bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and implementation of Wisdom classroom management system based on voice control
Authors: Gao Xia , Xiong HongZhou , Xu Fangqin
Published: November 2020

This paper implements a wisdom classroom management system. In response to the difficulties of traditional campus equipment management, this system can automatically collect the status of electrical appliances in the classroom and analyze the data to provide equipment status diagnosis and early warning functions. At the same time, the Raspberry Pi provides a visual 3D management interface for the terminal. In addition, the system also introduces the voice recognition function in deep learning to control the equipment in the classroom more conveniently.
Study on the Teaching Mode Based on OBE-BOPPPS – Taking Computer Application Fundamentals as an Example
Authors: Wang Bei
Published: November 2020

The implementation of flipped classroom can help students achieve the goal of deep learning, and the BOPPPS model which is rising in recent years provides a basic framework for the implementation of flipped classroom. Through the analysis of the concept of flipped classroom and the feasibility of constructing the flipped classroom teaching mode of computer application foundation, this paper summarizes the methods of constructing the flipped classroom teaching of Computer Application Fundamentals, hoping to provide reference for the flipped classroom teaching in higher vocational colleges. Teaching practice shows that the OBE-BOPPPS teaching mode is helpful to solve the problems in the teaching process, improve the teaching quality of university teachers, improve the learning effect of students, and promote the teaching reform of basic computer courses.
Review on the principle, Progress and Application of Block chain Technology
Authors: Xu Yipeng , Guan Kexin , Lei Lei
Published: November 2020

In recent years, electronic currencies such as bitcoin have been favored by people. As the core technology of Bitcoin, blockchain has attracted the attention of more and more experts and scholars at home and abroad. The changes of its classification mode, service mode and application requirements lead to the diversified development of core technology forms. Blockchain is a distributed ledger technology, which evolves into a complete storage system based on logic control functions such as intelligent contracts. Block chain technology is based on decentralized point-to-point transmission network, using cryptography principles and consensus mechanism to ensure the coherence of each node participating in the distributed system, and to ensure the accuracy, consistency, non-tampering or deletion of the recorded information. All these ensure the establishment of a sharing system with high privacy, high security and high efficiency. In this paper, the basic principles and characteristics of blockchain technology are analyzed, and the research status and application of this technology in the financial system are summarized in detail.
Research on Security Protection Technology Based on Terminal Information Jump
Authors: Wang Yi , Gao Yang , Luo Bing-Bing
Published: November 2020

This paper proposes a security protection model based on end-to-end information hopping to meet the actual needs of enterprise intranet protection. The problem has enhanced the proactive protection capability of the intranet.
A data security exchange method based on automatic arrangement
Authors: Qi Zhenfei , Zhang Fuhua , Hang Feilu , Xie Linjiang , He Yue
Published: November 2020

With the increasingly fierce attack and defense of network security, the security system needs to adopt the coordination of multiple functional modules to improve the security of the system. In practical application, web service protocol transmission and restful specification transmission can be used to ensure the security of data exchange. However, when the two protocols are used together (two security modules), it will cause great problems and workload to the security operation and maintenance. This paper proposes that the security function based on choreography which can distinguish the security operation sequence or script corresponding to the next step according to the protocol of the data package, and arranges the web service protocol and restful protocol together through script and engine to automatically coordinate work, so as to reduce the pressure of security operation and maintenance.
A Small amount of Labeled Data Chinese Online Course Review Target Extraction via ALBERT-IDCNN-CRF Model
Authors: Min Liang , Miao Xianglin , Bi Peng , He Feijuan
Published: November 2020

Aspect sentiment analysis of online course reviews is of great significance in helping users choose courses and improve course quality. Review target extraction is particularly important as the basis of aspect sentiment analysis. Because the current models mostly rely on a large amount of annotation data, there is fewer relevant research on the extraction of online course review targets with higher annotation costs. This paper proposes an ALBERT-IDCNN-CRF review target extraction model for a small amount of labeled data. First, using ALBERT pre-trained sentences obtained dynamic model Chinese word vector coding; Simultaneously, using ALBERT pre-trained model of Transformer obtain sentence abstract features. Then, abstract features are input into the dilated convolutional neural network (IDCNN) to reduce the number of neuron layers and parameters. Finally, conditional random field (CRF) is used to decode and annotate the review sentences to extract the appropriate review objectives. The experimental results on the school online real Chinese online course review data set show that our model has achieved better results than existing models.
HWOBC-A Handwriting Oracle Bone Character Recognition Database
Authors: Li Bang , Dai Qianwen , Gao Feng , Zhu Weiye , Li Qiang , Liu Yongge
Published: November 2020

