bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chinese spam filtering based on Stacked Denoising Autoencoders
Authors: Zhang Liuyan , Nie Yunfeng , Duan Shengyue 
Published: July 2019

Abstract
Aimed at the problem that the traditional feature selection method extracts the feature items and the filtering accuracy is degraded in the Chinese spam filtering process, this paper proposes a Chinese spam filtering method based on Stacked Denoising Autoencoder (SDA). Firstly, use the Continuous Bag-of-Words (CBOW) model training the Word2vec tool set for the processed corpus to transform word segments into vectors; the inputs are the word vectors; then apply the Stacked Denoising Autoencoder (SDA) to effectively extract the features text in unsupervised learning. Finally, the improved softmax classifier is used for regression classification. The test was carried out on the TREC06C dataset, the experimental results show that compared with Bayesian model, KNN classification algorithm and traditional Stacked Denoising Autoencoder, the accuracy, precision, recall, and f1 score of the method reached 93.5%, 94.8%, 92% and 93.2%, and had better dichotomous effect and robustness in the application.
The Application of Deep Learning in Vessel Extraction with Fundus Image
Authors: Yang Zuman , Wu Jiaxing , Jiang Kagyu 
Published: July 2019

Abstract
Fundus image is a very prevalent Biomedical data, so it is very important to choose a method to extract blood vessel from fundus images. Extracting vessel from the fundus image can be used for personal identity verification and assist doctor to diagnose diseases [1] such as diabetes. However, traditional extracted methods such as manually annotation area waste of time and energy. There is some another method [2] based on the image recognition, extracting the feature with the filter and segment with mathematical morphology method. But in the fact of the application, this method has low stability and accuracy. Traditional machine learning methods such as SVM [3] are also used for vessel extraction, but this method can only apply to dealing with one-dimensional data while the images showed up as matrix form. So, we have to stretch a matrix line by line or column by column into one-dimensional vectors [4-6]. However, by doing so, we will be face that some important information can’t be reflected because the position relation of rows and rows in the original matrix will be lost. Deep learning can process image data naturally and extract features automatically with convolution, and it is also popular for its good learning ability and high accuracy. U-net [7] is a convolutional network based on deep learning and widely used in biological image extraction, which constructs a U-shaped network from input image and output image. In this paper, building a U-Net convolutional network, which takes fundus image as input and vessel image as output, to extract vessels in fundus image. We established a training set with 20 fundus images and a test set with 20 fundus images. Finally, the accuracy rate of our U-net model on the training set can reach 99 percent, while the accuracy rate on the test set was stable above 99.5 percent.
Infrared Dim Target Detection Based on Multi-Feature Fusion
Authors: Wei Shuigen , Wang Chengwei , Zhang Congxuan , Yan Huibin 
Published: July 2019

Abstract
A novel infrared(IR) dim target detection algorithm based on multi-feature fusion is proposed in this paper. Firstly, the gray residuals map is obtained by calculating the 8-directional local gray residual. Secondly, the image is divided into a series of local image patches by using a sliding window, then the intensity mean of local image patches is constrained to achieve the local intensity mean constrained map. Lastly, the local image patch is further divided into 12 directional blocks, and the gradient direction constrained saliency map can be obtained by constraining the gradient direction of the pixels in each directional block. Then, the final saliency map is obtained from the above three feature maps by dot product operation, and dim targets are separated by threshold segmentation. The experimental results show that the proposed algorithm not only can effectively suppress background clutter and eliminate false targets, but also can accurately detect dim targets with high detection rate.
Implementation of automatic map based on saliency detection
Authors: Yuhong Liu , Xuan Wang , Xinyu Wang , Chunhong Cao 
Published: July 2019

Abstract
As one of the key technologies in film special effects production, virtual reality, augmented reality and digital image editing, image matting has been widely applied. In computer vision, art design and other aspects have played a very important role. In view of the limitations of traditional matting technology on background and the need for a large number of human interactions, this paper adopts an automatic matting algorithm based on saliency detection to solve this problem. Experimental results show that the improved RC algorithm can automatically mat out the images, and has a good effect and high efficiency in detail processing.
Part Attentioned Fine-Grained Categorization Using Context Sensitive Flow
Authors: Zhang Zhiqin , Zhang Bo 
Published: July 2019

