bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficiency evaluation of intelligent swarm based on AHP entropy weight method
Authors: Yue-ming Han , Dan Fang , Hong-yan Zhang , Yuan Li 
Published: December 2020

Abstract
With the rapid development of high-tech, UAV intelligent swarm is expected to become an important means to deal with the complex situation environment in the future due to its intelligent and high cost-effective characteristics. UAV intelligent swarm is a nonlinear complex system, and the traditional single effectiveness evaluation method can not effectively reflect the system capability. Therefore, this paper proposes an evaluation method based on AHP entropy weight method for UAV intelligent swarm system effectiveness evaluation. The final weight is obtained by combining the two weights, which can reduce the impact of subjective factors. Finally, the feasibility of the proposed method is verified by simulation experiments.
Online Test System Load Balancing Method Based on Learning Analytics and Prediction Model
Authors: Wen-xue Si , Bin Zhuge , Jia-qi Cai , Li-gang Dong , Xian Jiang 
Published: December 2020

Abstract
Aiming at the problem of load imbalance such as database instability caused by high concurrency in the online test system, an online test system load balancing method based on learning analysis and prediction model is proposed. The method is divided into two steps. The first step is to build a curve of the number of questions answered according to the user test data. Simulate the test load change at different time intervals, reduce the number of concurrent connections by reasonably arranging the test; the second step is to analyze the user’s usual learning data and extract multiple features and then construct a time prediction model through the convolutional neural network. When the user accesses the test system, we can test the time required for him to complete the exam based on the predictive model. Each type of user is evenly redirected to each server by length of time. The experimental results show that the number of concurrent connections in the database generated by this method is lower, and the load of each server can be effectively balanced. In addition, the method can predict the load situation in advance so that can provide a reference for purchasing server resources before the test.
Query Optimization Algorithm of Replication Join Based on Sampling Partition
Authors: Lu Xin , Yang Junchao , Yuan Jiao , Wang Xun , Fu Kun , Yang Ke 
Published: December 2020

Abstract
Aiming at the low efficiency of join query in MapReduce traditional partition join algorithm when data skew, a replication join optimization algorithm based on sampling partition is proposed. According to the sampled statistics of connection attribute data, the algorithm divides the datasets in connection relationship into skewed data subset and non skewed data subset. In order to optimize the query performance, join query processing is carried out on them respectively. For the join queries of non skewed data subsets, the improved consistency hash function is used to partition these subsets, so that the load of data connection query processing of each node is balanced. For the skewed data subset join query, the smaller skewed data subsets are distributed to each node, and the larger skewed data subsets are partitioned according to the non skewed fields. In the Reduce stage, these skewed data subsets are join queried. Experiments show that the algorithm can optimize the join query performance under different data skew rates, and achieve efficient join query processing of large datasets.
Research on the Construction of Artificial Intelligence Platform Based on Cloud Edge Collaboration
Authors: Wei Liu 
Published: December 2020

Abstract
In order to solve the problem of poor operation effect of traditional artificial intelligence platform, a cloud-based collaborative artificial intelligence platform construction and design method is proposed. The platform operation efficiency is improved by optimizing the cloud edge cooperative integration architecture and improving the platform configuration function. At the same time, the cloud-side collaborative data edge algorithm is optimized. Through denoising, clustering and iterative processing of data information, the interference noise in the data processing process is eliminated, and the problem of poor data effect is solved, thus ensuring the stable operation of the cloud-side collaborative artificial intelligence platform. Finally, the experiment proves that compared with the traditional intelligent platform, the operation effect and efficiency of the cloud-based collaborative artificial intelligence platform are obviously improved, which fully meets the research requirements.
Numerical Simulation of Tidal Current in Xiushan Bridge
Authors: Cao Jingyang , Zhang Jusheng 
Published: December 2020

Abstract
Delft3D software is used to simulate the flow field of Xiushan bridge. The model is verified by the tidal level data of two observation stations, and the results are in good agreement with the measured values. Five piers on the south side of Xiushan bridge and six observation vertical lines are selected to simulate the velocity distribution data of these 30 vertical lines. The main axis of rising and falling tide is basically in the west-east direction. The maximum velocity of rising tide and falling current both appear on the surface. The maximum velocity of rising tide is 1.99m/s, while the maximum velocity of falling current reaches 3.03m/s.
Digital Holographic Watermarking Algorithm Based on DWT-DCT
Authors: Liu Zhiyue , Wang Anling , Xin Kuang , Liu Fuping , Zhu Xiaofeng , Li Yajing , Yu Zichao 
Published: December 2020