The oracle bone character (OBC) from ancient China is the most famous ancient writing systems around the world. Identifying and deciphering OBCs is one of the most important topics in oracle bone study. In research, one of the challenges is that the literature review usually leads to a huge cost of time and manpower. Therefore, the digitazation of OBC literature through the automatic recognition is the inevitable trend of future development. However, the OBCs in the literature are usually writing characters while the database of handwriting OBC has not yet been presented. In this paper, we establish a handwriting oracle bone character database called HWOBC, containing 83,245 character-level samples which are grouped into 3881-character categories. We also present the performance of several baseline DCNN-based methods, in which Melnyk-Net exhibits the best accuracy of 97.64%. It is anticipated that the publication of this database will facilitate the development of OBC research.
CDS: Contextual Spoken Language Understanding Model Based on Dual Semantics
Authors: Ma Kun , Hu Minghao , Li Fangzhao , Luo Li
Published: November 2020

Spoken language understanding (SLU) in multi-turn task-oriented dialogue systems usually needs to take historical contextual information into account. To achieve this, current approaches encode dialogue utterances with contextual information to improve the accuracy of slot filling and intent detection. However, this kind of approach ignores the different influences of contextual information on dialogue utterances. Some utterances with independent context will be misunderstood due to the addition of noisy contextual information. In this paper, we propose a Dual Semantics model(CSD) that is based on dual semantics. Each dialogue utterance is encoded based on independent semantics and contextual semantics, where the contributions of these two semantic information are dynamically decided. A large number of experiments on two datasets verify the effectiveness of our approach. Specifically, the accuracy of slot filling at the KVRET dataset increases by 3.3% compared to the baseline model. A dataset of psychological service appointments(PSA), is further collected for the verification of our approach. Compared with existing methods, the accuracy is improved by 4.9% on this dataset.
Research on Single Antenna Co-frequency Mixed Signal Separation Based on Improved EFICA Algorithm
Authors: Ren Hongli , Zheng Yufu , Zhong Na , Cao Xiaofang
Published: November 2020

Signal separation under the condition of single antenna reception is a process of estimating the source signal component by using one-dimensional observation signal vector. Under the condition of single antenna receiving the same-frequency mixed signal, in view of the problem that the stability of the EFICA algorithm will be affected by the selection of a random initial iterative matrix, it is proposed to use the steepest descent method to select a suitable iterative initial matrix to improve the EFICA algorithm. An experimental comparison between the EFICA algorithm and the EFICA improved algorithm is presented. The simulation results show that the improved EFICA algorithm can achieve a better separation effect for the separation of mixed signals at the same frequency.
Study on Condition Monitoring of Lathe Cutting State Under Various Cutting Conditions Based on Motor Current Signature Analysis
Authors: Huang Shiqing , Zou Zhexiang , Lin Yubin , Gu Fengshou
Published: November 2020

Lathe machining occupies a large proportion in modern production and processing, which affects the machining accuracy of the workpiece and results in vibration and noise of the machine tool. To improve the efficiency of lathe cutting state monitoring, this paper presents a new method on studying the monitoring of lathe tool, with Motor Current Signal Analysis (MCSA) as the main research method, the characteristics of stator’s working current are selected as the analysis object. Three cutting depths, 1mm, 2mm, and 3mm are respectively performed on the same workpiece material, and the cutting conditions are blunt tool and sharp tool. The frequency characteristic of the current is analyzed the under various cutting conditions mentioned above and the internal relation of the current variation and the cutting condition is presented as the conclusion of this paper, by which provides a new idea for the research of conditions monitoring and fault diagnosis in field operation on lathe tool.
A dynamic weapon target assignment based on receding horizon strategy by heuristic algorithm
Authors: Zhang Kai , Zhou Deyun , Yang Zhen , Li Xiaoyang , Zhao Yiyang , Kong Weiren
Published: November 2020

Weapon-target assignment problem is the crucial decision support in Command & Control system. As a typical operational scenario, asset defense has less research on the issue of asset-based dynamic weapon target assignment, and the major models of A-DWTA are challenging in practical application. In this paper, an A-DWTA model is first established by receding horizon decomposition strategy, which closely reflects the operational requirement. Then a heuristic algorithm based on statistical marginal return is proposed to solve the A-DWTA model. Experimental results show that the proposed algorithm for solving the A-DWTA model has advantages of real-time and robustness. The obtained decision plan can complete the operational mission in fewer stages and be adjusted adaptively by model and algorithm parameters.
Ordinal Logistic Regression Analysis on Influencing Factors of Space Tourism Expectation Model
Authors: Duan Wenjing
Published: November 2020