Abstract
It is difficult to recognize fine-grained objects (e.g., dogs or cars) because of the challenges of difficult region localization and fine-grained feature learning. Current approaches neglect the fact that local context feature and global feature are mutually correlated and thus it is possible to use the related information from each other. In this paper, we propose a novel multiple scales attention fusing convolutional neural network which can learn region-based feature and discriminative object attention and at multiple scales in a mutually improved way. The learning is composed of several scales which consist of a classification sub-network and an attention fusing module. A Lrefine loss was proposed to refine the second sub-network category performance. We do some overall experiments and show that our proposed method achieves the best performance in two fine-grained tasks, with relative mean accuracy gains of 2.2%, on CUB Birds, and Stanford Cars.
A Rate Adaptation Scheme with Enhanced Loss Differentiation for LoRaWAN Communications
Authors: Xie HaoFei , Huang DaiWei , Long Yi , Wang QiuHua 
Published: July 2019

Abstract
As an emerging technology for long-range communication, LoRaWAN provides several data rates for transmission and the choice of data rates will affect the communication distance and the link quality. So how to allocate this communication resource in a network with a large amount of devices is a challenge. In this paper, we classify the reasons of packets loss as channel attenuation and data collision and then propose a rate adaptation scheme with enhanced loss differentiation. The proposed scheme can estimate two packet loss rates according to the channel attenuation probability model and data collision probability model respectively. And the selection of communication data rate is given by the balance of probabilities. Simulation results indicate that the proposed scheme is better than ADR and EXPLoRa on Data Extraction Rate and Goodput.
Research on Denoise Methods of Channel Estimation in OFDM System with High-speed Multipath Channels
Authors: Yao Ling 
Published: July 2019

Abstract
To solve the problem of low accuracy of channel estimation in OFDM systems in the scenario of high-speed multipath channels, it is proposed in the paper that a noise reduction scheme of channel estimation is based on DFT (Discrete Fourier Transform) algorithm. By analyzing the algorithm principles, the mathematical model of the system is built, and the performance of the scheme is analyzed. Simulation results reveal that in high-speed scenario, the scheme has effectively achieved low bit error rate and low mean square error, thus having good application value.
Design of a Small-size Broadband Circularly Polarization Microstrip Antenna
Authors: Sun Hang , Li Chang-sheng , Qiu Tian-ye 
Published: July 2019

Abstract
The information transmission of wireless system in coal mine has been faced with severe challenges, and the traditional wireless measurement method has the problems of low anti-interference ability and low reliability. The wireless system based on the temperature, humidity and harmful gas measurement of ultra-high frequency radio frequency identification (UHF RFID) technology can effectively improve the reliability of the system and enhance the safety of the operation in the coal mine. In order to detect the status in the coal mines in real time, this paper designs an ultra-high frequency micro-strip antenna with high gain, wide frequency band and circular polarization. Through the simulation, it can show the influence of different parameters on the antenna performance and obtain the optimal design size of the antenna with center frequency of 915MHz. Finally, through the experiment, it can obtain the micro-strip antenna with the gain of 3.84dBi and the circular polarization frequency band of 75MHz which meets the application of the ultra-high frequency radio frequency identification system in the coal mines.
Design of Burr Detection Based on Image Processing
Authors: Chen Xiaorong , Shi Gaohui , Xi Chuanli , Zhong Linshan , Wei Xiaoxiang , Zhang Kaikai 
Published: July 2019

Abstract
Traditional burr detection methods are increasingly unable to meet the requirements of deburring in terms of accuracy and efficiency. In this paper, machine vision is adopted. Firstly, the image of the workpiece is preprocessed, and appropriate edge detection operators are selected to detect the basic edge of the burr. Then, the closed burr edge is generated by detecting the slow changing area with the regional growth. Finally, the edge is judged to be burr by comparing the contour and other methods. Experiments show that this method has the advantages of fast, high efficiency and strong stability in the study of burr detection, and can meet the requirements of the project.
A vertical cavity surface emitting laser based on Fibonacci photon quasicrystal cavity
Authors: Liu Xiaolin , Sun Xiaohong 
Published: July 2019

Abstract
In this paper, a vertical cavity surface emitting laser (VCSEL) is designed based on Fibonacci photonic quasicrystal (FPQC) cavity. This laser uses a light source with a spectral center of 460 nm. Two different FPQC structures are designed and optimized to serve as the total reflection mirror (TRM) and the transmission mirror (TM) of the resonant cavity, respectively. Compared with the distributed Bragg reflection (DBR) cavity, the designed FPQC cavity laser has smaller far-field divergence angle and relative mode field area, as well as higher brightness.
Personalized Recommendation via Suppressing by Users and Items
Authors: Wang Can , Wang Kun , Wei Tao 
Published: July 2019

Abstract
An efficient recommendation system is fundamental to solve the problem of information overload in modern society. In physical dynamics, material diffusion based on binary networks has a wide range of applications in recommendation systems. However, material diffusion has the disadvantage of excessive diffusion and excessive attention to high-prevalence items. Most of the previous studies focused on reducing the popularity of the item. This paper suppresses the excessive diffusion between people and objects by simultaneously adjusting popular users and items. It evaluates the algorithm through two real datasets (Movielens and Netflix), which proves the method is superior to other algorithms in accuracy, diversity and novelty.
Research on Lost Circulation Risk Intelligent Identification and Processing System Based on CBR
Authors: Xiaorong Gao , Yingzhuo Xu 
Published: July 2019

