bluuebunny/crossref_metadata_2025_split huggingface dataset:
A 3-D Meshless Method for Solving Maxwell’s Equations by Using the Steger-Warming Flux Vector Splitting Approach
Authors: Gao Yukun , Chen Hongquan , Xu Shengguan , Zhang Jiale , Cao Cheng , Gao Huanqin 
Published: September 2020

Abstract
In order to solve Maxwell’s equations so as to study the electromagnetic stealth characteristics of targets, a 3-D meshless method is developed. According to the meshless method, the spatial derivatives on clouds of points are computed by using the weighted least square approach. After that, the Steger-Warming flux vector splitting approach is used to calculate the physical flux of Maxwell’s equations. By using the developed method, the calculated bistatic radar cross sections (RCS) of a 3-D sphere are obtained, which are agree with the series solutions. Finally, the electromagnetic scattering characteristics for a 3-D stealth aircraft model with different situations is given, which shows the developed method has the ability to accommodate complicated 3-D configurations with multi-element.
A Scientometrics Analysis on Brain-Inspired Intelligence
Authors: Liang Jianghai , Yang Xiao , Liu Shulei , Wu Ji 
Published: September 2020

Abstract
In recent years, brain-inspired intelligence has attracted increasing attention from both academia and industry. Although there are several literature reviews of brain-inspired intelligence research, they appear to be a lack of systematic quantitative and visual investigation. The purpose of this paper is to make insights into certain characteristics of brain-inspired intelligence research, and consequently to reveal the global trends as well as support researchers to determine future research directions. The data were retrieved from Science Citation Index Expanded (SCI-E) of core databases in Web of Science (WoS) and the global patent database of incoPat. Based on the aforementioned data and the scientometric techniques, we show the general characteristics of R&D (R&D) output, identify the research hotspots and the knowledge structure of brain-inspired intelligence and evaluate the R&D capability of countries, institutions. Finally, we conclude that (1) China, the United States, UK, Germany and South Korea are the main R&D forces in brain-inspired intelligence domain. (2) The researchers focus mainly on brain-computer interface, neuromorphic device, neuromorphic computing, and brain operation mechanism, and the R&D forces concentrate on the directions of G06N, G06F, A61B, G06K, H01L. (3) Major countries/regions pay more attention on academic cooperation and exchanges in brain-inspired intelligence. (4) America is leading in the basic research of brain-inspired intelligence. (5) China still has a big gap with the European and American countries in terms of industrial application.
Construction and Application of Big Data System in Automobile Intelligent Factory
Authors: Yang Yixin , Gao Jianjun , Pi Zhibo , Guo Konghui 
Published: September 2020

Abstract
Under the background of intelligent manufacturing, the application of industrial big data is the key to improve the utilization efficiency of manufacturing resources. Starting from the purpose of big data application and how to replace the efficiency of enterprise resource utilization, aiming at the construction of intelligent and transparent big data system in automobile factory, this paper combed the concept of big data system construction, discussed the construction ideas of big data system, constructed the big data system framework suitable for discrete manufacturing enterprises, and gave the methods of data acquisition, transmission, storage, processing and Analysis of the specific program. For big data applications, taking the enterprise intelligent operation cockpit, intelligent electronic process system and enterprise epidemic prevention and health cloud system as examples, the scene and application effect of big data application are demonstrated. The construction and application of big data system in intelligent and transparent automobile factory provides a reference model for further promoting intelligent manufacturing driven by big data.
Prediction of Length of Stay on the Intensive Care Unit Based on Bayesian Neural Network
Authors: Fang Jiansheng , Zhu Junlin , Zhang Xiaoqing 
Published: September 2020

Abstract
Predicting length of stay (LoS) accurately in the intensive care unit (ICU) is important to improve care quality and resource utilization. However, for LoS prediction, existing methods are facing main challenges, including uncertain prediction, generalization, interpretability, etc. In this paper, we utilize Bayesian neural network (BNN) to alleviate the above main challenges. The BNN introduces prior knowledge on the weights of neural networks and estimates outcomes from the predictive distribution after calculating the posterior distribution of weights. Extensive experiments on the eICU collaborative research database (eICU-CRD) show that the proposed method is competitive and more capable of anti-overfitting.
Research of Big Data Analysis Platform Based on Positioning Information
Authors: Li Xiong , Wang Shiyun 
Published: September 2020

Abstract
With the wide spread of intelligent terminals, a variety of location-based business applications continue to emerge. We began to think about the analysis and application of location-based information big data, using big data technology to transform the existing industry, so as to better deal with epidemic disasters and other issues. For the novel Coronavirus, if artificial influenza virus infection or infection is detected by artificial means, it is easy to cause a series of problems, such as low efficiency, potential infection, and deliberately concealing travel history and contact history, which will have certain impact on epidemic prevention and control. Therefore, with the huge amount of data and the rapid growth of service demand, it is very urgent and necessary to isolate and confirm suspected cases based on big data analysis of location information. The big data analysis platform of location information can analyse the user’s location information, form the user’s trace, and provide more comprehensive information for epidemic control.
Identifier and Locator Separation Based Site Multi-homing Path Failure Recovery
Authors: Liu Feng , Gong Cheng , Tu Rui , Tang Huatao 
Published: September 2020