Aiming at the problem of data validity in big data and information mining technology, this paper uses the method of ordinal logistic regression analysis based on statistics to evaluate the statistical validity of data, and analyses a case of space tourism expectation mode including 2025 valid samples. By analysing and calculating the significance level of the whole equation and the significance level of the equation goodness of fit in the case, the validity of the statistical data is evaluated, and the data regression equation is formed to support the statistical based artificial intelligence learning method.
Improving the Cold Start Problem in Music Recommender Systems
Authors: Cao Ke Yin , Liu Yu , Zhang Hua Xin
Published: November 2020

The music recommender system can help us extract valuable music from a huge amount of raw information, due to the increasingly severe data sparsity and cold start problems in music recommendation system, the recommendation results will be inaccurate. But the traditional algorithm cannot effectively solve these problems; the existing improved algorithm still requires specific parameters in advance due to its poor stability. In this paper, a proposition for a cold start based on community detection algorithm is proposed. By projecting the bipartite network, calculating the similarity between the User and the Item, the Louvain algorithm is used to perform community detection on the projected One-mode network, so that the new record can be updated to the original community, and then the user group is recommended for music. With higher accuracy, solid stability, and shorter running time (when compared with other ease cold start algorithms), the algorithm in the larger community can be safely applied to the music recommendation system or prediction field.
Data Privacy Protection Method of Internet of Things Based on Blockchain Technology
Authors: Wang Yajie , Huang Wei , Yu Jie , Chen Kai , Wang Fugui
Published: November 2020

In the process of the continuous reform of modern social economic system and the continuous development of science and technology, the distribution of the Internet of things reflects good application value. However, the Internet of things has many distributed nodes, data center, high computing cost and poor supervision. In the process of privacy protection, there are still some objective problems. Therefore, the use of blockchain technology in the privacy protection of the Internet of things privacy data block, structure decentralization, information hierarchy and other aspects, which can establish a more perfect Internet of things privacy protection model and more rich management mechanism, making the prevention of privacy leakage, data tampering and other aspects of privacy protection work has been further developed. Therefore, this paper takes the implementation mode of blockchain smart contract in smart home privacy protection as an application example to analyze the application value of blockchain in the privacy protection of Internet of things.
Defect detection in textile fabrics with optimal Gabor filter and BRDPSO algorithm
Authors: Zhang Jiawei , Li Yueyang , Luo Haichi
Published: November 2020

This paper presents an effective method that can detect fabric defects. The method utilizes the optimal Gabor filter and binary random drift particle swarm algorithm (BRDPSO) that can implement feature selection and parameter optimization synchronously. The parameters of 2D-Gabor filters are adjusted by quantum-behaved particle swarm optimization algorithm (QPSO) and the optimal Gabor filter is obtained. BRDPSO is used to select features on the original feature set and simultaneously optimize the parameters of the Isolation Forest (IF) classifier. Extensive experimental results indicate that the proposed method has effective detecting performance on the defect detection of textile fabric.
The Research of Lip Reading Based on STCNN and ConvLSTM
Authors: Zhu Yijie
Published: November 2020

Aiming at the problems in temporal model during the research of lip reading, a deep learning model is proposed based on spatiotemporal convolutional neural networks (STCNN) and Convolutional Long Short-Term Memory (ConvLSTM). Firstly, STCNN is used to learn the features of the extracted lip image, and then the learned features are sent to ConvLSTM to process the time series data, which is classified by softmax, and finally the CTC loss function is used to optimize the results. Using GRID data set for training, comparing with experiments, it is found that the recognition accuracy of this model achieves 95.0% at the word level. Experiments show that the model can improve the accuracy of lip reading.
Application of MATLAB in the teaching of probability theory and mathematical statistics
Authors: Zeng Lihua
Published: November 2020

“Probability theory and mathematical statistics” is an important public compulsory course of arithmetical foundation. MATLAB software is introduced into the teaching process to make some abstract concepts and theorems expressed in the form of graphics to achieve visualization effect. This method makes it easy for students to understand, improve their learning enthusiasm and achieve twice the result with half the effort.
Fabric defect detection method based on Cascade Deep Support Vector Data Description
Authors: Wang Xupeng , Li Yueyang , Luo Haichi
Published: November 2020

The Fabric defect detection method based on Cascade Deep Support Vector Data Description (SVDD) is proposed in this paper. The method describes the data by Deep SVDD to realize the correct evaluation between the normal fabric images and the images with defects in the high dimensional space. Combining with the cascading method, the proposed algorithm can be used to detect minor defects more accurately and quickly. We take the anomaly score as the evaluation result of the testing image, then the Median Absolute Deviation (MAD) outlier detection method is applied to get the final defect detection results. This newly developed method can be trained with only a small amount of defect-free samples, which greatly reduces manual intervention. A variety types of defects in the general fabric images can be detected efficiently. The experimental results demonstrate that the proposed model has good global performance and high recall rate.
The Cd Content Provided By Sources Affected the Bottom Waters in Various Ways
Authors: Yang Dongfang , Yang Danfeng , Zhu Hong , Ling Weifeng , Li Haixia
Published: November 2020

