bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conversion Principle and Control Method of Synchro Signal
Authors: Lv Wenfa 
Published: July 2020

Abstract
The synchro is a kind of micromotor used for angle measurement. It can output analog signals containing angle information, which is widely used in the fire control system. In order to facilitate the processing, it is necessary to digitize these analog signals. In this paper,8255A is used as the main control chip, and high-speed, high-precision digital to analog and analog to digital converters are used for real-time analog acquisition or mathematical conversion of selsyn signals to realize real-time data communication between fire control equipment and external equipment, and selsyn signals are used for real-time data communication, The technology has the characteristics of flexible control, simple circuit, strong real-time, high reliability, and it has been widely used in the fire control system of naval gun, and achieved good results
A New Handwritten Number Recognition Method Using HMM Based on MNIST
Authors: Lu Ruhua , Li Yalan 
Published: July 2020

Abstract
Taking MNIST data-set as research object, the HMM is introduced into the Handwritten Number Recognition for the first time. After the implementation of the classical HMM training algorithm, some optimization methods are proposed for the problems existing in the training. The general random initialization parameters lead to long training time and unstable data. The training of initialization parameters based on observations can speed up the training and avoid data overflow. The number of iterations in the training process is not positively related to the output probability. In order to obtain an optimal model, after the algorithm converges when the cross entropy loss function of the output probability of two adjacent iterations is the minimum, the training ends. Finally, a comparative experiment between the classical method and the optimization method is carried out in the two stages of training and recognition. In the training stage, compared with the classical method, the optimization method has the advantages of short average training time, high average output probability, fast iteration speed and high accuracy. In the test stage, the accuracy of the optimization method is higher than that of the classical method. The experimental results show that the HMM can be effectively applied in the field of Handwritten Number Recognition. And through the optimization method, to a certain extent, the recognition accuracy is improved. In a word, the method in this paper is an effective and feasible method.
Analysis and Elimination of Common Interference in Satellite Communications
Authors: Liu Qianqian , Tang Baoyan , Wang Yuanyuan 
Published: July 2020

Abstract
Because the satellite communication system is an open system, the satellite’s frequency resources and transponder power are limited. Although there are international organizations and satellite companies in various countries for orbit, frequency, and power allocation and coordination, satellite communications cannot be completely prevented from being interfered. The well-known Falun Gong interference with Xinnuo satellite is an obvious example. Articles on satellite communication interference are scattered in various professional newspapers. This article will discuss the causes, judgment methods and solutions of common interference in satellite communication, focusing on the practical methods of solving interference.
Research on Knowledge Graph Model for Cybersecurity Logs Based on Ontology and Classified Protection
Authors: Tao Yuan , Li Moyan , Hu Wei 
Published: July 2020

Abstract
In order to audit and analyse cybersecurity events from massive logs, the knowledge graph model for cybersecurity logs is proposed. The data layer and the pattern layer of the knowledge graph model for cybersecurity logs is obtained based on ontology and classified protection. Entity classification of cybersecurity logs is extracted by using classified protection standards in the data layer of the knowledge graph. The relationship and attribute of ontology from cybersecurity logs is defined. The pattern layer of the knowledge graph model for cybersecurity logs is defined, classified protection data is integrated in entity alignment, and classified protection information gain is used in ontology construction. The structure of the knowledge graph for cybersecurity logs is given. So that the efficient association and deep mining analysis of cybersecurity logs are realized, and it can be directly analysed and processed on the data without the need for precise modelling of the problem. The efficiency of logs analysis is improved. The model has heuristic characteristics and generalization abstract ability, and the model is suitable for big data analysis of large-scale cybersecurity logs.
Exploration and Analysis on the Development Path of Smart Tourism in Shandong Province in the Era of Artificial Intelligence
Authors: Guo Ranran 
Published: July 2020

Abstract
In the new period, China’s tourism industry has entered a new stage of popularization and industrialization, the tourism mode has also changed from “scenic spot tourism” to “all-for-one tourism”, and the development of big data, Internet of Things, artificial intelligence, block chain and other new generation of information technology have promoted the transformation of traditional tourism into smart tourism. As a big tourist province, Shandong Province’s smart tourism developed earlier and achieved more; however, there are still some problems, such as insufficient attention, low popularity, and unbalanced development of cities. This paper takes the impact of artificial intelligence on the development of traditional tourism as the breakthrough point and explores the development path of smart tourism in the era of artificial intelligence in the next stage of Shandong Province through analyzing the internal need of smart tourism development in the era of artificial intelligence in Shandong Province.
An Empirical Study on the Digital Consumption of Luxury Goods Based on Outlets
Authors: Dai Hongyu , Xie Yutong 
Published: July 2020