Abstract
Multi-homing is one of the effective ways to defeat path failure and increase the reliability of site network service. However, limited by the TCP/IP architecture, multi-homing has not been well deployment. In this paper, we proposed a sit multi-homing path failure recovery method based on “Locator/Identifier Split” by adding mapping service and RTT cooperative detection mechanism to the edge router. In order to reduce the probing’s cost of the router’s resource and link bandwidth, we also optimized the probing algorithm by reducing unnecessary active probing. The results of simulation test proved that this method could effectively detect the path failure and performance drop including packets delay and packets loss, and made a fast path switch to ensure the normal running of the upper application services (e.g., FTP). We also gave a theory analysis of the overhead brought by the sit multi-homing path failure recovery method. It shows that the addition bandwidth cost is 0.036% of the simulation link bandwidth and our approach is practicable.
Global Scheduling Real-Time Parallel Tasks on Multicore Platforms
Authors: Lei Zhenyang , Lei Xiangdong , Long Jun 
Published: September 2020

Abstract
Traditional real-time task scheduling strategies only considers cores scheduling, and do not take in account memory access and cache effects on multicore platforms. In this paper, we propose real-time parallel scheduling (PRTTS) strategy on multicore platforms. Each task is represented as a directed acyclic graph (DAG). The priorities of tasks are assigned according to task periods (rate monotonic, RM). In PRTTS scheduling strategy, priorities of tasks that access memory are promoted over priorities of tasks not accessing memory. Tasks accessing the data in the cache is dynamically raised to highest priority. The results of simulation experiment show that proposed new scheduling strategy offers better performance in terms of core utilization and schedulability rate of tasks.
A Parameter Eigenstructure Assignment Design with Any Desired State Vector in Linear Time-Invariant System
Authors: Liang Bing , Wang Guosheng 
Published: September 2020

Abstract
The parametric design problem for eigenstructure assignment with arbitrary expected state vector is studied via state feedback in linear time invariant systems. The main idea of the considered problem is to design a parametric form of the state feedback, so that the designed closed-loop system has the desired eigenvalues, the desired eigenvectors and arbitrary expected state vector. Based on the parametric solution for a class of Sylvester matrix equation, the parametric expression of all the state feedback gain matrices is given in this paper. The free parameters including in the state feedback gain matrix can be further used to meet other performance requirements for the control system design. Finally, a simulation experiment is given to illustrate the effectiveness and simplicity of the proposed parametric method for the state feedback eigenstructure assignment problem with arbitrary expected state vector in the linear time invariant systems.
Simulation Research on High-Speed Railway Dropper Fault Detection and Location Based on Time-Frequency Analysis
Authors: Li Jin , Zhang Xuewu , Zhang Cheng , Tian Tiantian 
Published: September 2020

Abstract
In this paper, a machine learning detection method, namely, SVM-ICA, which aims to solve the fault identification of droppers in high-speed railway, was proposed based on time-frequency analysis. The proposed SVM-ICA method can be utilized to detect and locate the faulty droppers. In detail, the time-frequency statistical features of the senor data are firstly extracted and the significant features are selected to construct the training set. Secondly, based on the training set, the support vector machine (SVM) fault detection model is trained. Finally, the trained model is used for detecting faulty droppers, and further the position of the break string can be located by using the independent component analysis (ICA) method. Simulation results show that the proposed fault detection method of droppers based on time-frequency analysis can accurately identify if the string breaks, and additionally can locate the position where the failure occurred.
A Rapid Method of Monocular Image Measurement Based on Rectangle Information
Authors: Ping Zhuolin , Yue Zhenjun 
Published: September 2020

Abstract
A method of rapid measurement of monocular image based on rectangle information is proposed in this paper. Based on the pinhole imaging model, a fast method of calculating the object size in monocular image is derived by fitting the straight line and then finding the intersection point of the straight line. The algorithm can not only measure the size and absolute depth of the object in monocular image, but also meet the conditions of the specific pinhole imaging model with a distance far greater than the focal length. The complexity of the algorithm is low, and it can meet the requirements of real-time calculation. It provides a new idea for the use of small and micro-computing platform.
Big Data Mining Analysis of Key Indicators of Online New Energy Vehicles
Authors: Gu Caidong , Liu Zhaobin , Fang Ligang 
Published: September 2020

