bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the Chinese Language and Literature Teaching Assisted by Computer Multimedia Technology
Authors: Tang Mingyu 
Published: August 2019

Abstract
The computer multimedia technology has changed the traditional teaching form, especially providing the special form to the Chinese language and literature teaching. By using multimedia technology, Chinese language and literature can make characters more vivid and three-dimensional, which promotes the improvement of teaching efficiency and effect. With the application of multimedia technology in modern teaching, its disadvantages are increasingly exposed, such as distracting students’ attention and weakening the teaching pattern. Through the "multimedia hot" cold thinking, this paper analysis the countermeasures on negative impact. We should develop multimedia teaching in a healthy, effective and sustainable way.
Comparative Analysis for Interval Modeling Algorithms of Wind Turbine Power Curve
Authors: Chu Jingchun , Yuan Ling , Hu Yang , Pan Lei 
Published: August 2019

AbstractConsidering the stochastic uncertainty of wind power generation, joint estimation of wind turbine power curve (WTPC) in point and interval forms is carefully studied in this paper. Firstly, inducing factors of uncertain data and outliers types in measured data are analyzed to show necessity of uncertainty modeling of WTPC. Then, theoretical principles of different methods for WTPC modeling such as Gaussian Bin method, kernel density estimation (KDE), conditional kernel density estimation (CKDE), conditional probability via Copula, Gaussian process regression (GPR) and relevance vector machine (RVM), are seriously straightened up. Their theoretical essences are discussed and compared in depth. Subsequently, evaluation indexes for point and interval estimations of WTPC are defined to quantify modeling performance. Finally, using measured data from actual 1.5MW variable-speed variable-pitch (VSVP) wind turbines, simulations are executed to show diversities of these methods and their features are discussed from different views. Meanwhile, their potential application scenarios and usage strategies are carefully analyzed, including wind power prediction and operational performance evaluation of wind turbine in an infinite-time-horizon form or a sliding-window form. It is helpful to enhance development of WTPC modeling techniques.
Multilingual Focused Crawler System based on Web Content Extraction and Path Configuration
Authors: Wang Jie , Deng Sanhong , Wang Lijuan 
Published: August 2019

Abstract
The multilingual focused crawler system combines web content extraction with path configuration to make use of their advantages and achieve automatic collection of network information in multiple languages. Firstly, system selects foreign language keywords according to crawling webpage language and Chinese keywords, and uses initial link to obtain webpage information. Then, it uses path configuration information or web content extraction algorithm based on the distribution line block to get webpage content, and adopts rules or configuration information to acquire new links, published time and title. Next, keywords are used to filter irrelevant information. Finally, results are presented as a list. When users use focused crawler system, the webpage path information can be configured or not according to requirements, and the collected network resources can also be searched or filtered.
Fault Diagnosis and Model Predictive Fault Tolerant Control of Networked Control Systems with Random Packet Loss
Authors: Jiang Peng , Yao Lina 
Published: August 2019

Abstract
In this paper, a fault diagnosis and fault tolerant control algorithm is performed for the networked control system with packet loss problem. Firstly, the random packet loss phenomenon of the system is viewed as a binary switching sequence which obeys a Bernoulli distributed white sequence taking on values of zero and one with certain probability. When a failure occurs in the system, fault diagnosis is performed by designing a residual signal and an adaptive adjustment rate for the faulty system, and then based on the fault diagnosis information, model predictive control is used for fault tolerant control for the system. By optimizing the solution of the model predictive control system, a suitable fault-tolerant controller is designed to complete the active fault-tolerant control of the system. Then the closed-loop stability of the system is analyzed. Simulation results are provided to show the effectiveness of the proposed method.
An Online Saddle Point optimization algorithm with Regularization
Authors: Xu Yue , Jiang Ying , Xie Xin , Li Dequan 
Published: August 2019

Abstract
This paper presents an online saddle-point optimization algorithm (OSP) to solve the optimal decision-making problem in economic games. In this setting, the two mutual competing players choose a pair of optimal decisions (Nash equilibrium) at each iteration. Firstly, the Follow the Leader (FTL) algorithm is proposed to update the decisions, and the regularization term is added to stabilize the Nash equilibrium for both players. Secondly, the saddle-point regret (SP-Regret) is used to measure the gap between the cumulative payoffs and the saddle point value of the aggregate payoff functions. To this end, this paper aims to minimize it. Finally, the simulation results show that, under the proposed OSP algorithm, the SP-Regret can still be sublinear with regularization and the decision variables of both players can be constrained to fluctuate within a certain range by adding regularization, which can effectively make the Nash equilibrium stable.
AT-LSTM: An Attention-based LSTM Model for Financial Time Series Prediction
Authors: Zhang Xuan , Liang Xun , Zhiyuli Aakas , Zhang Shusen , Xu Rui , Wu Bo 
Published: August 2019

