bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent Control of Threshing and Cleaning System Based on Big Data
Authors: Chen Xiusheng , Tang Huaizhuang , Xue Zhiyuan , Xing Xiaoming , Li Mengwei , Lv Guodong , Zhang Tongshuai 
Published: November 2020

Abstract
When the combine harvester works under different environmental conditions, its threshing and cleaning is a complicated process. To deal with changes in soil, climate, and crop conditions, it is extremely important to adjust and optimize the internal parameters of these machines. It is because the experience of agricultural machinery operators is different to cause uneven harvest quality, which cannot guarantee that all harvesters reach the best harvest state. Now, RS images of sensors and satellites are quite clear, and the error is controlled within the acceptable range. With the advent of the era of big data and AI, many fields have successfully applied them. In the agricultural field, innovation and transformation led by AI are also taking place. The article takes the intelligent control of the threshing and cleaning system as the object of study, which is specifically reflected in the impurity rate, crushing rate, and loss rate of cereals. The goal is to minimize the loss rate during harvest, and the lowest impurity rate and crushing rate in the grain after harvest. This subversive movement will completely rewrite the development status of China’s agricultural machinery.
A Summary of Research on Intelligent Dialogue Systems
Authors: Wang Bangjing , Cao Hui 
Published: November 2020

AbstractDialogue systems have always been an important research direction of natural language processing. In recent years, the potential and commercial value of dialogue systems have attracted people’s attention, and technologies such as intelligent customer service and chatbots have gradually become popular. First, it outlines the concept and classification of the dialog system; then introduces the constituent modules of the dialog system, and mainly studies the key technologies and mainstream ideas; finally, summarizes the problems encountered at this stage and looks forward to the future research direction.
Application of Artificial Intelligence Technology in College Physical Education
Authors: Zhang Li 
Published: November 2020

Abstract
With the rise of a new round of scientific and technological revolution, the vigorous development of artificial intelligence technology has gradually entered human daily life. Physical education, as one of the daily teaching activities of human beings, is an indispensable part of the current teaching evaluation and students’ quality assessment in Colleges and universities. With the development of the times, there are many shortcomings in the traditional teaching practice, and the unique advantages of artificial intelligence technology will largely make up for the shortcomings of traditional teaching. At present, the integration of physical education and artificial intelligence technology is still in the preliminary exploration stage. Therefore, this paper will first introduce the development history and main content of artificial intelligence technology. Then, starting from the reality of physical education in Colleges and universities, this paper analyzes the problems existing in traditional physical education teaching methods. Secondly, the paper puts forward the teaching methods of artificial intelligence technology such as virtual reality technology, big data analysis technology and intelligent recognition technology as the innovation points, in order to solve the shortcomings of traditional sports teaching method. The last part is the conclusion, analyzes the application prospect of artificial intelligence technology in college physical education.
Calculation and Application of Xin’an Painting School Art Style Model
Authors: Wang Decheng , Chen Yan 
Published: November 2020

Abstract
Artists and critics generally make artistic analysis of paintings based on their experience and subjective consciousness. In order to calculate the art style of the Xin’an School of Painting objectively, this paper attempts to propose several digital features including redundancy, degree of order and complexity to describe the artistic style of paintings algorithm under the framework of computable aesthetics on the basis of Shannon entropy in information theory. Taking the five painters of different periods as the research object, the digital features of the selected paintings are calculated and analyzed. The experimental results show that the proposed digital features can reflect the artistic style and the differences between different artists of the Xin’an School of Painting in different periods to some extent, which are basically consistent with the subjective aesthetic perception of the School.
Research on influencing factors of coal price based on improved principal component analysis and grey correlation method
Authors: Duan Zhaobin , Meng Yanjun 
Published: November 2020

Abstract
As an important basic energy in China, the price fluctuation of coal is related to the operation of China’s coal market and the whole energy market and coal price analysis has always been the focus of the industry. By establishing an improved principal component-grey correlation analysis (PCA-GRA) model, the influencing factors of coal price were analyzed and tested one by one, and the influence degree of the influencing factors such as total coal production, total coal consumption, coal import and export volume, and world coal price on China’s coal price was studied. The results show that coal price fluctuation is greatly influenced by coal energy storage level and market environment level. In order to promote the development of coal industry, some suggestions are put forward.
Transformation Of Non-Commitment Decision Risk Of Three-Way Decision Based on Limited Time Observation
Authors: Pang Meng Yang , Suo Zhong Ying , Bao Zhuang Zhuang , Huang Lin 
Published: November 2020

