, and meet the need to correct the accuracy of vegetation influence.
Which Factors Have the Greatest Impact on Student’s Performance
Authors: Li Feng , Zhang Yu , Chen Mo , Gao Kening
Published: August 2019

Nowadays, a great deal of educational data has been produced by E-learning system and MOOC. Educational data is important for Teaching and research. These educational data can be classified many kinds of feature, such as demographic features, social features and behavioral features. And which feature is the most import for student’s performance? In this paper, In this paper, we use some common data mining technologies including Naïve Bayesian(NB), Artificial Neural Network(ANN), Support Vector Machine(SVM) and Decision Tree Classifier(DT) to predict students’ performance, and try to find out the influence of characteristics on students’ academic performance. From the conclusion, we can see that SVM technique outperform others, and Behavioral Features have good effect on students’ performance.
A Text Mining Method to Trace Influential Product Features Based on PageRank
Authors: Liu Qin , Yu Zhuchao , He Shucheng
Published: August 2019

As a technology of Web2.0, social media has become a popular tool for both enterprises and customers to understand other voices. Therefore, an important project for enterprises to have an outstanding feedback is to use social media to promote and spread products correctly, so it’s necessary to mine some product features which customers care mostly. In this paper, we crawled online data from website DouBan to trace those features through their posted contents, such as descriptions, reviews, comments, etc. Firstly, we computed received attention of these contents by self-information algorithm and measured similarity by cross-entropy algorithm. Secondly, using the attention value as node weight and similarity as edge weight, we computed influence of product features in PageRank network by the proposed method. Experimental results show that it is books’ authors, genres and themes that consumers consider of mostly when they choose a book commonly. Furthermore, when it comes to a novel, the feature character is also one of the focuses while other features are not as important as them. This research informs a way for enterprises to choose emphasis in an online promotion, which can be very helpful for recommending products to target customers in a proper way.
330kV Live Working Platform Design based on Minimum Approach Distances Analysis via Weighted Digraph
Authors: Liu Zirui , Qin Xuan , Zhang Dingrui , Han Yanhua , Pu Tingyan , Liang Qinghua
Published: August 2019

Considering the strict requirements of live working platform in compact substation, a module assembly based structure is put forward. In order to meet the demands of insulation performance, a method of calculating minimum approach distances based on graph theory is proposed, and the feasible regions of structural parameters and flow parameters are obtained, which are finally verified in experiments.