Abstract
In order to improve the robustness and invisibility of watermarking, a digital holographic watermarking algorithm based on DWT-DCT is proposed. First, the watermark image is generated into a digital hologram to improve the security and non-tearability of the watermark, and the I1 image is selected as the watermark image to be embedded. Perform discrete wavelet transform on the original carrier image to extract its low-frequency coefficients; secondly, the low-frequency coefficients are subjected to DCT transformation, and finally the watermark information is embedded in the high-frequency coefficient matrix after DCT, thereby completing the watermark information embedding. This algorithm combines the transformation characteristics of DWT and DCT, makes up for each other’s shortcomings, and makes digital watermarking more robust and invisible. In order to verify the anti-attack ability of this algorithm, different attack tests were performed on the watermark image. The results show that this algorithm is robust against simple linear attacks and noise attacks.
A Mispronunciation Detection Method of Confusing Vowel Pair for Chinese Students
Authors: Huang Guimin , Chen Qiupu , Zhu Hongtao 
Published: December 2020

Abstract
This paper presents a discriminative features based mispronunciation detection method for confusing vowel pair /i/ vs /I/ that are frequently mispronounced by Chinese learners of English. Firstly, the mean of the 39-dimensional Mel Frequency Cepstral Coefficients (MFCC) feature vector over all the frames of the current phoneme segment is employed as features to characterize the phoneme. Secondly, many specific acoustic features that can effectively capture the crucial properties of the long and short vowels are extracted. Finally, the Support Vector Machine (SVM) classifier is used for discrimination between confusing vowels /i/ and /I/ by using the discriminative features extracted from each phoneme. The experimental results show that, the proposed method can produce higher accuracy than the traditional Automatic Speech Recognition (ASR) based methods. In addition, the combination of spectral features with specific acoustic features can achieve better performance than using individual features.
Data Mining Model Based on Improved Ant Colony Algorithm
Authors: Wu Haoning 
Published: December 2020

Abstract
Data mining technology is an important means and way of data analysis in the Internet era, and it is a process of extracting potentially useful information from large databases. From the perspective of theory and technology, data mining is a process of discovering the relationship between data and models or between data from a huge database. From the application level, data mining can provide the government and enterprises with valuable and different levels of knowledge, and provide strong technical support for social development. The existing data methods mainly include classification method, association analysis method, cluster analysis method and anomaly detection method, but these methods have some shortcomings. Aiming at the problems of big randomness, sensitivity to parameters and slow convergence speed of existing ant colony clustering algorithm, this paper proposes a data mining model based on improved ant colony algorithm, which will be applied to cluster analysis of experimental data sets. The experimental results show that the improved algorithm has higher accuracy and faster convergence speed, and can effectively realize data information mining.
Electric Vehicle Charging Scheduling Strategy based on Genetic Algorithm
Authors: Hou Shangwu , Jiang Chengpeng , Yang Yi , Xiao Wendong 
Published: December 2020

Abstract
When multiple electric vehicles need to be charged, it will take more time and money for the electric vehicles to randomly enter the charging stations during the disorderly scheduling process. In the meantime, the utilization rate of charging piles is different, and the load of power grid is heavier. In this paper, a charging scheduling strategy is designed considering of the requests of multiple electric vehicles, which schedule in a way of overall parallel. In this charging scheduling strategy, electric vehicles will cost less time and money, the utilization rate of charging piles is more equal, and the power grid has minimum load. According to the charging scheduling strategy, a vehicle charging scheduling model is established based on multi-objective optimization. Technique for order preference by similarity to ideal solution is used to eliminate the dimensions of multiple objectives, and the genetic algorithm is used to solve the model. The simulation results show that the charging scheduling strategy can select appropriate charging stations for electric vehicles and achieve the goal of multi-objective optimization.
Information hiding algorithm based on edge detection and holographic watermarking
Authors: Xin Kuang , Wang Anling , Liu Zhiyue , Liu Fuping , Zhu Xiaofeng , Li Yajing , Yu Zichao 
Published: December 2020

Abstract
In order to improve the robustness of the image watermarking and reduce the influence of the overall image quality of the carrier when watermark is embedded on the carrier, an edge detection method combined with holographic watermarking was proposed to realize the hiding of watermark information. The carrier image is processed by edge detection, the watermark embedding area is segmented in the carrier image, and the suitable embedding area is filtered according to the size of the watermark; the original watermark is processed by a four-step phase shift algorithm to generate a holographic watermark, and the holographic watermark is embedded in the selection The embedding area is completed to embed the watermark, and the carrier image which the watermark is embedded in is carrier image with watermark. The watermark extraction method is to perform the same edge detection segmentation and region screening on the watermark carrier image, find the embedded area of the watermark, extract the holographic watermark, perform holographic inverse transformation to obtain the original watermark image, and complete the watermark recovery. The algorithm which is based on edge detection and holographic watermarking reduces the influence of embedded watermark on the carrier image, increases the flexibility and concealment of embedded position, and improves the robustness of the image when it is under geometric attacks.
A correction method of word spelling mistake for English text
Authors: Huang Guimin , Chen Jingru , Sun Zhenglin 
Published: December 2020

