bluuebunny/crossref_metadata_2025_split huggingface dataset:
DMC-PID Cascade Control Algorithm for the Constant Rate Temperature Rising Process in Differential Scanning Calorimeter
Authors: Yang Yu , Jingtao Hu , Laixing Wei 
Published: June 2019

Abstract
In view of the poor tracking performance of conventional control method in differential scanning calorimeter during constant speed heating progress, a DMC-PID cascade control algorithm for differential scanning calorimetry is proposed. Firstly, based on the predictability and optimality of the DMC algorithm, the DMC controller is used in the main circuit to control the generalized object composed of the PID controller and the reheating furnace. Secondly, on the basis of the characteristics that PID controller can improve the dynamic of the controlled object, the PID controller is used in the secondary loop of the control system to improve the linearity of the controlled object. Finally, the proposed control method is applied to differential scanning calorimetry constant temperature control process. Experimental results show that the proposed method can effectively reduce the static error and improve the tracking performance of the control process.
Spatio-Temporal Context Tracking Algorithm Based on Correlation Filtering
Authors: Fang Qi , Zhou Hao , Zhang Lu 
Published: June 2019

Abstract
The Correlation Filter tracking algorithm is different from the traditional method based on target feature, which has high accuracy and fast tracking speed. In this paper, the Spatio-Temporal Context(STC) target tracking algorithm is combined with Correlation Filtering, the position of the target is predicted by using Correlation Filtering and the prediction result is corrected by using the STC target tracking algorithm. The target tracking algorithm combined with Correlation Filtering and STC can realize effective tracking when the target is occluded or there is an interference target. The experimental results show that the proposed algorithm can not only be applied to the tracking of visual targets in complex backgrounds such as illumination changes, target rotation and background region interference, but also have better robustness.
An Improved LeaderRank Algorithm for Identifying Critical Components in Service-Oriented Systems
Authors: Jiang Shuo , Zhang Xiuguo , Cao Zhiying 
Published: June 2019

Abstract
Identifying critical components of Service-Oriented Systems is of great significance to the overall reliability of the system. As the size of software systems increases, identifying critical components can reduce the number of components that need to be predicted and shorten the prediction time in the process of predicting system reliability. Moreover, predicting the reliability of critical components can also ensure the stability of system. Therefore, we propose a method for identifying the critical components in Service-Oriented Systems. This method transforms the interaction between service components of Service-Oriented Systems into service dependency graph. An improved weighted LeaderRank algorithm is used to measure the importance of components and obtain the sequence of critical components. Through experimental analysis, the method can accurately and efficiently identify critical components in the system.
Weight Adjusting Algorithm Based on Sensor-space-time Quantum for Air-Information Fusion Precision Evaluation
Authors: Xiao-chao Wu , Ying Cheng , Long-fei Cui , Yun-ge Tang 
Published: June 2019

Abstract
The precision index in the performance test of air-information fusion system is one of the important indexes, which benchmark to be determined in complex environment is difficult. This paper proposes a space-time weight adjusting algorithm for the air-information fusion system to solve this problem, as follows: The first is to build the weight priority by the magnitude of radar detection precision; the second is to find the sets of the radars’ time domain that play a dominant role during fusing; the third is to calculate the time cumulate value of radar precision; the fourth is to use the time cumulate value of radar precision and fusion track to allocate all the radar precision weights, and calculate the reference precision by weighting the radar detection data precision; the last is to evaluate the precision index according to fuzzy evaluation method. This algorithm applies to the precision evaluation of the air-information fusion system under the static simulation or dynamic flight conditions with good adaptability and credibility. The simulation shows its value.
Review of Polar Integrated Navigation Algorithm
Authors: Ming Tian 
Published: June 2019

Abstract
Developing the research of navigation and positioning in high latitude area has important political, economic and military value, and it is of great significance to realize the global navigation of inertial navigation system. In this paper, the basic algorithms suitable for polar navigation are summarized, various polar integrated navigation methods are compared, and the research of inertial / astronomical integrated navigation with small error, high accuracy, wide application is particularly introduced.
Maximum Entropy Image Segmentation Method Based On Improved Firefly Algorithm
Authors: Liu Qianru , Jiang Zhanjun , Shi Haoqiang 
Published: June 2019

Abstract
An adaptive parametric firefly algorithm is proposed for the premature convergence of the firefly algorithm itself and the late oscillation of the algorithm iteration. In the image threshold segmentation test experiment, the algorithm is used to optimize the maximum entropy image segmentation, and the maximum entropy and the basic firefly improved maximum entropy algorithm are compared. At the same time, two important indicators of image segmentation evaluation index regional consistency are used to evaluate the results. It shows that the experimental results of the algorithm have better intra-regional consistency and noise immunity.
Research on Weighted Clustering Algorithm for Ad Hoc Networks Based on Topology Correlation
Authors: Shan Luchao , Zhang Luyong 
Published: June 2019