Abstract
Outlets, a business model with a history of more than 100 years abroad, has shown a rapid increase in recent years. To explore the shopping needs of consumers in the luxury economy, at the same time, to improve the tourism competitiveness of outlets, this study uses quantitative research methods, taking outlets tourism industry and consumers as research objects, by using single-factor variance analysis, non-parametric test and variance homogeneity test, this paper analyzes the shopping willingness of consumers with different attributes and finds that the shopping willingness of consumers with low income is lower than that of consumers with high income; the shopping willingness of consumers with low educational level is lower than that of consumers with high educational level, and the shopping willingness of consumers with white-collar jobs is significantly higher than that of consumers with other jobs. In the light of the above conclusions, this study puts forward two suggestions for the flourishing tourism retail industry: it suggests that outlets managers should adopt targeted marketing strategies for high-income, high-educated and white-collar people and that outlets managers should pay attention to the important role of sales personnel in tourism retail consumer service.
Research on Side-Channel Attack Based on the Synergy between SNR and Convolutional Neural Networks
Authors: Liu Zixin , Zhu Ting 
Published: July 2020

AbstractThe data encryption process in the encryption chip will be leaked by means of timing operation time, probing data collecting operation power and electromagnetic signal interception, which makes side channel attack possible. Nowadays, the research on template creation of template attacks has shifted from Gaussian distribution to the use of machine learning algorithms to create templates. With many parameters, it is difficult to find a suitable network structure. Based on many experimental studies, the experience of a convolutional neural network structure suitable for side channel analysis is summarized and proposed.
Research on Side-channel Attack Based on Stochastic Model with Lower Leakage
Authors: Liu Zixin , Zhu Ting 
Published: July 2020

Abstract
Cryptographic algorithm as an important means to ensure information security transmission has been widely concerned, but as the carrier of cryptographic algorithm, the security of encryption equipment is easy to be ignored. With the wide application of encryption equipment in modern society, the existence of side channel analysis technology is a great threat to information security. Stochastic approach (SA) is a method to deal with side-channel attack, based on many experimental studies, combined with Chebyshev filter, it can improve the accurate for attacking and signal to ratio.
Four Arithmetic Operations on the Quantum Computer
Authors: Zhang Yong 
Published: July 2020

Abstract
Quantum computer has been proved to be superior to electronic computer in solving some NP problems. Based on the former quantum adder, this paper proposes a new quantum adder and quantum subtracter, and then designs a fixed-point quantum multiplier and a fixed-point quantum divider based on fixed-point number operations. Four arithmetic units are presented using quantum gate circuits. These researches lay a foundation for the quantum implementation of digital filters.
Research and Implementation of Mesh Model Deformation Simulation Technology
Authors: Wang Zhongmin , Cheng Jing 
Published: July 2020

Abstract
According to the requirement of deformation simulation of polygon mesh model which is easy to operate and control, the design idea of deformation simulation is given and realized. The deformation technique implemented in this paper is to calculate the deformation migration shape variables by acting on the local meshes to be deformed. During the deformation process, an adaptive subdivision mesh technology is proposed to solve the problems of insufficient mesh density and mesh vertices. A model cutting simulation technology is proposed, and a cover cutting simulation implementation is given for the polygon set without holes. This article uses the half-edge structure of the grid to reduce the time consumption of querying and editing the grid.
Aircraft Maintenance Training Simulation System Based on “CAN Bus + Ethernet” Architecture
Authors: Zhu Dan , Zhang Wei , Ren Zaiqing 
Published: July 2020

Abstract
Aiming at the problem of centralized data collection scheme, a distributed data acquisition and control scheme based on “CAN bus + Ethernet” is proposed. According to the functional structure design, the cockpit control equipment is defined as independent data acquisition and control nodes on the communication network. The architecture and related key technologies improve the reliability, maintainability and testability of the system. The application of this system to carry out training shortens the training period of aviation ground handling personnel and saves a large amount of maintenance and support funds.
Blind-Depth Light Field Super-Resolution
Authors: Zhang Lei , Fan Jianpeng , Yang Jungang 
Published: July 2020

Abstract
Light field cameras have drawn much attention due to the ability of post-capture adjustments such as refocusing and 3D reconstruction. However, the low resolution has been the bottleneck of them and limits their further application. To solve this problem, various methods have been proposed to increase the resolution, namely, to recover details and reconstruct a high resolution (HR) image of the scene from several low resolution (LR) observations. Most of the previous super-resolution (SR) frameworks depend on prior knowledge of depth information, which is an indispensable part in their SR approaches. However, it may be difficult to obtain precise depth information in some practical situations. In this paper, we propose a novel light field super-resolution (LFSR) framework independent of the prior depth information. The framework combines a variational regularization-based SR approach with light field refocusing process, which can super-resolve the focused region while preserve the unfocused region and generate a series of multi-focus super-resolved images. We then employ a multi-focus image fusion algorithm based on stationary wavelet transform (SWT) and finally obtain an all-in-focus HR image. Synthetic and real-world datasets are utilized to demonstrate the effectiveness of the proposed framework both quantitatively and visually.
Seawater Temperature Outlier Detection Strategy Based on Wavelet Analysis for Solving the Migration Problem of Fishery Companies
Authors: Liu Yuan , Qiu Zheyong , Wang Zhongyue , He Jiaqi 
Published: July 2020