Abstract
In this paper, more than 300 new energy vehicle key indicators of online sales are mined, and big data visualization analysis is carried out. In October 2018, big data mining analysis was conducted on the brand, model, pure electric endurance, slow charging time, fast charging time, battery capacity, maximum power, maximum torque, monthly sales volume, annual cumulative sales volume, monthly transaction price, user score, number of public praise, manufacturer guidance price (low) and manufacturer guidance price (high) of each new energy vehicle market in Sina auto.com. The results show that the average pure electric endurance of new energy vehicles is 210.3 km (with a maximum endurance of 613 km), and the average fast charging time is 1.7 hours (with a minimum duration of half an hour). The best-known brand of new energy vehicles is BYD, with the highest market share of BYD and Toyota.
Research on Formation Keeping of Multi-rotor UAVs Based on Improved Virtual Structure Method
Authors: Li Long , Sheng Wei , Hu Chen 
Published: September 2020

Abstract
Multi-UAV cooperative formation flight (CFF) can greatly improve the damage resistance and mission completion efficiency of single UAV, so it has become a research hotspot in recent years. The entire process of multi-UAV formation flight can be decomposed into target tracking, formation keeping, obstacle avoidance and collision avoidance. The research of formation keeping mainly uses the leader-follower or virtual structure method. The former has the problem of single point of failure, and the formation error will be amplified step by step. The latter is difficult to select virtual leader, and is hard to adjust the formation adaptively with limited communication conditions. Based on the virtual structure and multicast method, this paper proposes a recursive fusion strategy which reduces the requirement of communication frequency between UAVs, increases the adaptability of formation to single point of failure, and improves the accuracy and adaptability of formation keeping to a certain extent. On this basis, the cascade PID model is used to design the formation controller. Finally, the effectiveness of the strategy is verified by using MATLAB.
An Empirical Study on the Phenomenon of Information Narrowing in the Context of Personalized Recommendation
Authors: Huang Yijun , Zhou Lan , Zeng Ziqian , Duan Lingli , Wang Jiayu 
Published: September 2020

Abstract
Personalized recommendation services have been widely applied to improve users’ search efficiency, but when filtering information for users, they also hide the risk of the declining in content diversity, which is information narrowing. In this study, comprehensive news and information platforms in China were taken as the empirical research object. The experimental tracking method was used to collecting the content data of client end recommended news. Simpson and Shannon-Wiener indexes were used to measure the information narrowing phenomenon. Questionnaires were used to collect users’ data of news and information applications usage, the perception of information overload, and the perception of information narrowing. The data of questionnaire was analyzed and processed by MATLAB and SPSS 20.0, and the hypothesizes were tested by statistical methods. The results reveal that there is indeed information narrowing phenomenon in news information personalized recommendation service, but most users are unaware of this.
The Best-Worst Ant System for the Lock Chamber Arrangement Problem
Authors: Liu Ruijie , Qin Fang , Li Lin , Bo Yu 
Published: September 2020

Abstract
There are many rivers in china, and the inland navigation resources are abundant. With the fast development of the inland waterway transport and the increasing of quantity ships, it is obviously exposed the deficiency of inland river ship locks navigation capacity. In order to improve the ship lock navigation capability, to ensure safe, convenient, stable and orderly navigation through the ship locks, this paper used a best-worst ant system to solve the lock chamber arrangement optimization problem, established the best-worst ant system for the lock chamber arrangement, implemented corresponding algorithm through further design, and tested the validity of it through the Yangtze Gorges lock chamber arrangement and the Qingyuan Water Conservancy lock chamber arrangement. The calculation results show that the best-worst ant system is effective in application of the lock chamber arrangement.
Multi-source Information Data Fusion Method under Complex Battlefield Situation
Authors: Wang Deyi , Zhang Jisheng , Zheng Shicheng 
Published: September 2020

Abstract
Under complex battlefield situation, the situation information is instantaneous and changeable. The uncertain information always causes difficulty in acquisition information and miscalculation, and lead to low efficiency and poor accuracy for aircraft situation awareness. In order to solve the problem, in this paper it proposes a deep-learning based multi-sensor situation awareness data fusion method. With data acquitted from multiple sensors in multi-band and multi-angle, it realizes multi-sensor information data fusion by comparing and analysing of two AI data fusion methods that one is based on classical evidence theory and the other is based on deep learning. The simulation results indicate that the deep-learning based data fusion method presents higher efficiency in dealing with the environment information fusion with large evidence conflict.
College Students’ Physical Fitness Test Data Analysis, Visualization and Prediction Using Data Mining Techniques
Authors: Liu Yanqin 
Published: September 2020