Abstract
In order to improve the clustering performance of wireless networks, an improved multi-parameter weighted clustering algorithm named TCWCA was proposed. The algorithm takes into account the calculation factors of node degree, inter-node distance, node mobility, node energy, and the increment of network topology association in the clustering process. In addition, the algorithm applies the increments of regional topology association of network nodes to the process of calculating weighting factors. The simulation results show that the algorithm has better clustering performance and improves the stability of the network clustering structure.
Ternary Search Trie based Algorithms for Recognizing the Names of Power Devices
Authors: Liu Yi , Xu Jiahui , Gong Xiujun , Bai Jingjie , Xiao Linpeng , Zhu Hongqin , Tan Chen , Lou Tianyue 
Published: June 2019

Abstract
With the development of the smart grid technology and the advent of the big data, it is highly desired to develop efficient computational methods for mining the values behind of huge volumes of data on the grid. However non-unified naming of the electronic devices from the de-centralized and heterogeneous power systems makes the procedure a hard problem. In the present work, we proposed a Ternary Search Trie based algorithm for recognizing the devices from the huge volumes of transaction data in the power grid. On the base of well-defined professional vocabulary of electronic devices, the algorithm scans and segments records having names of devices using the Ternary Search Trie method. An integrative scoring schema combing semantic and word order similarity is designed to evaluate the segments against the target device names. Experimental results indicate that the proposed method achieves only 3.13% error in parsing stage, and higher recognition rate. Moreover, the algorithm lists top-n matches for professional judgments to make the work more useful and rigorous.
A Spectrum Allocation Algorithm Based on Non-cooperative Game
Authors: Zhang LuYong , Wang Chao 
Published: June 2019

Abstract
Aiming at the problem of spectrum allocation among cognitive users, this paper proposes a spectrum allocation algorithm under non-cooperative game conditions, and presents a non-cooperative game Nash equilibrium algorithm, which can be iterated faster. Under the model and system settings, the improved fireworks algorithm is used to solve the spectrum allocation problem among cognitive users, and the utility function of cognitive users is improved to ensure the fairness of spectrum allocation.
A Node Localization Algorithm in Wireless Sensor Network
Authors: Fang Yanfeng , Zhang Yijiang 
Published: June 2019

Abstract
In order to solve the problems such as complex topology computation and low positioning accuracy induced by the random error factors in the localization process of Wireless Sensor Network(WSN), a new node localization algorithm for WSN is proposed. Firstly, the preliminary position estimation of positioned network node is finished based on the linear parameter evaluation mechanism and evaluates the received anchor node information to calculate the linear parameter value for searching the best anchor triangle, then the precise positioning of the node is done by using the weighted evaluation method and triangle positioning parameters of different anchor nodes to multiple weighted average for obtaining the accurate location information of nodes. Simulation results show that this algorithm has higher localization accuracy and lower error compared with DV-hop algorithm and DV-distance algorithm.
A High-Precision Moving Target Tracking Algorithm Based on UKF
Authors: Yao Ling , Peng Xia , Wang Enliang 
Published: June 2019

Abstract
Since Kalman Filter is not applicable to non-linear systems, a moving target tracking algorithm based on Untracked Kalman Filter (UKF) is proposed. The algorithm performance is analyzed by algorithm principle analysis, building mathematical model of the system, and simulation GUI design. Simulation results show that the algorithm has an expected tracking precision and high reliability in 6-dimensional CA mode, and has a good application prospect.
Research on keyword indexing algorithm based on big data
Authors: Hui Baofeng 
Published: June 2019

Abstract
Keyword retrieval is widely used in various aspects of Web information, such as information processing, data mining, etc. Keyword search has been studied for a long time. Many research results have been obtained from keyword search of early relational database, keyword search of semi-structured data and keyword search of graph data. Given multiple keywords, the goal of keyword retrieval is to find the matching node of keywords and find the most compact data fragment containing all keywords. For traditional retrieval model exists in the retrieval of data redundancy, the disadvantage of fuzzy matching, retrieval information deviation, combined with the current hot, discusses the heterogeneous data integration and redundant data, efficient data classification, keyword retrieval model and the method, such as to big data environment, make full use of the traditional technology combined with spatial retrieval model and other technical storage model, improved classification algorithm, and optimize the retrieval algorithm, thus improve the operation efficiency of the algorithm, to provide users with a set of data storage, classification and retrieval in the integration of large data retrieval platform.
Research on the Model of Lyric Emotion Algorithm
Authors: Xia Xiaoling , Gu Xin , Lu Qinyang 
Published: June 2019