Abstract
This paper proposes an attention-based LSTM (AT-LSTM) model for financial time series prediction. We divide the prediction process into two stages. For the first stage, we apply an attention model to assign different weights to the input features of the financial time series at each time step. In the second stage, the attention feature is utilized to effectively select the relevant feature sequences as input to the LSTM neural network for the prediction in the next time frame. Our proposed framework not only solves the long-term dependence problem of time series prediction effectively, but also improves the interpretability of the time series prediction methods based on the neural network. In the end of this paper, we conducted experiments on financial time series prediction task with three real-world data sets. The experimental results show that our framework for time series pre-diction is state-of-the-art against the baselines.
Research on K-means Algorithm Optimization based on Compression Learning
Authors: Shuai Cai , Lei Zhu , Zeng Weijun , Yu Re , Xiao Zhao 
Published: August 2019

Abstract
The K-means algorithm is one of the classical algorithms of clustering. However, as the data set increases, the computational cost of clustering becomes higher. The orthogonal matching pursuit algorithm is a classic signal reconstruction algorithm. The paper improves its algorithm based on compression learning and applies it to the K-means algorithm, which uses the sketch of the original data set to estimate the cluster center. The experiment results show that the clustering effect of this method is similar to that of K-means algorithm, because the size of the sketch is independent of the size of the original data set, only related to the number of centroids K and the dimension n of the data, which reduces the computational complexity of the algorithm. For large data sets, experiments show that the improved algorithm is more optimized than the traditional K-means algorithm.
Evaluation of Teaching Effectiveness Based on Gray Markov Chain in the Context of MOOC
Authors: Li Zhang 
Published: August 2019

Abstract
Markov chain is an analytical method to study the law of change between different states of an event in a random process. In this paper, the GM (1, 1) model and Markov model are combined under the background of MOOC to construct the gray Markov model. The Grey Markov model evaluates the using effect of MOOC in the teaching process.
Granular computing and reasoning based on rough logic
Authors: Wu Yanhong , Bai Jianxia , Shen Liping 
Published: August 2019

Abstract
This paper makes an effective combination of rough set and granular computing, and proposes a logic inference form based on rough granular computing. The advantage of rough logic granular computing lies in its demonstration form which is given by array, it can illustrate the formula semantic more directly, and furthermore the reasoning results can be got according to the data information searched momentarily.
Chinese classification code: O159 Document identification code: A
A threshold segmentation method for non-uniform illumination image based on brightness equalization
Authors: Guo Hui , Chen Peng , Huang Shun , Hu Cong , Zhang Lijun 
Published: August 2019

Abstract
In the process of image acquisition, non-uniform illumination images are common due to poor lighting, surface reflection, or a combination of these two factors. In order to improve the quality of image segmentation, an image threshold segmentation method is proposed for non-uniform illumination images. First, the brightness of different regions in the image is compensated to make the brightness background of the whole image consistent. Second, the gradation histogram of the processed image is obtained and simulated into Gaussian distribution curve, then the inflection points of the curve are calculated. Finally, the two inflection points are set as thresholds to segment the image after brightness equalization. This method eliminates the influence of non-uniform illumination to a certain extent and gets a better segmentation effect. Experiments were carried out on images containing several different types of non-uniform illumination. The results demonstrate that the proposed method outperforms the compared enhancement algorithms in threshold segmentation.
Image retrieval based on convolutional neural network and linear discriminate analysis
Authors: Xia Wei , Hu Cong , Cheng Xianghui , Chen Peng , Jin Xing 
Published: August 2019

Abstract
Image retrieval is a hot research topic in the field of computer vision image processing, and the user queries the image database for similar images and produces a list of recommendations. The paper firstly sets forth the research status of image retrieval, then the convolution neural network is briefly introduced. Due to the traditional image retrieval and recommendation system use manual extraction of image features is relatively cumbersome, and the retrieval accuracy is not high research status, the paper proposes an image retrieval method based on the improved convolutional neural network and linear discriminate analysis. Caltech256 and CIFAR-10 datasets were trained using the model in this paper, experimental, results show that the proposed method can effectively improve the performance of retrieval.
Dynamic multi-objective optimization of battlefield support resources based on Interactive Genetic Algorithm
Authors: Ning Lei , Jiping Cao , Yinjie Gao , Qingxiao Wang 
Published: August 2019