Abstract
In order to avoid or reduce the loss of direct decision-making caused by insufficient information, on the basis of traditional two-way decision, the non-commitment decision is added in three-way decision. However, in most practical applications, the final decision needs to be made within a limited time, the risk of non-commitment decision will increase with time and even be equivalent to the risk of commitment decision. Therefore, from the perspective of multi-time decision points, the method of transformation of non-commitment decision risk of three-way decision based on limited time observation is proposed. First, the non-commitment decision risk is analyzed and the time risk coefficient is introduced to explain the law of non-commitment risk changing with time. Then, the subjective and objective factors in the decision process are analyzed so as to make the decision process more in line with human cognition and thinking logic. Finally, an example is given to verify the effectiveness and practicability of the method.
Modelling Dependence Structure of Exchange Rate and Energy Price by C-Vine Copula in China
Authors: Li Yangheling , Liao Ruofan , Sriboonchitta Songsak 
Published: November 2020

Abstract
This study aims to investigate the co-movement between the exchange rate and the different energy prices by using the canonical vine copula approach. China needs to import large amounts of oil, natural gas, and other energy sources every year, thus, the exchange rate plays a vital role in the energy market import in China. This study uses C-vine copula to estimate the interdependence between the exchange rate and the different energy prices including gasoline price, coal price, and liquified natural gas price. The result shows that the exchange rate has a positive dependency effect on all energy prices, especially the highest degree of interdependence between the exchange rate and gasoline prices.
Radio Frequency Temperature Measurement System Based on PDC Material
Authors: Tao Yongxiang , Li Tianli , Huang Dehang 
Published: November 2020

Abstract
PDC (Polymer Derived Ceramics, PDC) material sensor is a new type of temperature sensor, which has the characteristics of high temperature thermal stability, corrosion resistance, oxidation resistance and creep resistance. It is a high temperature polymer precursor ceramic temperature sensitive material. Using its characteristics, it can solve the problem of high temperature. There is a resonant cavity inside the sensor, and the resonant frequency of the internal resonant cavity will change accordingly when its temperature changes. In this study, a set of RF temperature measurement hardware system was developed using the perturbation method and the corresponding relationship between the resonant frequency and temperature of the PDC material temperature sensor.
Bivariate Oblique Decision Tree Algorithms Based on Linear Discriminant Analysis
Authors: Liu B , Song J , Jiao S R 
Published: November 2020

Abstract
To solve the problem of the low efficiency of the traditional decision tree algorithm in dividing decision boundaries that are not parallel to the coordinate axis as well as the problem of the difficulties and the limited interpretability of some oblique decision tree algorithms in solving high-dimensional data covariance matrix, the present study proposed a series of bivariate oblique decision tree algorithm based on LDA. In the algorithms, LDA was used to determine the division boundary, and impurity and variance analysis were used as a criterion for the selection of split variables, thus ensuring the interpretability of this method while improving the efficiency of defining the decision boundaries that were not parallel to the coordinate axis. Experiments conducted in multiple data sets show that algorithms has achieved satisfactory results.
Study on Multi-user Detection Technology of MIMO-OFDM System
Authors: Jiang Han , Zhou Xiaoli , Zhu Zhiyuan , Guo Jing 
Published: November 2020

Abstract
This article introduces the detection method of MIMO-OFDM, then introduces the V-Blast algorithm and compares the main interference cancellation algorithms. Finally, combining the advantages of the OBF algorithm and the V-BLAST signal detection algorithm, an improved OBF algorithm is proposed.
The interference effect and reason analysis of the three lasers to CCD detector
Authors: Peng Tian , Shao Zhiqiang , Xie Minyong 
Published: November 2020

Abstract
At present, the two lasers including Nd:YAG pulse laser and Nd:YAG high frequency laser, which can both produce laser with wavelength of 1064nm, are widely used in in medical, military, people’s livelihood and other fields. Their working material is Nd:YAG crystal. The appearance of passive Q Nd:YVO4 crystal of graphene quantum dots can improve the pumping efficiency of Nd:YAG high frequency laser and increase the peak power. In this experiment, the interference effect of the three lasers will be interfered with CCD, and the interference effect generated under different laser power and frequency will be measured. The interference mechanism of the three lasers will be analyzed to lay a foundation for their application.
A dynamic port isolation method
Authors: Tao Sun , Hai Wang , Yang Gao 
Published: November 2020

Abstract
With the rapid development of enterprise information construction, the enterprise internal network information interaction is frequent, which greatly improves the efficiency of enterprise work, but the problem of network security is increasingly serious. In view of the above problems, this paper proposes a port dynamic isolation method, which can complement the existing security tools, and provides an idea for solving the security of intranet.
Sentiment Analysis For Product Reviews Based on Deep Learning
Authors: Xiao Shaozhang , Wang Hexiang , Ling Zhi , Wang Lanfang , Tang Zhaoxia 
Published: November 2020