Abstract
Spell correction is already a mature field, the need to combine advantages of different methods for better performance arises in non-word problem. A combined spell correction method is proposed in this paper, which contains Levenshtein distance for comparation between misspelled words and correct spelled words in dictionary, improved Double Metaphone algorithm that includes vowel phoneme rule sets aimed at Chinese English learners, and global vectors(GloVe) for character representation that can generate vectors in order to obtain better suggestion lists for misspelled words. Result shows that the combined approach proposed in this paper is better than phonetic correction or edit distance method only, and a comparison with two generally implemented spell check tools is done for experiment which shows that this approach is better than them in correcting misspelled words, and the success rates of suggestion lists for spelling mistakes hit the spot.
Optimization of flight-to-gate assignment based on the passenger transfer demand
Authors: Tian Le , Li Feng , Chen Xiaojing , Wang Hui 
Published: December 2020

Abstract
Flight-to-gate assignment problem is one of the focused issues in the field of air transportation management and optimization. This paper focuses on improving the used airport gate utilization and minimizing passenger transfer tension via making a reasonable flight-to-gate strategy. A two-stage mathematical model has been formulated. In the first stage, we are aiming to minimize the transfer tension of passengers. Based on the flight-to-gate assignment result from the first stage, the second stage aims to increase the utilization rate of airport gates. Finally, a flight-to-gate assignment plan is designed to certify the validity of our model.
Research on the quality Data Service Platform architecture of Intelligent electricity Meters
Authors: Huaiying Shang , Yan Liu , Qi Zhang , Angang Zheng , Yongjin Xu 
Published: December 2020

Abstract
This paper introduces the basic situation of the quality data of intelligent electricity meters, and designs the overall architecture of the quality data service platform of intelligent electricity meters in view of the informationization demand of the quality data of the whole chain of intelligent electricity meters. The technical framework of the platform is further studied, and combined with the technical framework, the equipment quality data trusted acquisition system, digital factory service system, measurement equipment quality data service system, operation management system and quality data edge acquisition terminal are respectively introduced. Finally, the further development of the platform and its application in practical business are prospected.
Chinese text classification method using FastText and term frequency-inverse document frequency optimization
Authors: Zhou Tiantian , Wang Yintong , Zheng Xin 
Published: December 2020

Abstract
With the development of information technology, obtaining information quickly and accurately has become an indispensable part of people’s lives. Text classification can filter and organize massive text data to find valuable information, and its practical application is of great significance. This paper proposes Chinese text classification method using FastText and term frequency-inverse document frequency optimization(I-FastText). This method introduces term frequency-inverse document frequency(TF-IDF) optimization into the input layer of FastText model, removes words with high frequency and low discrimination ability, and achieves high-quality word vectors to train the text classification model. The experimental results on the THUCNews dataset shown that the Chinese text classification accuracy of I-FastText is significantly better than TF-IDF and FastText methods.
Application of deep learning in image recognition
Authors: Li Lingyun 
Published: December 2020

Abstract
Deep learning is a new field in machine learning, which is based on the neural network model to extract features by simulating human brain cognition. Image recognition is an essential subject in the field of deep learning and has made significant progress in recent years. In the training process, only the computer needs to cooperate, and no personnel need to participate in the process to get a good image recognition effect. This paper makes an in-depth analysis on the application of deep learning in image recognition, discusses the necessary steps of image trial and the basic principle of the neural network, and puts forward some problems and popular deep learning models used in image recognition.
Research on the Application of Architectural Pattern Elements in Southern Fujian in the Age of Artificial Intelligence
Authors: Zhou Xiaoping , Lian Xiaobo , Huiting Guo 
Published: December 2020

Abstract
The decoration of ancient buildings comes from the artistic treatment and artistic expression of architectural components, which is attached to architectural entities and reflects national aesthetic concepts and national cultural traditions. The diversified and integrated culture in southern Fujian also makes the architecture in this area have its own expression. The traditional architecture here is exaggerated, colorful and decorated with many patterns. Therefore, such a beautiful traditional building should be passed down by design techniques. Visual elements, such as patterns, bricks, doors and windows, ornaments and carvings, all constitute the classic symbol styles of characteristic cultural buildings in southern Fujian. This paper tries to analyze the application of architectural pattern elements in southern Fujian from the perspective of artificial intelligence era, aiming at providing reference for further improving artistic creation and aesthetic consciousness.
Image Salient Object Detection Based on K-means and Level Set Superpixel Segmentation
Authors: Huang Jindiao , Jin Xing , Zhang Jingjing 
Published: December 2020

