bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract

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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.