Abstract
With the rapid development of Internet technology, various social networking platforms, especially mobile social networking platforms, continue to increase, resulting in a large amount of public opinion information. Internet public opinion has a clear emotional orientation, and its emotional orientation is very easy to spread and be infected, and even affect the development of the event. Aiming at the characteristics of lyric information rich and which are easy to change with time, the lyric theme analysis model and the lyric emotion evolution model are proposed. The LDA model is used to extract the topic from the lyric text in a period of time, and the sensational heat value is calculated according to the forwarding amount and the number of comments, and the lyrical theme with the highest heat is obtained. The relative entropy between sub-topics in the adjacent time slice of a specific hot topic is calculated, and the degree of association between the topics in the adjacent time slice is determined, thereby analyzing whether there is a split of the sub-topic and a new topic. Then the evaluation object is extracted, combined with the joint deep neural network model to judge the emotions of each evaluation object in different time, and the emotional evolution of the hot topic is analyzed from multiple dimensions. Finally, an example analysis of the network public opinion information from June to July 2018 is carried out to verify the validity of the above model. The model effectively solves the problems of immature emotion analysis model and low accuracy of emotion classification in the current public opinion analysis.
Research on Collision Avoidance Method Based on Image Recognition Technology
Authors: Ma Ziyan , Zhang Yan , Li Huiyuan 
Published: June 2019

Abstract
This paper describes the development of unmanned surface vessel (USV) collision avoidance technology, introduces the basic principles and key technologies of image recognition technology, analyzes the shortcomings of traditional collision avoidance technology, and explains the research status of the image recognition technology in the collision avoidance of unmanned surface vessel. The design ideas of the existing research and development results are summarized. Advantages and disadvantages are analyzed and compared. This paper focuses on the techniques of stereo vision, deep learning, multi-sensor fusion, and the method of unmanned surface vessel collision avoidance based on image recognition technology. The future development trend of this field is also discussed.
Exploration and Research on Computer Aided Translation Technology and English Translation Practice
Authors: Chen Guoping 
Published: June 2019

Abstract
In recent years, the rapid development of science and technology and economic globalization have made the demand for translation more and more in all walks of life. Therefore, computer-aided translation technology has received more and more attention. Combining modern information technology with traditional translation theory, this paper expounds the principle of computer-aided translation technology, and uses the combination of translation memory technology and mixed strategy of statistics and examples to realize computer-assisted translation system. According to the specific English translation practice research, it is proposed to use translation memory technology to optimize translation process, improve translation quality and translation efficiency, and hope to bring work help to the majority of people engaged in English translation, so that their work can be carried out more smoothly.
Color Quantization Application Based on K-Means in Remote Sensing Image Processing
Authors: Cheng Guojian , Wei Junjie 
Published: June 2019

Abstract
In this paper, four kinds of RGB remote sensing images are processed using color quantization algorithm based on K-Means to reduce the number of colors in the image. The color quantization algorithm is to select the most representative color and reduce the useless color in the image as much as possible. This paper assumes that the RGB remote sensing image is composed of multiple pixels. Using the K-Means algorithm to perform unsupervised clustering on these pixels with specific colors, color quantization can be realized. The use of K-Means for color quantization of remote sensing images can reduce the number of colors in those images, so that remote sensing images can be reproduced well in lower performance computer equipment. At the same time, color quantization reduces the size of remote sensing images and improves the efficiency of remote sensing image processing.
Web Service Recommendation Technology Based on Knowledge Graph Representation Learning
Authors: Qiao Xinghao , Cao Zhiying , Zhang Xiuguo 
Published: June 2019

AbstractThis paper proposed a recommendation algorithm based on knowledge graph representation learning (RABKGRL).The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. The relationship information of services is incorporated into the recommendation algorithm by calculating the distance between the service entities. The association between services that is not considered when using the collaborative filtering algorithm can be supplemented, and the recommendation effect is enhanced. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate and coverage rate of recommendation, but also solve the cold start problem to some extent.
Research on SAR Image Target Recognition Based on Convolutional Neural Network
Authors: Xinyan Fan , Weigang Zhu 
Published: June 2019