Abstract
In order to detect and segment the salient objects in digital images to solve complex machine visual problems, this paper proposes an image salient object detection algorithm based on K-means and level set superpixel segmentation. The algorithm segments a given target image into multiple superpixel regions with similar features to abstract unnecessary details in the images, and reduce the number of colors in all superpixels by Histogram acceleration to increase computational efficiency. The saliency map obtained by calculating the distance of all superpixel in the Lab color space, and optimizes the detection effect by background prior and multi-scale spatial fusion. A large number of experiments show that the saliency detection method proposed in this paper is superior to algorithms such as SR, AC, FT, MSS, LC, CA, SF, HC and RC in accuracy, recall rate, AUC value and average absolute error.
Reliability analysis of software-defined satellite network based on control delay
Authors: Guo Zizhen , Liang Jun , Xiao Nan , Chen Jintao , Xie Baohua 
Published: December 2020

Abstract
At present, there are few studies about the link failure and reliability of software defined satellite network (SDSN). However, the inter-satellite links (ISLs) may break down because of the long distance between the satellites, the high relative moving speed and the easy interference of the wireless channel. At the same time, most researches on controller deployment in SDSN only consider the transmission delay but ignore the processing delay. To solve these problems, considering the failure probability of intersatellite link, the failure probability model of ISLs is designed, the queuing delay and processing delay are added into the delay model, and the software defined satellite network reliability analysis model based on control delay is established. This model can describe the reliability of the network against link failure. The simulation results show that the model is consistent with the actual situation and can provide some reference value for controller deployment in SDSN.
A metric learning network based on attention mechanism for Power grid defect identification
Authors: Yu Xiaodie , Ju Xiaoming , Wang Yong , Qi Haojin 
Published: December 2020

Abstract
Aiming at the current situation that deep learning has a strong dependence on sufficient samples and it is difficult to obtain sufficient labeled samples in practical applications, a few-shot image classification method combining attention mechanism is proposed. Firstly, the feature extraction module adds an attention mechanism to increase the weight of important parts, pay attention to the more important parts of the image, and obtain the corresponding feature vector. Secondly, the relevance measurement module is used to calculate the similarity between features to determine the category of the query image. Experiments have proved that our proposed model improves the accuracy of about 1.2% compared with the existing model. At the same time, the model is applied to the classification of power grid defects.
Hyperspectral Image Unmixing Method Based on Multiple Kernel Graph Non-negative Matrix Factorization
Authors: Yao Junyi , Wang Hongqiao , Fu Guangyuan , Wang Ling 
Published: December 2020

Abstract
In order to improve the accuracy of non-negative matrix factorization (NMF) in hyperspectral images unmixing, this paper proposes a hyperspectral image unmixing method based on multiple kernel graph NMF. Using multiple kernel learning (MKL), the hyperspectral data is mapped to the high-dimensional feature space, so that the linearly inseparable hyperspectral data in the low-dimensional space is linearly separable. Introducing the graph regular terms that characterizes the spatial structure relationship between pixels, the internal manifold structure of the hyperspectral image is effectively expressed. The results of both simulated dataset and real dataset show that compared with other method, the proposed method can improve the unmixing accuracy.
Face Recognition Based on Deep Features
Authors: Yi Li , Zhifang Sun 
Published: December 2020

Abstract
A face recognition method based on deep feature decision fusion is proposed. First, a convolutional neural network (CNN) is designed for deep feature learning on face images. Then, all the feature are mapped to construct a single depth feature vector. In the classification stage, the sparse representation-based classification is used to characterize the constructed depth feature vector, and the face category of the object to be recognized is determined according to the overall reconstruction error. Experiments are carried out on ORL and Yale-B datasets and compared with several existing face recognition methods. The results verify the effectiveness and robustness of the proposed method.
Text Classification on Large Scale Chinese News Corpus using Character-level Convolutional Neural Network
Authors: Wu Xingsu , Chen Jinhui 
Published: December 2020

Abstract
In view of the current problems in the field of Chinese news, such as chaotic news data and lack of automatic and efficient management, most of the existing Chinese text classification methods take word characteristics as the basic unit of text representation, but ignore the beneficial performance of character characteristics. In this paper, an automatic classification method of Chinese news based on character-level convolutional neural networks(char-CNN) is proposed. In order to compare with traditional models and deep learning models, we constructed a large scale Chinese news corpus to show that character-level convolutional neural networks could achieve the most advanced or competitive results.