Abstract
With the popularity of the Internet and e-commerce, the sentiment analysis of text can help users to quickly and accurately obtain effective information they are interested in from massive product reviews to purchase satisfactory products. In this paper, a sentiment analysis system for product reviews was designed based on deep learning, and the digital and electronic products on Jingdong Mall with at least 100,000 reviews were crawled as the training data set. After data pre-processing operations such as word segmentation and removal of stop words for product reviews to remove useless features, the feature vectors were constructed based on the bag of words model and word2vec method, and then three classification algorithms, namely LSTM, Naive Bayes and logistic regression were used to model reviews. The LSTM algorithm is significantly superior to Naive Bayes and logistic regression algorithm in the training stage, and provides a reliable reference for the analysis of product reviews.
Investigating Back-Translation in Tibetan-Chinese Neural Machine Translation
Authors: Liu Ding , Li Yachao , Zhu Dengyun , Liu Xuan , Ma Ning , Zhu Ao 
Published: November 2020

Abstract
In recent years, the proposal of neural network has provided new idea for solving natural language processing, and at the same time, neural machine translation has become the frontier method of machine translation. In low-resource languages, due to the sparse bilingual data, the model needs more high-quality data, and the translation quality fails to achieve the desired effect. In this paper, experiments on neural network machine translation based on attention are conducted on Tibetan-Chinese language pairs, and transfer learning method combined with back translation method is used to alleviate the problem of insufficient Tibetan-Chinese parallel corpus. Experimental results show that the proposed transfer learning combined with back translation method is simple and effective. Compared with traditional translation methods, the translation effect is significantly improved. From the analysis of translation, it can be seen that the citation of Tibetan-Chinese neural machine translation is smoother, which is greatly improved compared to the translation without back translation. At the same time, there are common deficiencies in neural machine translation such as inadequate translation and low translation loyalty.
Prediction of stocks with high transfer based on ensemble learning
Authors: Ni Jie , Zhang Linghong , Tao Jiaming , Yang Xiaorong 
Published: November 2020

Abstract
The “high transfer” dividend policy implemented by listed companies is a special phenomenon in the Chinese stock market. The stocks with high transfer can generate significant positive excess returns before and after the announcement date, so it is important for investors to predict the high transfer stock as precisely as possible. Previous studies mainly focused on regression models to make predictions, such as logistic and probit models, the prediction results of which are average. This paper uses the data of the Chinese A-share market for the last eight consecutive years and screens out 13 important factors through stock dividend theory. We construct three ensemble models for prediction, namely XGBoost, LightGBM, and CatBoost. The results show that the three ensemble models are far superior to the traditional Logistic model. And the prediction effect of the three ensemble models have been improved after Bayesian parameter tuning. Furthermore, three models are fused by simple voting and rank-score, it turns out that almost all metrics, including accuracy, precision, F1 value and AUC value, perform better than the single model. In addition, the top 10 and top 20 hit rate reach 100%, suggesting that the fusion model can predict the top 20 stocks with high transfer accurately, which has certain practical significance for strengthening the security of investment.
Hierarchical Convolutional Features Fusion for Visual Tracking
Authors: Zhang Fan , Chang Shuo 
Published: November 2020

Abstract
Hierarchical convolutional features have different impact on the tracking performance, as the higher convolutional layers encode the semantic information of targets and earlier convolutional layers are more precise to localize targets. In this paper, we propose a novel scheme for hierarchical convolutional features fusion for visual tracking. In the proposed scheme, hierarchical convolutional features are first concatenated to form the cascading feature at the feature level, and then a convolutional layer is added to reduce the feature dimension. Discriminative correlation filter (DCF) is finally utilized to obtain the target location, which is treated as a differentiable layer in the neural network. The experimental results demonstrate that our proposed scheme achieves superior performances on the visual tracking benchmark.
Real-time indoor semantic map construction combined with the lightweight object detection network
Authors: Gao Xumin , Jiang Lin , Guang Xingyu , Nie Wenkang 
Published: November 2020

Abstract
Aiming at the lack of semantic information of indoor objects in the grid map commonly used in the indoor mobile robot, this paper proposes a real-time indoor semantic map construction method combined with the lightweight object detection network: firstly, we proposes a lightweight object detection network which is called S-SSD (ShuffleNet-SSD) by improving the SSD (Single Shot MultiBox Detector) network, it can be used to extract the semantic information of indoor objects in real time; then the semantic information of indoor objects is transformed into the grid map which is created by mobile robot, so the indoor semantic map is constructed. The effectiveness and superiority of S-SSD in the indoor object detection of mobile robot are verified by the comparative experiment, and the effectiveness of the real-time indoor semantic map construction proposed in this paper is verified by the experiment of semantic map construction.
Building extraction from remote sensing image based on improved segnet neural network and image pyramid
Authors: Yu Shisen , Xie Yuehui , Liu Congna 
Published: November 2020