According to the investigation data of Jiaozhou Bay in 1992, this paper studies the level of Cd content and its horizontal distribution in the bottom waters from the center of the bay to the south of the bay mouth. The result indicates that the Cd content in the bottom waters of the bay ranged from 0.09 to 0.72μg/L in May, 0.08 to 1.08μg/L in August and 0.30 to 1.19μg/L in October, which means that waters of the bay were not polluted at all by Cd in May but some waters were mildly contaminated in both August and October. In a word, the bottom bay waters from the center of the bay to the south of the bay mouth were slightly polluted or not polluted at all by Cd in May, August and October. In May, surface runoff and rivers together transported Cd content of 1.07-1.53μg/L to Jiaozhou Bay waters. In the bottom waters from the southeast to the center of the bay, the Cd content decreased from 0.72μg/L to 0.09μg/L. The Cd content in the bottom waters of the southeast of the bay is relatively high for the sources of Cd could reach its surface waters there. However, much little Cd content is found in the bottom waters of the center of the bay due to the lack of Cd access to its surface waters. In August, rivers and main sea currents together transported Cd content of 0.96-1.11μg/L to Jiaozhou Bay waters. According to transporting mechanism of substance content in Jiaozhou Bay introduced by the author, high Cd content area of 1.08μg/L exists in the bottom waters of the center of the bay, while low-content area of 0.08μg/L was in the southwest of the bay where currents are about to leave the bay. In October, rivers transported Cd content of 1.04-1.10μg/L to Jiaozhou Bay waters. The Cd content decreased from 1.19μg/L to 0.30μg/L along the content gradient from the high-content area of the southeast of the bay to the waters of the south of the bay mouth. In the waters of the southeast of the bay, relatively high Cd content transported to the surface waters by sources leads to high level of Cd in its bottom waters. It’s the exact opposite in the waters of the south of the bay mouth. The Cd content provided by sources affects the law of the bottom waters in various ways, and the horizontal variation of Cd content reveals its changing process in the bottom waters.
A Review of Decision Tree Classification Algorithms for Continuous Variables
Authors: Jiao S R , Song J , Liu B
Published: November 2020

Improving the division accuracy and efficiency of continuous variables has always been an important direction of decision tree research. This article briefly introduces the development of decision tree, focuses on the two types of decision tree algorithms for non-traditional continuous variables — based on CART and based on statistical models. Finally, the future development trend of decision tree algorithms for continuous variables is discussed.
Port container number detection based on improved EAST algorithm
Authors: Feng Xing Qi , Liu Qing , Wang Zhi Wei
Published: November 2020

The methods of intelligent port container number detection tend to be diversified. However, traditional container number positioning methods and current deep learning algorithms are mostly multi-stages, which need to be optimized during difficultly training, resulting in bad model effect and time-consuming. In view of the above problems, the main contributions of this paper are drawing on the EAST text detection method to obtain an improved EAST algorithm that directly predicts the box number area. This new algorithm not only eliminates multiple intermediate stages, but also improves the regression method and loss function of the box number area, optimizes the boundary, balances positive and negative samples to adapt to the detection area of the box number based on the original algorithm. At the same time, it adopts the lightweight design idea, and uses ShuffleNet’s channel shuffling and depth separable convolution for reference to optimize the original model, which reduces the complexity of time and space, compresses the model and reduces the detection time. The final experimental results show that the accuracy of the algorithm reaches 97.5% while keeping the FPS index no less than 14.
Selecting Valuable Mask Topic Stocks through Ontology Reasoning
Authors: Chen Liming , Xiu Baoxin , Ding Zhaoyun , Zhu Xianqiang
Published: November 2020

Due to COVID-19, masks are in short supply. Accordingly, mask topic stocks have surged as well. However, faced with various mask topic stocks, plenty of individual investors can only blindly follow the trend, but lack of objective judgment. In light of this, an ontology-based stocks selection framework was proposed. Different from most prior methods, the proposed framework starts from fundamental analysis and combines qualitative knowledge and quantitative data. Concretely, qualitative knowledge refers to news, information of executives and industry chain partners, while qualitative data are the financial ratios from the financial statements of companies. Notably, supply chain information was also introduced to address the delay of statements disclosure. Moreover, with the risk preference coefficient, the proposed framework can adapt to investors with different risk preference. Lastly, the results of case study are basically consistent with the research results from four investment institutions, which proves the practicality and effectiveness of the proposed framework.
Research on Application of Collaborative Filtering Algorithm in Digital Movie Recommendation
Authors: Li Sansan , Zhou Dongxian
Published: November 2020

