bluuebunny/crossref_metadata_2025_split huggingface dataset:
Defect detection in textile fabrics with optimal Gabor filter and BRDPSO algorithm
Authors: Zhang Jiawei , Li Yueyang , Luo Haichi 
Published: November 2020

Abstract
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

Abstract
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

Abstract
“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

Abstract
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

Abstract
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

Abstract
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

AbstractThe 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.