Abstract
Physical Fitness test analysis is crucial in the field of kinematics and health science, the success of which can potentially help efficiently explore students’ physical issue and promote better physical education as well. Considering that the physical fitness of many college students is declining, it has become urgent to conduct a scientific, rational, and exploratory analysis of physical fitness testing data. In this paper, we visualized and explored several data mining algorithms to learn the relationship among different test items and uncover some potential patterns. Experiments are conducted on a real dataset from department of sport, Xi’an Fanyi University. The experimental results show that Catboost outperforms existing approaches in terms of prediction accuracy and F measure. The promising results will effectively evaluate students’ fitness condition, provide insights on correlation of different test items and assist educators for decision making.
Synthesizing Patterns for Videos Based on the Adversarial ConvNet
Authors: Tian Wei , Lv Jingyuan 
Published: September 2020

Abstract
In this paper, we proposed the application of the adversarial ConvNet mode with back-propagation algorithm and weighted method for synthesizing dynamic patterns. A multi-layer spatiotemporal filter is designed in the adversarial ConvNet to capture the spatiotemporal information that may be lost in the convolution phase. The model defines probability distributions. The problem of pattern design is to obtain the best synthesis by training these probability models and maximizing the objective likelihood function. In the training phase, multiple layers parameters of filters are updated through the matching between the synthesized videos generated based on the adversarial trained ConvNets and the observed training videos. The maximum likelihood learning algorithm is used to realize the synthesis of real natural image patterns. The effectiveness of this method is evaluated on public DynTex datasets and compared with other technology through the experimental results.
Retrieval of Ocean Wave Parameters Using Envisat Advanced Synthetic Aperture Radar Data and Its Verification Analysis
Authors: Liu Xiaoyan , Yang Qian , Wang Shuying , Zhou Yan 
Published: September 2020

Abstract
Based on MPI iterative retrieve algorithm, we retrieve the 2-D (two-dimensional) ocean wave spectrum by using Envisat ASAR wave mode Single Look Complex (SLC) imageries. The first guess spectrum is a necessary input item to run this MPI algorithm. In this paper, the first guess spectrum was calculated with reanalyzing wind data of ECMWF (European Centre for Medium-Range Weather Forecasts) and NCEP (National Centers for Environmental Prediction) as input wind stress of WAM Model. The Significant Wave Heights (SWHs) retrieved from the first guess spectrum as well as the best fit spectrum corresponding to ECMWF and NCEP wind field respectively are compared with the measurement data from buoys all over the world ocean. The comparison shows that the SWHs retrieved by ECMWF wind are less than field measurement buoy data in general, while the SWHs retrieved by NCEP wind are greater than those buoy data, but the mean SWHs of them fit with the field buoy data are better than whichever pro. However, the MPI algorithm makes the retrieved SWHs more accurate in comparison with the first guess spectrum derived from either ECMWF or NCEP wind field. Moreover, the relative error of the MPI method is improved by ∼12 percent with respect to the first guess spectrum.
Research of Modulation Feature Extraction from Ship-Radiated Noise
Authors: Wang Hongling , Guo Lihua 
Published: September 2020

Abstract
Information degree of the future naval battle will be very highly, and Striving for information advantage is the foundation of winning modern naval battle. Accurate and prompt recognition is the premise and foundation of tactical command. Modern naval battle is fast and complicated, and submarine sonar is required to be able to detect and classify targets fast and precisely. So the passive target detection and identification is the important research part in the modern acoustic technique field. Firstly, the mechanism of ship-radiated noise is analyzed. The envelope detection model of the ship noise is built. The envelope spectrum is extracted. Through the purity of the envelope spectrum and the smoothness of the continuous spectrum, I separate the line spectrum, from which the target base-frequency related with the rotate speed of ship screws can be detected. Secondly, the method of extracting frequency features of the ship radiated noise envelop is presented by using double frequency method, which is the basis of the classification.
Research on Body Mass Estimation Method of Koi Broodstock Base on Feeding State Image Recognition Technology
Authors: Ma Yinchi , Wei Wei , Zhou Chao 
Published: September 2020

Abstract
Koi is graceful and colorful. In the process of breeding, monitoring the growth state and predicting the body mass of the broodstock are both important. The broodstock will surface on the water while being feed. At this time, the digital camera captures the feeding state image rapidly. The head and mouth of the broodstock show the semi-spindle shape and bright color significantly which have very high contrast with the water background in the image. Based on the method of OpenCV image visual, feature triangle was defined based on the extracted colorful semi-spindle shape automatically. The projection area of the triangle in the image was computed through the vector graphics calculation by computer. A great deal of measurement data was taken analysis on the correlation between the shape parameters and the body mass of the broodstock by MATLAB. A correlation analysis model was established between the projection area of the feature triangle and the body mass. Through this method, the body mass of the broodstock in the aquaculture pond was estimated automatically. The estimating error of the body mass was lower than 9 percent. This method will reduce the workload of collecting the growth state data during the breeding process of the broodstock greatly. And the method can also improve the accuracy of the body mass estimating effectively, which has important production and application value.