Abstract
Due to the large number of complex and uncertain factors in the drilling process, it is very difficult to identify and deal with the risk of lost circulation by establishing an accurate mathematical model, and the existing intelligent methods are difficult to acquire or require large sample data. In view of the defects, an intelligent decision-making method based on case-based reasoning is proposed, and a software system for intelligent identification and processing of lost circulation risk is developed. In this system, the lost circulation cases of oil fields in the past are represented as case forms in the case base. The design idea of the case retrieval model is to design an improved similarity calculation model for different types of attributes, based on the traditional instance similarity calculation model, which can effectively solve the information uncertainty. The system was tested by field data and the results show that the lost circulation risk identification and processing method can meet the application requirements of the drilling site, and can effectively improve the intelligent level of lost circulation risk management.
Three-dimension Point Cloud Technology and Intelligent Extraction of Trace Evidence at the Scene of Crime
Authors: Liu Shuxia 
Published: July 2019

Abstract
Three-dimensional (3D) point cloud technology is a kind of high-tech for the extraction of trace evidence from crime scenes. It is also an artificial intelligence technology for criminal justice investigation and diagnosis with great development prospects. It is one of the frontier issues in the field of criminal science and technology research. The purpose of this paper is to explore and analyze how to use 3D point cloud technology to extract trace evidence of crime scenes on the basis of understanding 3D point cloud technology, and to discuss the intelligent features of 3D point cloud trace evidence extraction. The principle and characteristics of 3D point cloud technology are introduced and described by the method of comparison and example. According to the technical characteristics of laser scanning, the methods and steps of extracting trace evidence from crime scenes are designed, and the overall workflow is given. This paper analyzes the intellectualization of 3D point cloud technology for trace evidence extraction from the perspective of the fusion of technology and perception, and draws some conclusions through simple practical description. Research shows, trace evidence extraction based on 3D point cloud technology is the full embodiment of digitalization and intellectualization of crime scene investigation. Doing a good job in information collection, data sample database construction, human-computer interaction platform design, and the data and knowledge of crime scene diagnosis experience are the basis for the development of this intelligent technology. In addition, with the different characteristics of different crime scenes, the improvement and innovation of the 3D laser scanning method is also an important work in the future.
Visible Light Communications Relying on Cell-Free Amorphous Networks
Authors: Wen-yan Xu , Hong-yi Yu , Sen Yang 
Published: July 2019

Abstract
The user-centric architecture is the trend for constructing future networks. To improve user experience, visible light communication system adopts user-centric cooperative transmission technology, which breaks the traditional concept of network-centric. Generating user-centric cells for each user can make the best of network resources, which is of great significance in the 5G era with the design concept of small unit scenes. However, it is still in the initial stage, and the basic theories and key technologies need to be further studied. This paper expounds the new design concept, describes the characteristics and the forming process of user-centric virtual community centering on users, and introduces the key technologies of multi-user processing, channel estimation and network resource management combined with the unique features of visible light. This new mode of transmission can provide the user with the service of the network and improve the user’s peak rate and edge user performance.
Discussion on the development of mechanical and electrical integration of construction machinery in China
Authors: Meiying Hou 
Published: July 2019

Abstract
The rapid development of science and technology has changed the engineering application of the model, the backward technology and procedures such as microelectronics, information has been replaced by a variety of new technology, has realized the integration of mechanical products production and application, makes the product more safe and reliable, lower energy consumption amount, realized the high precision products and high quality control, the future will also toward miniaturization, greening, such as direction, product application is more efficient, convenient, and convenient people’s life, and provide technical support for the development of various industries. This paper discusses this problem in detail.
A power management circuit based on autonomous control
Authors: Cheng-ying Yang , Yong Chen 
Published: July 2019

Abstract
Based on the concept of external triggering and internal autonomous control, by using the semiconductor device with switching characteristics, using the function of capacitor voltage regulation and energy storage, and fully utilizing the charge and discharge function of the capacitor, the author introduces a new type of power management circuit. The circuit realizes the function of the device to independently control the power supply that is turned off when the storage, standby, etc. do not need to work. When the device needs to work, the power supply switch of the circuit is triggered by the external condition, and the power supply automatically supplies power to the device through the circuit. Therefore, the function of energy saving and energy saving of the device is realized, the battery life capability of the battery is improved, the loss of the power supply of the device is greatly reduced, and the technical problem that the battery of the current device is largely lost during idle and standby periods is solved, and the environmental protection and energy saving has Certain meaning.
Synchronized Vibration Transition of Three Exciters in Non-resonant Vibration System
Authors: Zhang Nan , Zhang Junling 
Published: July 2019

