bluuebunny/crossref_metadata_2025 huggingface dataset:
Capabilities of the Maple computer algebra system in the study of set theory and combinatorics
Authors: Olenev A A , Kirichek K A , Potekhina E V , Pelikh O V
Published: December 2020

The basic branch of both discrete and continuous mathematics is Set theory, the study of which is a difficult task. Designed and developed in the Maple computer algebra system, the Maplets package–the set theory application–enables to provide assistance both in teaching (by demonstrating the performance of operations on sets, testing knowledge and skills in using operations), and in studying set theory and solving the simplest combinatorial tasks (can be used as a simulator for students). By using interactive worksheets and animated images in Maple, students are given the opportunity for numerous experiments that will contribute to the development of their math education as well as computational skills. The application will form a sustainable need for the use of computer systems by students in the study of various branches of mathematics.
Research on Dynamic Identity Authentication Mechanism Based on Digital Signature
Authors: Zhan Weishu , Ye XinXin
Published: December 2020

With the economic development of globalization, the widespread Internet and the continuous establishment of national informatization, information security has become the focus of the national security guarantee system. Identity authentication is the first line of defense of the network communication system and the network security portal. Cryptography is the core content of the information security system and is widely used in various applications such as network security and data security. As the cornerstone of network security, since passwords are easy to be stolen or lost, it is not safe to authenticate identity with static passwords alone. Therefore, dynamic identity authentication came into being. It effectively resists the main security threats and attacks faced by static password authentication, solves the main drawbacks of static password authentication technology, and effectively prevents replay attacks. In addition, dynamic identity authentication solves the problem of static passwords being stolen in the database or stolen during transmission, thereby preventing illegal users from intruding and accessing the system. This article first studies the principle of digital signature technology and the design idea of dynamic identity authentication, and combines the advantages of both to propose a new dynamic identity authentication system based on digital signature technology.
Research on data access security agent technology in cloud computing security
Authors: Ou Xian
Published: December 2020

Cloud computing is a new application model, with multi-tenant, flexible, fast and easy to scale features, it can significantly reduce operating costs and improve operational efficiency. Cloud computing has received extensive attention from the business community and academia. With the continuous development of cloud computing technology, security and privacy issues have become the biggest obstacles to cloud computing applications. This paper first introduces the main problems faced by access control in the cloud environment, then researches and analyzes the cloud access security agent technology in terms of functional role and key technologies, and predicts the future challenges of the cloud access security agent technology based on the analysis of existing technologies.
Research on Cloud Storage Platform Technology Based on Hadoop
Authors: Hu Hairong , Fang Lulu , Yang Chaohong , Zhang Yang
Published: December 2020

In order to solve the problem of high cost of traditional data calculation and storage, and difficulty in writing parallel programs, this article summarizes the core technology of Hadoop, and uses Hadoop distributed processing technology and virtualization technology to design and build a cloud computing storage platform. The experiment proves that, compared with the traditional single-machine computing method and the physical machine cluster computing method, the platform has higher performance, and the resource utilization rate has also been improved.
Test Input Selection for Deep Neural Networks
Authors: Wang Zhiyu , Xu Sihan , Cai Xiangrui , Ji Hua
Published: December 2020

With the rapid development of deep learning technologies, the quality and security of deep learning systems have aroused great concern recently. Much research has been done in testing deep learning systems. Nevertheless, the oracle problem still remains since the input space of DNN-based software is usually very large and manually labeling is boring and cost-effective. Inspired by structural testing for traditional software, in this paper, we propose an algorithm to mitigate the oracle problem by selecting test inputs worth labeling. Specifically, we propose a test subset selection algorithm that can automatically select a test suite with high coverage but a small size when the labeling budget is limited. Compared with DeepXplore, the proposed algorithm can generate smaller test suites with higher coverage.
Optimal valid path prediction method for inter-domain networks considering commercial relationships*
Authors: Zhang Jingye , Zhang Jun , Xiong Xinli , Wang Yongjie
Published: December 2020

In the security research of large-scale inter-domain networks, it is often necessary to consider network state changes under the condition of failure or attack. In this case, the connection between ASes often switches between disconnection and recovery frequently, requiring frequent prediction of the AS-level path. Previous AS-level path prediction methods generally require detailed network information data and are complicated to calculate, which is difficult to meet the needs of such large-scale dynamic computation. In this paper, an optimal valid path prediction method for inter-domain networks considering commercial relations is proposed. This method predicts the optimal valid path according to the commercial relationships between ASes, which requires less network information data and low computational complexity. Through experiments, it is found that compared with the shortest path method commonly used in the security simulation of large-scale inter-domain networks, the prediction results are closer to the actual path of the network and can better reflect the actual path situation of the inter-domain network with lower computational complexity.
Clustering Preserving Projections for High-Dimensional Data
Authors: Cai Weiling , Zhou Honghan , Xu Le
Published: December 2020