Abstract
Aiming at the problems such as the insufficient use of context information in high-resolution remote sensing image by convolutional neural network and the time-consuming training model, a building extraction method based on the combination of improved Segnet neural network and image pyramid is proposed. First, the Gaussian and Laplacian pyramid algorithms are used to obtain the multi-scale and multi-resolution detailed feature images of the original image respectively, and the network is trained together with the original image, so that the model can learn and extract more abundant image features. Secondly, in order to reduce the complexity of the network, reduce the encoder and decoder structure in the original Segnet neural network, save the calculation cost and effectively avoid the occurrence of over-fitting and other phenomena. The experiment uses Massachusetts buildings as the dataset. Compared with the original segnet neural network extraction method, the proposed method has significantly improved the extraction accuracy and extraction efficiency.
Generative Pedestrian Trajectory Prediction with Graph Representation
Authors: Zhang Haoyu 
Published: November 2020

AbstractMulti-agent trajectory prediction is one of the core modules of unmanned driving and intelligent robots. The traditional method is difficult to measure the relationship between multiple agents, and the modeling ability is rigid. Nowadays, most of the methods make less use of geographic information, and social relationship modeling is not sufficient. Our model uses Graph Neural Network (GNN) to measure social relationships to improve its usability. The model is built by the basic Conditional Auto-encoder (CVAE) framework, using Gaussian Mixture Model to mix multiple Gaussian distributions and the possibility of obtaining more potential space through dynamic integration to make the model achieve better results. Our model has achieved excellent results on the Stanford Drone Dataset. The evaluation by average displacement error (ADE) and final displacement error (FDE) metrics has exceeded the majority of existing models.
Design and application of learner emotion recognition for classroom
Authors: Su Chao , Wang Guozhong 
Published: November 2020

Abstract
The emotion recognition of learners in the classroom plays an important role in improving classroom efficiency. At present, the recognition methods based on traditional image processing generally have problems such as low recognition accuracy, difficulty in feature extraction. In order to effectively solve the above problems, a deep learning-based method is proposed to perform emotion recognition for learners. This method first replaces the DarkNet-53 of YOLO v3 by introducing the convolution structure of MobileNet, which makes the model lighter and reduces the parameters. Then improve the loss function of the model by using GIoU loss. Finally, K-means clustering algorithm is used to obtain an anchor suitable for the emotion recognition scene on the self-made dataset. Thus, a high-accuracy and lightweight learner emotion recognition model ER_YOLO is obtained. Experimental results show that the improved model mAP increased by 4%, F1 score increased by 3.2%, detection time reduced by 1/3, and parameters reduced by 1/10.
A Deep Composite Predict Network for Eye Disease
Authors: Kong Weiyi , Yang Menglong , Huang Qinzhen 
Published: November 2020

Abstract
Different areas of eyes reflect different states of internal organs of human body. That can assist to detect the disease internal organs, facilitate the prevention and cure by analyzing the image of human eyes. In this paper, we propose a new algorithm, Deep Composite Predict Network (DCP-Net), combined deep learning technique with traditional Chinese medicine theory, which can segment interesting areas of the human eyes, detect the disease eyes texture and then predict internal lesion organ. Aimed at helping doctors to diagnose quickly. The proposed algorithm consists three sub-modules, i.e., multiple segmentation sub-module, fast detection sub-module and predict eye disease module. The multiple segmentation module is solved by the improved retraining U-network, which can obtain an accurate eye to segment interesting areas. The fast detection module is based on enhanced Yolo framework. The predication disease method combined Chinese traditional theory with computer vision technology. Finally, our network can accurately detect the disease of human eyes and predict lesions of internal organs, which can help doctors quickly diagnose lesion organs by human eyes.
TSFnet: a new two-stream fusion framework for scene text detection
Authors: Zhou Ziheng , Zhao Xuezhuan , Pei Lishen , Li Lao , Pan Jiahao 
Published: November 2020

Abstract
In order to solve the problem of algorithm robustness caused by scale change and imbalanced distribution of classes in the scene text detection task, we propose a new two-stream fusion framework TSFnet. It is constructed by the Detection Stream, the Judge Stream and the Merge output algorithm. In the Detection Stream, we propose a loss balance factor (LBF), which is used to optimize region proposal network. Then, the Regression-net and the Segmentation-net are used to predict text global segmentation map and its corresponding coordinates probability score. In Judge Stream, we use the feature pyramid network to extract the Judge map. In the process, the LBF is calculated to support the Detection Stream. Finally, we design a novel algorithm to fuse the outputs of the two-stream, and the precise position of the text is localized. Extensive experiments are conducted on the ICDAR 2015 and the ICDAR2017-MLT standard datasets. The results demonstrate that the framework performance is comparable with the sate-of-the-art approaches.