Abstract
A synthetic aperture radar (SAR) automatic target recognition can effectively improve the utilization efficiency of SAR image data. In order to improve the generalization and accuracy of SAR image target recognition, a convolutional neural network model is proposed to be applied to SAR image target recognition. Firstly, the network structure is designed for the characteristics of SAR image. Secondly, the introduction of dense block network structure improves the generalization performance of the model. Finally, Dropout reduced the computational complexity of the network and improved the generalization performance of the model. Experimental data were obtained from the United States Moving and Stationary Target Acquisition and recognition database. Experimental results of 10 types of target recognition showed that the overall recognition rate of the improved convolutional neural network is 99.18%. The convolutional neural network model proposed in this study improves the accuracy and generalization of the network, and is an effective method for target recognition of SAR images.
Method on parametric design technology of spacecraft assembly MGSE
Authors: Zhu Yahong , Li Xiaohuan , Zhang Chengli , Meng Shaohua 
Published: June 2019

Abstract
MGSE [1] (Mechanical Ground Support Equipment) is necessary equipment for spacecraft research and development. The traditional MGSE design method requires repeated modeling, analysis and drawing, so the traditional design method is inefficient for the design of stereotyped MGSE. This paper introduces a new parameter-based fast design MGSE method. This method extracts the key parameters to drive the establishment of the model. Parametric analysis can be carried out in the system. Compared with traditional design methods, the efficiency can be at least double.
Predicting the structural, electronic and mechanical properties of three-dimensional boron nitride foam containing sp, sp2 and sp3 hybridized bonds
Authors: Zheng Haibin , Bu Hongxia , Sun Lili 
Published: June 2019

Abstract
A first-principles approach is utilized to systematically investigate the structural, electronic and mechanical properties from a new BN phase (denoted super-(BN)16). Super-(BN)16 contains sp, sp

2
and sp

3
hybridized bonds. It is mechanically stable, even though it is energetically unfavorable than c-BN, w-BN, yne-BN, and so on. Due to the different hybridization type and the B-N covalent bonds with ionic characteristics, super-(BN)16 has unequal bonds and bond angles in these equal space positions. The calculated electronic structure showed it is a semiconductor with a direct band gap of 1.94 eV. The electronic states in the region near Fermi level mainly come from the 2p orbitals of the sp hybridized B and N atoms. The elastic constants and moduli of this phase exhibit clear anisotropy and super-(BN)16 should have a low hardness and high ductility. Additionally, it can be obtained from nanosheets, nanotubes and nanoribbons of yne-BN family under pressure.
Error compensation method of large size steel sheet measurement based on control field
Authors: Geng Lei , Yao Fucai , Wang Wen , Xi Jiangtao , Li Yaru 
Published: June 2019

Abstract
Aiming at the problem of low accuracy of large size sheet edge, a method of sheet size error measurement based on control field is proposed. Firstly, an error compensation model of the measuring system based on control field is established by analyzing the causes of the errors of the measuring system. A grid standard plate is designed and the error distribution on the grid line is obtained by using the measurement results of the standard plate. Secondly, the error curve is established according to the distribution, and the trig function theorem is used to project the curve into the image pixel coordinate system. Finally, the control field of the whole measurement area is reconstructed by linear interpolation, and the measurement results are compensated and corrected. The steel sheet is measured in the measuring area of 1.2m × 2.6m on the basis of these theories and technologies. The experiment shows that the precision of the measuring system can reach 1mm per meter, which satisfies the accuracy and speed requirements of large-size steel sheet measurement in industry, and has high application value.
Error Analysis of High-time-resolution Na Lidar Data and Power Spectrum Density of Mesospheric Na Layer
Authors: Wang Xiaodong , Yi Fan 
Published: June 2019

Abstract
The observed 6-s Na layer data at Beijing (40.2°N, 116°E), China was conducted error analysis and compared with 5-minute Na Lidar data located at Wuhan (30.5° N, 114.4° E), china. It was found that 6-s time resolution fluctuations/change rates of Na atomic density were geophysical phenomenon rather than measurement error. The relative measurement errors between Beijing and Wuhan are comparable around 90 km. Based on the long-duration (longer than 10 hours in an observational night) 6-s Na profiles, power spectral density (PSD) of the Na density fluctuations has been calculated. There exist strong fluctuations at frequencies larger than the typical value (2.5×10-3 Hz, equivalent to a period of ∼5 min) of the Brunt-Väisälä frequency in the mesopause region.
Application of Virtual Reality Technology in College Physical Education Teaching and Training
Authors: Liu Hao 
Published: June 2019

Abstract
With the continuous development of social science and technology, the application range of computers is wider and wider. The computer “virtual reality” technology is used in college sports training, which can enrich the content of physical education classrooms, update classroom teaching methods, and improve the quality and efficiency of physical education training. However, in the process of actual application, there are still some problems to be solved.