In this paper, a novel clustering preserving projection method is presented to retain the cluster (or group) structures hidden in the original data. This method involves two steps: (1) the famous clustering method Fuzzy c-means is utilized to discover the cluster distribution in data; (2) such distribution is preserved by finding a linear embedding that minimizes the intra-cluster compactness in low-dimensional space. The feasibility and effectiveness of the proposed method are demonstrated on UCI dataset and USPS digital handwritten dataset.
Text classification based on graph convolutional network with attention
Authors: Tong Genmei , Zhu Min
Published: December 2020

The paper proposes a text classification method based on the graph convolutional network, the traditional text classification problem can be transformed into the node classification problem in the graph, and then proposes a attention structure to increase the weight of certain words and phrases. This paper verifies that the above structure has a positive effect on text classification accuracy. In particular, the structure can handle irregular data, such as citation network. We obtain very competitive results on 5 commonly used text classification datasets and achieve state-of-the-art results on 4 datasets, which include one dataset of citation network. Experiments show that combination of both structures can significantly reduce the classification error.
Digital Signal Modulation Classification using Data Conversion Method based on Convolutional Neural Network
Authors: Yang Yong , Chen Menghan , Wang Yizhu , Ma Piming
Published: December 2020

Automated modulation classification (AMC) plays a very important part in cognitive electronic warfare. Deep learning (DL) is a new machine learning (ML) method, which has been successfully implemented in many application fields. This paper proposes a new data conversion algorithm in order to gain a better classification accuracy of target signal modulation. Then a Convolutional Neural Network (CNN) architecture is developed, and its performance is proved to be better than the traditional data conversion method. In addition, the impacts of representation on classification performance are also analysed. Experimental results demonstrate the significant performance advantage of the proposed data conversion method using DL-based approach for modulation classification.
Achieving Manufacturing Excellence through the Integration of Process Planning Change and Data-driven Simulation
Authors: Wang Xiaogang , Liu Yiqun , He Yang , Li Nie , Mu Hua , Bai Yuewei
Published: December 2020

In this paper, a data-driven simulation framework with the integration of process planning change is proposed. It solves the problems of insufficient throughput and unbalanced resource utilization caused by the extremely unbalanced working time, improper resources allocation. Integration with Flexsim using a 3D graphical user interface is designed to meet the needs of complex data management. Experiments were tested, and the results show that the proposed approach combines the real-time rescheduling and quickly changing.
Towards quality analysis of MES through CMM data interoperation
Authors: Wei Shuangyu , Bai Yuewei , Xu Lai
Published: December 2020

The implementation of MBD/MBE (Model-based Design and Engineering) in the product design and manufacturing can effectively support multi-step data interoperation among “Design-Manufacture-Measurement.” Due to the limited data interoperation functions provided by the current CMM software (Coordinate Measurement Machine), most studies of MBD/MBE focused on designing the upstream. In contrast, the downstream (the underlying measurement data utilization) research is less. MES has an essential function in managing the manufacturing quality that analyses the condition and its development trend by collecting the manufacturing process quality data. Insufficient use of the underlying measurement data will lead to limited MES functions, especially for the capability of decision-making in intelligent manufacturing systems. The paper presents a measurement data interoperation method based on the interoperation layer method to support the quality analysis in MES (Manufacturing Execution System), discusses the relevant critical logic and data processing flow of the layer. It is verified that it can provide more comprehensive measurement data for quality management in the workshop.
Research on Space Launch Information Integration
Authors: Feng Wu , Ailiang Zhang , Xiuluo Liu , Jia Wang , Ying Liu , Jianjun Qi , Min Wang
Published: December 2020

The key directions for the automation, intelligence and integrated development of space launch have been very clear. Within the scope of C3I systems, professional automation system, ground handling system, research is conducted on the overall technical architecture of the space launch information integration, including: (1) Research on technical specifications system to promote the systematization and consistency for technical specifications at each level of access, transmission, processing and application; (2) Research on system functional architecture, highlighting the unified terminal access, unified transmission carrying, unified basic application and unified information interaction; (3) In view of the future development direction of space launch informatization, research will be carried out on relevant information guarantee technologies for remote command operation, automatic detection and diagnosis, wireless object connection and unattended launch tower.
House Price Prediction with An Improved Stack Approach
Authors: Zhang Hang , Wang Kehan , Li Mengchu , He Xinchen , Zhang Ruibo
Published: December 2020