Information overload will undoubtedly increase the time for users to search for information on the Internet. Data mining technology based on big data is constantly improving this situation. It extracts hidden, previously unknown information from massive data information to find out the internal connection between users, between the user and the item, and between items, so as to recommend information according to the user’s preferences and needs, and even provide the user with a customized page. So how does it implement personalized recommendations? This article takes online digital movie recommendation as an example, and explains the principle and process of collaborative filtering recommendation algorithm to achieve personalized recommendation through the combination of theoretical analysis and experiment.
Analysis of Fault Diagnosis Methods of High-power Permanent Magnet Synchronous Motor
Authors: Mai Liu
Published: November 2020

High-power motor is widely used in actual production and life, and motor failure will seriously affect the efficiency and safety of the equipment, and normal production and life. Therefore, it is necessary to strengthen the fault judgment of the motor, quickly locate the fault and analyze through integrated means, and improve the subsequent maintenance methods. The article will take the permanent magnet synchronous motor of an electric vehicle as an example to discuss the analysis of the fault diagnosis method of the high-power motor.
Research on False Data Injection Attack Detection of S mart Grid Based on Machine Learning
Authors: Tao Zhen , Zhan Weishu
Published: November 2020

The safe and reliable operation of power systems is an important guarantee for the healthy development of the national economy. Industry and people’s lives are inseparable from electricity, so the safety and reliability of electricity supply is very important. The sudden interruption of power supply will not only bring serious economic losses, but also seriously affect people’s normal lives and even endanger social stability. False data injection attack (FDIAs) are a new type of power system network attack method. FDIAs are a new type of power system network attack method. It can successfully bypass the bad data detection mechanism, offset the power measurement data, and mislead the control center under extremely subtle conditions. Therefore, it poses a very serious threat to the stable operation of the power system. Therefore, this article first analyzes the principle of false data injection attacks, in order to provide a theoretical basis for subsequent attack detection. Then this paper constructs a detection method based on extreme learning from the perspective of optimizing learning efficiency. Based on the IEEE14-bus standard test system, the method is verified through simulation, which shows the feasibility of this method, which provides a direction for building a safe and stable smart grid.
Emotional Analysis of Cigarette Consumers Based on CNN and BiLSTM Deep Learning Model
Authors: Lin Jingju , Jian Zhongquan
Published: November 2020

Cigarette online reviews can truly reflect the word-of-mouth of cigarettes, and help cigarette industrial and commercial enterprises to understand consumers’ cigarette use experience and cigarette word-of-mouth dynamics. In order to extract effective consumer experience information from massive online reviews of cigarette consumption, this paper studies the text sentiment analysis of cigarette online reviews. This paper presents a feature fusion model of convolutional neural network and BiLSTM. Experimental results show that the proposed feature fusion model effectively improves the accuracy of text classification. The model can provide new insight for the evaluation of cigarette management, dynamically monitor the change of consumers’ emotion, and grasp the trend of consumers’ emotion in the tobacco market environment in time.
Compact Broadband Absorptive Bandstop Filter Based on Microstrip
Authors: Lu Qi , Wu Xianliang , Wang Chao
Published: November 2020

A compact broadband absorptive band rejection filter (ABSF) based on microstrip line is proposed. The filter consists of a pair of coupled wires and is loaded with a resistor. The coupling line of the filter has an open stub. The transmission zero point can be adjusted by adjusting the width of the coupling line. The center frequency can be adjusted by changing the length of the intermediate coupling line. Absorption performance can be achieved through loaded resistors. In this way, the signal not input in the filter will be dissipated in the filter and will not be reflected. When F0=3.8 GHz, 26% of the 20 dB bandwidth covers 3.15 GHz-4.2 GHz, the rejection depth of the stopband is less than -40 dB, and the in-band absorption is less than -10 dB.
Effectiveness analysis of machine learning in education big data
Authors: Zhou Ya , Song Zhuoqing
Published: November 2020

In the education big data environment, the online data and information of the current education industry learning resources supply become complex and diverse. Different scenarios and different analysis environments face different learners. Combined with the current education field in China, prediction, evaluation and analysis play an important application value and broad space for education and teaching. Machine learning technology has strong learning ability. Breaking the limitation of time and space, deeply analyzing the rationality of data relationship and obtaining the effectiveness evaluation are the effective means to explore this field. As well as a way to summarize the general rules and trends of using machine learning in education big data and educational data mining.
A Novel Tri-band Frequency Selective Surface
Authors: Wu Shenbing , Chen Mingsheng , Wang Chao , Wu Xianliang
Published: November 2020