Abstract
With the deterioration of the international environment and the intensification of military conflicts, the scale, type, direction and emphasis of military activities are constantly changing. which requires the corresponding adjustment of maintenance and support resources to ensure that the wartime support tasks are met in various battlefield environments. Based on the background that the support demand and battlefield situation have changed and the original resource support scheme can not meet the needs of wartime support tasks, this paper readjusts the support resources dynamically to ensure the strong support of combat forces, constructs a dynamic multi-objective optimal allocation model of battlefield support resources, formulates corresponding strategies, and presents a heuristic algorithm for solving the model.
Model Parameter Identification of FCC Reaction Regeneration System Based on Affine Bat Algorithm
Authors: Li Zhe , Chu Jizheng , Chu Minghui , Liu Hongjuan 
Published: August 2019

Abstract
In real industrial, due to the existence of measurement error, noise interference and other factors, the traditional bat algorithm can not effectively obtain the mathematical model of the actual system. In order to solve this problem, this paper proposes an affine bat algorithm. The bat algorithm is introduced to find the median of the optimal value range of the parameters, and the affine algorithm is used to calculated the value radius of the parameters. In order to make the radius shrinking more flexible, a new interval shrinking strategy was added. Then, the algorithm is applied into the catalytic cracking reaction regeneration system. The effectiveness of the proposed algorithm is verified by comparison with industrial field data.
Coal blending optimization for power plants with particle swarm algorithm
Authors: Gao Shuci , Li Bin 
Published: August 2019

Abstract
Optimizing coal blending strategy is important for increasing the running efficiency and lowering down the emissions of utility boilers. A model, considering price, calorific value, ash content, volatile matter content, moisture content and sulfur content of the coal, has been established using quantum-behaved particle swarm optimization algorithm. The calculation result showed that, compared with the particle swarm algorithm, the quantum particle swarm had better global search capability and astringency, the optimal coal blending ratio can be quickly searched at reasonable boiler running cost. The blending mode is in line with the actual requirements, and the algorithm has high stability.
Construction of Construction Engineering Cost Cloud Service Platform and Its Application
Authors: Liu Yuqin 
Published: August 2019

Abstract
With the continuous development of industrial informationization, engineering cost informationization has become more and more important. However, as the amount of engineering cost information increases and changes constantly, engineering cost information faces great challenges. In this paper, we firstly analyze the advantages of cloud computing in the field of engineering costing, and then combine cost information with cloud computing technology to build a cloud service platform for engineering cost information services. The construction of the project cost cloud service platform can not only lead the development trend of the industry information service, to effectively meet the extensive needs of enterprises for building materials and materials, but also greatly reduces the cost of intermediate links and the asymmetry of information, so that the losses incurred are conducive to promoting fair and open market competition.
An Ensemble Learning Method for Dialect Classification
Authors: Ye Shuai , Zhao Ruoyan , Fang Xinru 
Published: August 2019

Abstract
Dialect Classification Task is the first step of the Multilingual Automatic Speech Recognition System. Because of the difference of accent between dialects in different regions, the problem of Dialect Classification is a very challenging one. Dialect classification is widely used in information processing, military information retrieval and other fields. Therefore, the study of dialect classification is of great significance. This paper proposes an ensemble learning method for dialect classification. Firstly, the low accuracy of dialect data sets is processed and amplified. Then, three models, GRU, CNN and DNN, are used to classify dialects respectively, and the final dialect types are determined by voting. The accuracy of dialect classification by this method is higher than that of the single model with the best performance, the validity of the model is verified.
The Least-squares Algorithm for Interferograms with Random Phase Shifts
Authors: Zhai Zhongsheng , Li Jinsong , Zhang Yanhong , Lv Qinghua , Wang Xuanze 
Published: August 2019

Abstract
In surface shape measurement with phase-shifting interferometry, the extraction accuracy of initial phase is significant and it will influence the correct of measurement result. A least-squares algorithm is proposed to solve the parameters of interferograms with random and known phase shifts. It can rapidly extract the initial phase, modulation amplitude and background intensity without any iteration. Numerical simulations and optical experiments are implemented to demonstrate the performance of the proposed method. The simulation and experiment results show that the presented method is effective, rapid and accurate.
The development and application of computer aided software in college physical education
Authors: Yang Guiqi 
Published: August 2019

Abstract
The development and application of computer aided software in college physical education has many benefits, such as, it is helpful for stimulating students’ subjective initiative and improving their innovative spirit and practical ability. The computer aided software can promote the physical education teaching in colleges and universities to be modernized, scientific and standardized. This paper first defines the meaning of computer aided software application, and then it analysis the frame design and ADDIR modeling. Finally, this paper briefly analysis the application of CAI and CAT to improve the overall teaching level of college physical education.
Analysis on Research Frontiers and Hotspots of “Artificial Intelligence Plus Education” in China --Visualization Research Based on Citespace V
Authors: Jing Liu , Ruyu Xie , Anling Song 
Published: August 2019