As a typical framework in ensemble learning, Stacking creates a multi-layer learner by integrating the learning results of different individual learners. Despite a general improvement in accuracy, multiple layers in stacking results in high computation cost. In addition, some individual learners do not perform better than others in single-model prediction task. To solve the above drawbacks of traditional stacking, this paper proposes an improved Stacking algorithm based on the concept of “weighted attribute”. For each individual learner, a corresponding weight will be calculated based on its prediction performance on training data. Weights will then be used as evaluation criteria for model performance. Models with larger weights will be selected as attributes for meta-learner. When improved Stacking algorithm is applied on the same dataset, experiment results indicate a slight improvement in prediction accuracy and a relatively large reduction in computation cost. This demonstrates practical application value of the improvement.
Research on multi-feature fusion text classification model based on self-attention mechanism
Authors: Luo Xiaoxia , Wang XuanHao
Published: December 2020

For complex long texts, critical semantic information will be weakened and non-critical features will be forgotten. This paper proposes a multi-feature fusion text classification model based on autoattention mechanism. The preprocessed text data is represented by character-level vectors through the Bert model. First of all, self attention mechanism (Self-Attention) is used to learn the dependence of text words to capture the internal structural information of the text, Secondly, according to Deep superposition convolutional neural network (DSCNN) and Bi-directional Gated Recurrent Unit (BiGRU) based on soft attention mechanism (Soft-Attention), the semantic features of text data are extracted separately, and two different feature extraction results are combined. Finally, the Softmax layer is used to classify the deep-extracted features, and the accuracy of the classification model is improved by adding a uniform distribution item to the cross-entropy loss function. The text is verified in the medical consultation data set, and the results show that the F1 value of the text classification of the model is as high as 88.91%, Text semantic understanding is better than mainstream models.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Detection of regions with the least impact on true and fake image classification through reinforcement learning
Authors: Li Wei , Qiao Peng , Dou Yong
Published: December 2020

With the development of artificial intelligence [1-3], convolutional neural networks (CNNs) have made significant progress in image generation and manipulation. The generated images using facial image synthesis methods, e.g., Faceswap [4], Deepfakes [5], Face2face [6], NeuralTextures [7], are very realistic and even difficult to distinguish by human eyes. It poses serious challenges to social and personal security. There are many methods that can detect synthetic facial images with an accuracy rate of over 99%. But the synthetic facial image classification model can only give a true or fake result, and it is impossible to know which part is the discordant area. Therefore, we propose and experimentally verify a method that using reinforcement learning to select the region in the image that has the least impact on the classification model.
Research on Contextual Recommendation System of Agricultural Science and Technology Resource Based on User Portrait
Authors: Zhang Hui , Qin Xiaojing , Zheng Huaiguo
Published: December 2020

Under the new situation of rapid development of information technology and increasingly diversified demands of farmers, agricultural information service is also faced with many problems, such as the traditional information service system is difficult to quickly and accurately find the information required by users in the mass information, and the universal information service form cannot meet the growing personalized needs of users. In view of the above problems, this paper explores the application of user portrait technology in agriculture. Through in-depth analysis of the purpose of drawing portrait of agricultural users, characteristics of the agricultural industry and actual needs of agricultural information service, it builds a user portrait model in the agricultural field. At the same time, the situational recommendation system of agricultural science and technology resources based on user portraits is constructed by comprehensively considering the influence of multidimensional situational factors such as the time, geographical location, categories of agricultural production and changes in users’ interests on the demand for agricultural information resources. Through practical application, the system realizes the individualized recommendation of agricultural information resources based on situational awareness and user interest adaptation, and satisfies the individualized demand of agricultural users for agricultural information better.
Integrated software design of dispersive spectrometer imaging system
Authors: Wang Xiaoyan , Peng Bangping , Li Jie , Sun Li
Published: December 2020

Hyperspectral imaging technology is a new means of ground observation technology, which can simultaneously obtain the geometric image of the target and spectral information. It is the main instrument of high spectral remote sensing data acquisition. In order to facilitate the analysis of the motion imaging degradation mechanism of dispersive spectrometer and the high-precision real-time correction effect, we have developed a software system integrating motion imaging degradation of dispersive spectrometer, restoration correction and quality evaluation. Given the powerful image data processing function of Matlab and C# written form of the structure of the interface height flexibility and convenience, this system is compiled with the C# and Matlab mixed mode. Practical applications show that the software system has the characteristics of reasonable structure, complete functions, stable performance, and open system. It provides technical support for establishing a fast hyperspectral high-precision restoration correction processing platform and improving the accuracy of hyperspectral data, which will speed up the practical application of hyperspectral data.
Identifier and Locator Mapping Service Research
Authors: Liu Feng , Gong Cheng , Tu Rui , Tang Huatao
Published: December 2020