Abstract
As the global ocean temperature rises, many marine organisms begin to find new habitats. The problem of fish migration has a profound impact on the development of fisheries. This paper uses wavelet anomaly analysis method to find the abnormal value of seawater temperature, uses discrete wavelet decomposition and reconstruction to de-noise the time series, and predicts the age of the fishing company. Firstly, we conducted continuous wavelet analysis of seawater temperature data to obtain the relationship between seawater temperature change rate and time. We performed Fourier transform to obtain its transform function, and then reconstructed the sequence to obtain wavelet coefficients of seawater temperature transformation speed. After that, we used wavelet coefficients to conduct simulation screening to obtain all outliers. In this way, the time for bankruptcy under the best and worst conditions of the fishing company can be obtained. Although the model comprehensively considers many factors, in order to establish a universal model, idealizing many influencing factors has certain limitations. The predicted bankruptcy time may have a certain error from the actual.
Morphological Enhancement for Remote Sensing Image Based on GAN Structuring Elements
Authors: Wang Xiaopeng , Wang Qingsheng , Yang Wenting , Yao Lijuan 
Published: July 2020

Abstract
A morphological image enhancement method based on the GAN (General Adaptive Neighborhood) structuring elements is proposed for some problems such as low overall contrast, noise and inter-target edge of some remote sensing images. Firstly, a pixel to be processed is selected as a seed point, and an adaptive neighborhood is defined according to the image pixel characteristic and the neighborhood constraint relation to obtain the self-adaptive structuring elements set. A corresponding self-adaptive morphological operation based on the self-adaptive structuring elements set is constructed with reference to the classical morphological operation definition, and the self-adaptive alternative filter is constructed by using the adaptive morphological operation combination so as to realize the enhancement of the remote sensing image. The experimental results show that the method can preserve the accurate positioning of the objects edge while enhancing the image, avoid the loss of information, has better noise removing performance. Compared with the classical morphological filter operator, the single-scale Retinex algorithm and bi-histogram equalization (BHE) algorithm, the contrast ratio and the signal-to-noise ratio of the enhanced image are improved.
Research on Application of Feature Analysis Method in DNS Tunnel Detection
Authors: Liu Yong , Gou Xiangnan 
Published: July 2020

Abstract
Traditional DNS tunnel detection methods based on load analysis and traffic monitoring have a high false positive rate and cannot effectively respond to new DNS tunnel attacks. To this end, a log-based statistical method is proposed to detect DNS tunnel attacks. Compare and analyse the differences between DNS tunnel attack behaviours and normal DNS parsing behaviour from the perspective of DNS sessions, extract the multi-dimensional features dominated by cache hit ratios, compose DNS session evaluation vectors, and use random forest classification algorithms to construct DNS session evaluation vector detection classifiers A DNS tunnel attack detection model based on characteristic statistical behaviours is established. The actual test results show that this method has a small false positive rate and low false negative rate, and it also has a high detection ability for unknown DNS tunnel attacks.
Design and Implementation of Rugby Tackle Technology Assisted Training System Based on Somatosensory Capture
Authors: Zhang Zhiqiang , Gao Li , Xiang Yangyang 
Published: July 2020

Abstract
Rugby is a highly challenging and spectator contact team sport with intense physical confrontation, specific technique, and group tactics, which requires athletes have tenacious will, courage and teamwork. “Tackle” is an essential feature of rugby which is the smallest action unit of body contact and confrontation between the offensive and defensive players during match. Tackling ability is not only a demonstration of personal skills, courage, and experience, but also the base of tacitly cooperating with other teammates in helpful defense which a well-trained part of team needed. For rugby beginners’ tackle training, this study designs and implements rugby tackle technology assisted training system based on somatosensory capture to help beginners master rugby tackle correctly and effectively during single or double training so that beginners could complete the confrontation exercises from simple to difficult.
Evaluating Top-N Join Queries with Real-time Entity Resolution
Authors: Zhu Liang , Cheng Ye , Wang Yu , Ma Qin , Meng Weiyi 
Published: July 2020