Abstract
With the rapid development of artificial intelligence technology, the combination of artificial intelligence and education attracts increasing attention and thereby more and more scholars start their research on “artificial intelligence plus education”. Through analysis of knowledge graph with Citespace, a tool utilizing visualization approach analyzing research base, frontiers and hotspots, it could be learned that the research frontiers of “artificial intelligence plus education” mainly include “application of artificial intelligence education, smart education, big data of education” and the main research hotspots consist of “higher education, education reform, intelligent education, vocational education, big data of education, application of artificial intelligence education”, based on which the future development trend of “artificial intelligence plus education” is also predicted.
Development Trend of Software Process Model
Authors: Hao Liangliang , Zhang Jinsheng , Ma Xiaoyu 
Published: August 2019

Abstract
With the continuous development of software technology, the scale and complexity of software have never been higher, and the difficulty of design and development has also increased. The software process changes the chaotic situation of software development and provides an effective roadmap for the software development team to make the development process more controllable and stable. This paper briefly introduces the software process, and introduces the existing software process model in detail. Finally, the development trend of software process model is forecasted.
Teaching Research of Single-chip Microcomputer Principle Based on Knowledge Architecture
Authors: Hao Shangfu , Ma Yanli , Sun Baili 
Published: August 2019

Abstract
According to the “cross penetration” organization way, this paper will take the undergraduate “system ability” training as the goal, comb the relevant knowledge of many core courses for effective connection, construct complete knowledge system of this course, and draw the knowledge point connection. This teaching mode makes clear the skeleton of the learning content, carries out the image concretization of the abstract knowledge, provides an effective idea for the construction and teaching of the single-chip microcomputer course, and provides a strong support for the student’ system ability, practice and innovation ability.
Research on Application Scenario of Large Data Cloud Service Platform for Power Energy Measurement
Authors: Zheng Angang , Ding Xunan , Shang Huaiying , Liu Yan 
Published: August 2019

Abstract
This paper studies the technical framework and composition of the large data cloud service platform for power energy measurement, and discusses the application scenario design of the platform for government, enterprises and users. Three typical application scenarios of city perspective, suspected power theft intelligent diagnosis and intelligent analysis and application of residential power load characteristics are discussed in detail. Finally, the development of cloud platform and its application in business field are prospected.
The Research on Big Data Platform based on Timetable Management
Authors: Xu Tong , Zhou Leishan , Bai Zixi , Li Weihua , Guo Bin , Wang Zeyu 
Published: August 2019

Abstract
Timetable is the core technical plan of the railway transportation organization, and it is the basis for the daily work. However, at present, China’s timetable management technology is relatively backward, which cannot meet the safety and efficiency of actual transportation. This paper analyzes the problems in the current situation of tiemetable management. Starting from the problem, the application research of big data platform is proposed. Firstly, the data is collected and normalized, then the safety analysis and index analysis are carried out, and finally the intelligent optimization is carried out.
Short-term load forecasting of BP network based on bacterial foraging optimization
Authors: Han Yikan , Xiong Hejin , Wei Di 
Published: August 2019

Abstract
The traditional BP neural network search algorithm based on gradient orientation is easy to fall into local minimum, the convergence process is slow, and it cannot guarantee the convergence to the global optimal solution. In order to improve the prediction accuracy of short-term load forecasting, this paper applies the bacterial foraging optimization algorithm to BP neural network, using its unique breeding and eviction operation, can improve the convergence speed of the algorithm, strengthen the ability to search the global optimal solution, and reduce the prediction error.
A method for software trusted update on network security equipment
Authors: Wang Guan , Yan Zhiwei , Chen Jianzhong 
Published: August 2019

Abstract
Network security equipment plays an important role in preventing network security attacks. However, with the change of network environment and the upgrade of network attack means, the protection ability of the security software on the existing network security equipment will gradually decrease with time. For some large organizations or enterprises, their information security operations centers lack attention to software version control and software update processes for network security equipment, resulting in some security crises in the software update process. In this paper, we propose a method for software trusted update on network security equipment. This method can provide trusted identify authentication, secure data transmission and effective software version control. It enables the network security operations centers to more safely manage software update process on network security equipment. This method uses the functions of Trusted Cryptography Module to provide trusted execution environment. In this paper, we had introduced the process design, the prototype design and theoretical analysis to explain the feasibility and safety of this method.