The original TCP/IP architecture has faced some critical challenges from scalability, mobility, security and so on. It also hampered the development of some new network technologies such as multi-homing, traffic engineering. One of the most important reasons is IP address semantic overloading. Academy commonly considered that we should give a clean-slate design for the naming and addressing architecture of the future Internet. “Locator/ID Split” is one of the most important research areas. The scalable flat-labels based mapping service is the kernel of “Locator/ID Split”. HLIM uses hash-based routing to provide a deterministic mapping resolution for the edge network through distributed mapping servers. HLIM satisfies the scalability of the flat-labels mapping service, and can adapt mapping nodes’ dynamic join and exit.
Development of automatic arrange system for disordered components in bulk
Authors: Honglei Ran , Kui Zhang , Yaru Cui , Shengbiao An , Jie Huang
Published: December 2020

Due to the increasingly thin, chip, miniaturized and high-precision electronic components, the samples in many links of the production and testing process of components are scattered and disordered. Meanwhile, the low efficiency of manual testing and the instability of detection accuracy make the intelligent material discharging detection of components extremely urgent. In this paper, intelligent requirements for detection are comprehensively analyzed, and technologies such as machine vision imaging optimization, deep learning feature matching, and tiny sample grasping and placement are deeply studied. An automatic discharging system integrating counting module, defect detection module and automatic discharging module is developed to realize intelligent discharging detection of tiny bulk disordered devices.
Target Locating and Grabbing Algorithm for Robot Prosthesis Based on PSO-RBF
Authors: Zhou Jianbo , Wang Chuanjiang , Sun Xiujuan , Wang Kunpeng , Feng Zhengkai
Published: December 2020

Aiming at the target locating and grabbing problem for a five-degree-of-freedom intelligent robot prosthesis worn by the disabled, an algorithm is proposed. Firstly, the laser rangefinder and attitude sensor installed at the head of the wearer are used to simulate the motion of the human eye to realize the position of the target in the prosthesis workspace. Secondly, the laser ranging information is obtained by the 3D attitude sensor installed at the wearer’s shoulder to obtain the position coordinates of the target relative to the prosthesis shoulder, so as to obtain the position coordinates of the target in the prosthesis workspace. Finally, the PSO-RBF neural network is used to train the samples to obtain the nonlinear mapping relationship between the position coordinates and the joint angle, thus predicting the joint angle and grabbing the target. The simulation and experimental results show that this method can locate the target accurately and calculate the angle of each joint of the prosthesis in real-time, so as to complete the action of target grabbing.
Inverse solution of slave hand for single-port laparoscopic surgery robot based on hybrid Particle Swarm Optimization
Authors: Han Shu , Wang Chuanjiang , Tian Zhaohui , Fan Binghui
Published: December 2020

Because of the particularity of the slave hand of the single hole laparoscopic surgery robot configuration, it cannot meet Pieper criterion, and its inverse kinematics solution cannot be obtained by traditional kinematics model. In order to solve the problems, the screw theory is used to establish the kinematics model for the slave hand of the single hole laparoscopic surgery robot. On this basis, the inverse kinematics is converted into a minimum solution problem. The optimal solution of each joint variable is obtained by using the hybrid particle swarm optimization (HPSO), and then the accuracy of pose matrix is analysed to verify the effectiveness of this method.
Two-stage Topology Control Strategy for Software-defined UAV Networks Against Jamming Attack
Authors: Li Zhiwei , Qiao Wenxin , Lu Yu , Zhao Donghao , Shi Runze
Published: December 2020

Software-defined network (SDN) technology brings new methods to the autonomous control of UAV networks. Aiming at the deterioration of UAV network wireless communication quality caused by jamming attacks, we propose a two-stage topology control algorithm to improve network service quality. The first stage is the redeployment of the control plane consisted by the relay UAVs. We calculate the position of the jammers based on Received Signal Strength Indication (RSSI), and then we first redeploy the position of the Relay UAVs. The second stage is the redeployment of the data plane consisted by the mission UAVs. Mission UAVs that lose control links due to jamming are redeployed according to the historical state of controllers, and other UAVs are redeployed according to the new state of controllers. The performance metric is the weighted average of load balancing, redeployment delay and Signal to Interference plus Noise Ratio (SINR). The experimental results show that compared with the Distance Closest Migration (DCM) strategy and the Distributed Hopping Algorithm (DHA), the comprehensive performance of the proposed algorithm is better.