This paper presents a novel tri-band frequency miniaturized frequency selection surface (FSS). The proposed FSS consists of single-node folding and single-node folding bending. The single-layer FSS exhibits three stop bands centered at 2.45, 3.61 and 5.79 GHz, with bandwidths of 0.42, 0.48 and 0.70 GHz, respectively. The proposed FSS has better miniaturization characteristics, and its cell size is 0.126 λ×0.126 λ. The three stop band frequencies of the FSS can be independently controlled by simply changing the length of the corresponding branch. In addition, the proposed FSS is symmetrical about the center and exhibits good resonance stability in both TE and TM polarization. The prototype of the proposed FSS was fabricated and tested, compared with the simulation results, the FSS showed stable performance. This method provides ideas for designing multiband FSS in the future.
Exploring Geographic Information Effects for POI Recommendation in LBSNs
Authors: Liu Shuang , Zheng Wenguang , Xiao Yingyuan
Published: November 2020

With the rapid development of global positioning technology, location-based social networks (LBSNs) have emerged. Massive check-in data provides development possibilities for mining user behavior preferences, and also brings a variety of location-based social services. For example, Point of Interest (POI) recommendation service can help users find their desired places. In this paper, we propose a novel POI recommendation model by integrating users’ preference and locations geographic information such that an efficient and accurate POI recommendation can be achieved. The model explores the spatial distribution of check-in points visited, the relevance of the category of check-in points visited, and the difference in the frequency of check-in points visited to generate a recommendation list for a particular user. Experimental results on the New York and Tokyo datasets from Foursquare show that this model has higher recommendation performance than other comparison models.
Research of Cartographer Graph Optimization Algorithm Based on Indoor Mobile Robot
Authors: Xu Jiali , Wang Di , Liao Maosheng , Shen Wenshuai
Published: November 2020

In the recent years, mobile robot technology has received widespread attention from all over the world, and Simultaneous Localization and Mapping (SLAM) technology has always been a hot topic for researchers. In this paper, the Cartographer graph optimization SLAM algorithm is studied. Aiming at the problem that loopback detection may have wrong loopback points in the presence of noise, similar and uniform characteristics, a delay judgment module is added to filter the wrong loopback points. The Cartographer algorithm before and after optimization is deployed to the mobile robot platform based on ROS, and a 2D laser scanner is used for the map of indoor building. The experiment results prove that after the algorithm is improved, the overall relative error of the map is reduced by 1.148%, and the accuracy of mapping is improved.
Electromagnetic scattering of two-dimensional rough cylinder based on statistical integral equation
Authors: Zhu Mengnan , Wang Anqi , Huang Zhixiang
Published: November 2020

The study of electromagnetic scattering from Gaussian rough surface is of great significance in radar reconnaissance, target tracking and ocean remote sensing. The moment method (MOM) is a commonly used method with high accuracy. However, in the past research, Monte Carlo method is used to simulate the Gaussian rough surface. In this method, a large number of random simulation operations are carried out, and then a statistical convergence result is obtained by counting the results of all operations. In this paper, the stochastic integral equation method SIEM(Stochastic Integral Equation Method) is proposed to calculate the electromagnetic scattering of Gaussian rough surface. SIEM avoids the difficulty of high difficulty precise modeling and repeated calculation many times, and only single modeling and calculation are carried out for the target problem. In this paper, the electromagnetic scattering of two-dimensional cylinder with Gaussian rough surface is solved, and the effectiveness of SIEM is proved by comparing with mom.
Progressive face super-resolution via learning prior information
Authors: Duan Yanfei , Liu Yintian , Wang Ruixiang , Yao Dengguo , Zhang Hang
Published: November 2020

Face super-resolution is a branch of the field of super-resolution. It is mainly aimed at the reconstruction of face images and distinguishing SR from general images. Face geometry prior information is used to optimize the face super-resolution network, which can generate high-resolution face images with better visual quality from low-resolution face images. In order to further improve the visual quality of reconstructed images, an improved face super-resolution network is proposed. In this paper, the key module of FSRNet is improved and new loss function is added to achieve a better face super-resolution network. Specifically, our job is to:(1) In the generated rough SR face image, we input low-resolution(16 x16) face image, then use the Deconv convolution enlarge images.(2)By introducing heatmap loss, facial attention loss and adversarial training to reduce the artifacts of FSRNet network. (3) We divide the network into two-step training, first train coarse SR network, get SR images quickly, and then we train the rest of the network. The final output is a super-resolution face with high visual quality.
Research on Sheep Recognition Algorithm Based on Deep Learning in Animal Husbandry
Authors: Ma Chao , Sun Xiaolin , Yao Chunxia , Tian Minglu , Li Linyi
Published: November 2020