Abstract
Using nonmonotone ranking functions in top-N queries is a challenge. Traditional techniques for top-N queries are based on clean data without entity resolution (ER). For dirty datasets with duplicate tuples referring to the same real-world entity, these techniques may yield top-N tuples duplicates for a query. Consequently, the effective size of the result set of the query is less than N, and some useful tuples may fail to be retrieved from the datasets, which leads to poor effectiveness. Using an ER-Index based on a divide-and-conquer mechanism and nonmonotone ranking functions, in this paper, we propose a method for processing top-N join queries with real-time ER. This method integrates ER with the processing of a top-N join query over dirty datasets on the fly. Extensive experiments are conducted to measure the effectiveness and efficiency of the method over dirty datasets.
Identification of Comprehensive Energy Consumption Feature Based on Rough Set Bayesian Classification Algorithm
Authors: Liu Jian , Ouyang Cengkai , Zhao Shuangshuang , Tian Zhengqi , Wang Lihui 
Published: July 2020

Abstract
Mining of valuable information from user energy consumption data efficiently and accurately has always been a research hotspot in the power industry. Bayesian classification method is one of the important data processing methods in the field of machine learning and data mining research. It has the advantages of simplicity, high efficiency and stable classification effect, and it provides an effective solution to the user’s comprehensive energy consumption feature identification. A model describing energy consumption data to predict trends is built, training data set is analyzed, a classification model is constructed, and the data records in the database to a given category are maped, which can be applied to data prediction. By studying incomplete information systems, expanding rough sets, constructing extended models, Bayesian classification algorithms based on rough sets theory is designed, and user comprehensive energy consumption feature identification is realized. Experiments show that the user comprehensive energy consumption feature identification algorithm based on rough set Bayesian classification can greatly improve the classification accuracy.
The Improved Equilibrium Optimization Algorithm with Best Candidates
Authors: Zhao Juan , Gao Zhengming 
Published: July 2020

Abstract
The best candidates play the important role during the exploration and exploitation of individuals in almost all of the swarm-based algorithms. More best candidates were involved in such procedure of the grey wolf optimization algorithm and the newly raised equilibrium optimization (EO) algorithm we called here. The EO algorithm introduced four best candidates besides their average and constructs an equilibrium pool. However, the best candidates would still perform the guiding role and a novel improvement was introduced. Experiments on some classical benchmark functions were carried out and results show the better performance than the original one. Consequently, the EO algorithm should be improved and focused more on the best candidates furthermore.
Research on Technology of Sea Emergency Rescue Based on Unmanned Surface Vehicle
Authors: Hu Xinming , Fu Huaichun , Wu Xiang 
Published: July 2020

Abstract
Based on the advantages of small size, easy to carry by large ship, high speed, flexible mobility, no need to drive by human, high autonomy, the Unmanned Surface Vehicle (USV) is very suitable for the sea emergency rescue. During rescue, the USV needs to track the castaway, and it needs to sail to the downstream position of the castaway in the shortest time to release the life-saving equipment. In this paper, the technology of sea emergency rescue of USV is researched, the motion of the castaway is established, and the downstream position and velocity of the castaway is predicted based on Kalman Filter (KF). In addition, the motion model of the USV is established, and the target tracking based on the Dynamic Window Approach (DWA) is carried out, which will guide the USV to reach the rescue position, and local obstacle avoidance is carried out near the castaway to avoid collision. The rescue strategy does not need to consider the influence of the weather on the position of rescue, so it has high adaptability. Finally, the rescue strategy is simulated, the simulation results validate it works effectively.
Design of Networked and Digital Teaching Platform based on Big Data
Authors: Xie Hui , Wei Li 
Published: July 2020

Abstract
In the traditional network-learning, we have produced the massive data, but they do not play a corresponding role in the actual teaching, therefore, large data technology is used for improving the existing teaching platform, so that it can dig the hidden knowledge in teaching, this issue has become a hot topic that many educators continue to explore. We deeply study the big data technology, take MapReduce as the data analysis model, introduce clustering technology and association rule as the main models of data analysis, which are used for building the teaching platform based on big data. In the process of designing the large data teaching platform, we have built the digital teaching platform and the networked teaching platform respectively, the emphasis of the two platforms is different, among them, the networked teaching platform is used to analyze the students’ academic performance, that is, we take a certain student as the research object, analyze the students’ different academic achievements, adopt association rules technology, analyze the relationship of different disciplines, to develop a set of knowledge points to improve the performance of students’ weak disciplines, and generate a networked large data teaching platform; digital teaching platform can be used for the recommendation of students’ suitable curriculum combination, clustering technology will be introduced, analyze the effective combination of disciplines, which can help students improve academic performance, at the same time, students are willing to spend more time on learning, and take the subject combination module as a main function to generate digital large data teaching platform. By the design of digital and network large data teaching platform, they help educators make personalized learning plans for students, therefor, it help more students to achieve their learning goals.