Abstract
The synchronized vibration transition has been proposed in the non-resonant vibration system with three exciters. Based on former man, the movement equations of self-synchronous vibration system with three rotors are replaced by differential equation of phase difference angle first, and the necessary conditions of synchronous movement for system are analyzed, stability and bifurcation of equilibrium points of vibration system are discussed. Firstly, dynamics model are established, differential equation of phase difference angle is deduced based on the dynamics equation of the vibration system. Then, the necessary conditions of synchronous movement are established, stability and bifurcation of equilibrium points of vibration system are discussed using Lyapunov theories. Finally, the effects of system parameters on synchronization stability about self-synchronous vibration system are investigated with numerical simulations.
Compressed sensing model for vibration signals of mechanical faults based on modulated wideband converters
Authors: Zhong Hua , Zuo Xiaobo , Dai Dongsheng , Liang Xiaolong 
Published: July 2019

Abstract
Based on Compressive Sampling, the Modulated Wideband Converter (MWC) sampling method can implement sampling at a rate lower than the required by Nyquist theory. This paper designs a spectrum reconstruction system, based on MWC, which can reconstruct the spectrum of Sparse-Frequency signal at the rate lower than Nyquist-Frequency, and verify through the simulation signals and the measured fault bearing vibration signals.
Text Tiling Text Segmentation Based on Hierarchical Dirichlet Process Model
Authors: Ye Wei , Gong Xiaogang , Zhang Quan , Ju Xiaoming 
Published: July 2019

AbstractText segmentation plays an extremely important role in many areas such as abstract extraction and information retrieval. Topic model is one of important methods in text segmentation. However, the current methods based on topic model generally rely on the manual setting of the number of topics. In order to improve the efficiency of text segmentation, this paper proposes a TextTiling text segmentation method based on Hierarchical Dirichlet process (HDP) model. Firstly, the method uses the HDP model to obtain a vector representation of the text in the topic space, which can also automatically generate the number of topics. Applying the theme vector to the TextTiling segmentation algorithm can implement text segmentation. The results show that the method can effectively improve the performance of text segmentation, and be free from manually set topics.
Research on Wear Status of Diesel Engine Cylinder Based on BP Neural Network and Instantaneous Speed
Authors: Qiao Xinyong , Gu Cheng 
Published: July 2019

Abstract
The instantaneous speed contains a large amount of diesel engine operating state information. This paper establishes the diesel engine dynamics model to obtain the instantaneous speed curve under different cylinder wear states, and extracts its four dimensionless parameters, then establishes the cylinder wear state model based on BP neural network using the simulated fault signal, and the actual vehicle test data is used to verify. The results show that the cylinder wear analysis model based on BP neural network can accurately determine the cylinder wear state.
Augmented Reality Museum Visiting Application based on the Microsoft HoloLens
Authors: Hou Weiting 
Published: July 2019

Abstract
Immersive technologies such as the Augmented Reality (AR), which attaches the virtual information to the real environment, are extending the research space and attracting various domain experts including museum experts. Applying the AR technology in the 3D relic information visualization and helping visitors to visit the museum are also hot research issues. Current museum solutions are mainly based on the website, smart phone and Virtual Reality (VR) technology, AR museum applications are also mainly deployed on the smart phone. In this paper, an AR museum application based on the Microsoft HoloLens is presented, and the Microsoft HoloLens is a kind of head mounted device (HMD). Besides, multiple user interaction forms are supported in this solution, such as gaze, voice command, voice speech and hand gesture. Evaluation about this solution has also done, and the conclusion can be drawn that most participants appreciated this solution in clear interface and completion aspects.
Research and Improvement of Isolation Forest in Detection of Local Anomaly Points
Authors: Gao Rongfang , Zhang Tiantian , Sun Shaohua , Liu Zhanyu 
Published: July 2019

Abstract
Three algorithms of classification-based, density-based, and isolation-based are researched and compared in this paper. It is concluded that Isolation Forest algorithm has characteristics of low time complexity and quantitative description of anomalies, which is obviously superior to other algorithms. However, it has disadvantage in detecting local anomaly point, which affects the accuracy of algorithm. Therefore, an improved algorithm based on Isolation Forest is proposed, of which the main idea is the K-means algorithm divides samples into different clusters, and the local anomalies before clustering are transformed into global anomalies of adjacent clusters, and finally the anomaly scores of the samples are calculated in each cluster. Experimental results are that the improved algorithm is better than Isolation Forest algorithm in detecting local anomaly points.