At this stage, the domestic animal husbandry industry has a very important support role in the domestic market economy, and the sheep industry has received extensive attention as one of the important industries. Limb movements and activities can directly reflect the adaptability of sheep to the breeding environment and conditions, and provide a richer scientific and technical experience for sheep breeding. The sheep target detection is an important prerequisite for grasping the movement behavior of sheep. Therefore, how to efficiently and accurately identify and detect sheep has become the key to the development of sheep industry at this stage. In this paper, the Faster-RCNN neural network model based on the Soft-NMS algorithm is studied, which realizes the real-time detection and positioning of sheep under complex breeding conditions, and improves the accuracy of recognition while ensuring the detection speed. Experiments show that the proposed detection model can detect sheep with 95.32% accuracy and mark the location of the target in real time, which provides an effective data foundation for sheep behavior research and helps promote the development of high-tech animal husbandry effect.
A new dance generation task from start to end
Authors: Xu Xiao , Duan Lijuan
Published: November 2020

The dance generation task is an interesting task. The general task is to predict the next dance sequence through a given initial frame, or to convert from music to dance through audio information. However, these works cannot control the direction of dance generation and lack suitable ground truth to evaluate the generated results. In our work, we give the start frame and the end frame, which can effectively solve the above problems, and can also be understood as a standardized new task. Our experimental results show that through a two-stream CNN network, we can well complete the new task that we proposed.
Potential Risk Identification and Prevention of Large-scale UHV Construction Projects by Information Science
Authors: Cheng Jia-Xu , Yu Xue , Wu Luan-Ying
Published: November 2020

As China’s power transmission technology matures, UHV transmission technology has shown the unique advantages in optimizing energy resources, air pollution controlling, and achieving sustainable human development. So UHV grid engineering practice has entered a rapid, large-scale development of new stage in our country. Under the background of large-scale construction, there are huge challenges for key resources and critical management processes. In this paper, the key process risk identification is carried out from the key management process by information science and date mining.
Application Analysis of Face Recognition Technology in Video Investigation
Authors: Qiu Bo
Published: November 2020

This article analyzes the basic application process of face recognition technology. The content of this article includes face information collection, face information detection, face information recognition, synchronization warning function, etc. The author optimized the basic conditions by studying and optimizing the system operation algorithm, and at the same time strengthened professional training and information security management. In normal times, we apply face recognition technology in the detection and solving of suspects in the identification of suspects, and in the detection of crowded scenes and multiple scenes. In addition, face recognition technology will also be applied to road safety precautions and residential area safety precautions. The purpose of this article is to improve the application effect of face recognition technology in work, and to provide more convenient conditions for the smooth development of investigation work.
A Research on Online Grammar Checker System Based on Neural Network Model
Authors: Hao Senyue , Hao Gang
Published: November 2020

Grammar error correction is one of the most important research fields in natural language processing, while grammar checker is the tool to help people correct grammatical error. We focus our work on researching a better approach to an online grammar checker system to help people correct the grammatical error in their text more precisely, user-friendly, and efficiently. This new method of online grammar checker system is based on neural network model, Transformer, and is able to detect about 25 different types of grammatical errors in the text. We did the evaluation on our new online grammar checker system, and the final experiment results proved that we have found a good approach to the online grammar checker system.
Fire recognition with convolutional neural network based on transfer learning
Authors: Liu Xiang , Wei Guangcun , Rong Wansheng , Xiao Xinguang
Published: November 2020

The fire recognition model based on deep learning can avoid many defects in the traditional method, but its construction requires a large amount of data to train the network parameters, and it takes a lot of time. In order to improve the accuracy of the model, this paper proposes a fire recognition model TNVGG-19 (Transfer learning + Newly fully connected layer module + VGG-19) with convolutional neural network based on transfer learning. First, we use the strategy of transfer learning to train the feature extraction network. Secondly, based on the VGG-19 model, this paper adds a newly designed fully connected layer module. Considering that flame data belongs to small sample data, we adopted a data augmentation strategy. Experiments show that the TNVGG-19 fire recognition model based on transfer learning proposed in this paper can effectively improve the accuracy of fire prediction and reduce the false alarm rate.
Cancer image classification based on DenseNet model
Authors: Zhong Ziliang , Zheng Muhang , Mai Huafeng , Zhao Jianan , Liu Xinyi
Published: November 2020

Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination. Image processing introduced a promising strategy to facilitate disease classification and detection while diminishing unnecessary expenses. In this paper, we propose a novel metastatic cancer image classification model based on DenseNet Block, which can effectively identify metastatic cancer in small image patches taken from larger digital pathology scans. We evaluate the proposed approach to the slightly modified version of the PatchCamelyon (PCam) benchmark dataset. The dataset is the slightly modified version of the PatchCamelyon (PCam) benchmark dataset provided by Kaggle competition, which packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task. The experiments indicated that our model outperformed other classical methods like Resnet34, Vgg19. Moreover, we also conducted data augmentation experiment and study the relationship between Batches processed and loss value during the training and validation process.
AGA-GRU: An Optimized GRU Neural Network Model Based on Adaptive Genetic Algorithm
Authors: Bai Chenyao
Published: November 2020

The weight adjustment of gated recurrent unit (GRU) network depends on the gradient descent algorithm heavily, therefore this paper proposes an improved GRU neural network model based on adaptive genetic algorithm (AGA-GRU) to solve this problem. In this model, AGA is used to construct the optimization system, and the parameters of neural network model are optimized to improve the prediction performance. The results on UCI dataset show that the prediction accuracy of AGA-GRU model is significantly improved, and the generalization performance is stronger.
A Multi-Criteria Band Selection Method of Hyperspectral Images
Authors: Wang Zhonghua , Zhao Yulin , He Bangsheng , Wang Hao
Published: November 2020

Hyperspectral remote sensing images are characterized by many bands and large amounts of datum, which require the dimensionality reduction, but band selection is one of the basic methods for dimensionality reduction of hyperspectral datum. For this reason, this paper proposes a multi-criteria band selection method. Firstly, the intrinsic dimensionality of hyperspectral image is calculated by virtual dimension, then the subspace is divided according to the band correlation criterion; and secondly, the information criterion is adopted to select the high-quality exponential bands in each subspace; and finally, the most suitable bands in each subspace are selected by class separability criterion to form the optimal band subset. Various experiments show that, compared with the optimum index factor and adaptive band selection methods, the proposed method has better performance in the measurement of information volume and information redundancy.
Terminal Credibility Analysis Method for “One End Multi Network” Environment
Authors: Bin Dongmei , Yang Chunyan , Ling Ying , Yu Tong , Li Xin
Published: November 2020

it is necessary to monitor the terminal’s credibility continuously in both the government external network and the government internal network. When the terminal’s credibility is damaged and does not meet the network requirements, the terminal’s network access behavior will be blocked. To monitor the terminal’s credibility continuously, it is necessary to collect and send the terminal’s hardware assets, software assets, security configuration, security loopholes, network behavior, user behavior and other information to the management platform, which conducts machine learning, association analysis, statistics and so on, so as to give the terminal’s credibility in the current state. To analyze the credibility, the terminal needs to continuously monitor the change of the collected information and send it to the management platform without omission. However, in the “one end multi network” environment, the terminal access network is constantly switching, each network has its own management platform, each management platform can not reach each other’s network. Therefore, for the terminal, due to the switching of the network, its credibility analysis data is incomplete in a single network, which will affect the accuracy of the platform for terminal credibility analysis. The invention provides a method for enabling the management platform to obtain complete credibility analysis data.
APSO-LSTM: An Improved LSTM Neural Network Model Based on APSO Algorithm
Authors: Chen Keqiao
Published: November 2020

In the LSTM neural network model, the updating of the weights and threshold parameters depends on the gradient descent algorithm. When the number of hidden layers increases, the convergence rate decreases, and the adjustment of the weights may fall into local extremum, which affects the generalization ability and prediction performance of the LSTM model. Based on this, this paper proposes an improved LSTM neural network model based on APSO algorithm (APSO-LSTM). In this model, the root mean square error is designed as the fitness function, and APSO algorithm is used to build the optimization system. In the verification stage, the weight parameters of each neuron are globally optimized to improve the prediction performance of the model. The experimental results on time series datasets of UCI show that the prediction performance of APSO-LSTM model is significantly better than that of the standard LSTM model, which verifies the rationality of the model.
An Empirical Study on the Reform of Teaching Mode in Western Universities -- Taking the online and offline blended teaching reform of “Computer Foundation” in Xinjiang University as an example
Authors: Liao Yuanyuan , Liu Yan
Published: November 2020

The course “computer foundation” bears the responsibility of cultivating the future national information literacy. Facing the fact that the overall computer ability of freshmen in western universities is low and the teaching effect is not optimistic, the teaching reform is urgent. This paper takes the online and offline blended teaching reform of “computer foundation” in Xinjiang University as an example. Through the exploration and practice of the mixed teaching mode of “multivariate-six order”, the overall performance of students shows an upward trend, and students have a high degree of recognition and acceptance of the blended teaching. Facts have proved that blended online and offline teaching is effective in the practice of this course.