bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient Dense Spatial Pyramid Network for Lane Detection
Authors: Li Yinggang , Lu Xiaobo 
Published: July 2020

AbstractLane detection separating the roads and positioning each lane is a significant task in Autonomous-driving field. Most of existing methods take lane detection as a semantic segmentation problem, and the methods based on encoder-decoder networks achieve robust performances than traditional methods with the disadvantages of large amounts of parameters and high computational cost. To improve efficiency and better adapt to adverse environments, we propose an EDSPnet by compiling the efficient dense module of depthwise dilated separable convolution (EDD module) and dense spatial pyramid (DSP) module. The two proposed modules enable our network to make full use of the contextual information from different feature maps when compared to a chain of layers. Different from symmetrical segmentation networks, we concentrate on the encoder block and a simple decoder is designed to match the input resolution. The decoder is complemented by two methods of upsampling: bilinear interpolation and deconvolution, which guarantee the accuracy and efficiency simultaneously. Our method is evaluated on the TuSimple dataset, and the experimental results show that our network has less parameters and is still efficient even compared with the state-of-the-art semantic segmentation networks.
A New Text Sentiment Analysis Method Based on Chinese Morphological Features and HowNet
Authors: Cheng Yan , Liu Chunjia , Li Yunhong , Zhong Linhui , Feng Yue 
Published: July 2020

AbstractTraditional deep learning methods have two problems when using vectorized words as input. One is that they only consider the overall semantic information of the vocabulary, but ignore the morphological features of the Chinese vocabulary and the prior knowledge of the Chinese external knowledge base. Second, the word vector corresponding to each word will be limited to a single word vector training model. Aiming at these problems, we propose a double-channel convolutional neural network model based on Chinese morphological features and HowNet. First, the cw2vec model and the SAT model (Sememe Attention over Target Model) are used to train the word vectors. Second, the two different word vectors are used as the input of the two channels of the model. Finally, the convolutional neural network are used to extract the characteristics of the two channels to complete the sentiment analysis task. The comparative experimental results on the two data sets show that the proposed model achieves significantly better classification performance than traditional sentiment analysis methods.
New Decoupled 2-Dof Parallel Mechanism with Fully Spherical Workspace
Authors: Yu Shanshan , Zhang Jianjun , Li Weimin , Yang Xianhai 
Published: July 2020

Abstract
Decoupled 2-dof spherical parallel mechanism is an important valued spherical parallel mechanism. It is one of the preferred solutions in the field of large-angle spherical workspaces. By studying the configuration of the exiting kinematic decoupled spherical parallel mechanism, it is found that the key issues that it cannot achieve a fully spherical workspace is that the output angle range of the “linear input – rotation output” kinematic chain cannot reach 180°. Based on the input and output characteristics of the double rocker mechanism, two new ideas for constructing the configuration of the “linear input – rotation output” kinematic chain are presented. The output angle range of the new “linear input – rotation output” kinematic chain can reach 180°. Based on the new design idea for “linear input – rotation output” kinematic chain, two new kinematic chains are designed: P5R and PRR. Based on the above kinematic chains, two 2-dof decoupled parallel mechanism with a fully spherical workspace are designed, i.e. RR&P5R parallel mechanism and RR&PRR parallel mechanism.
The Fog Computing Attemper Layer Model Based on Agent Role
Authors: Wu Chenhan , Chen Wei , Chen Xiong 
Published: July 2020

Abstract
In this paper, a new role task scheduling method based on behavior flow is proposed for fog computing system. Because the traditional computing has changed from data oriented to edge oriented and distributed computing, the task scheduling method combines the role collaboration behavior and role Organization definition behavior in resource nodes by establishing the role behavior flow system, and establishes the role behavior flow mode from the center to the edge, so that the computing nodes can advance according to the behavior roles of their respective nodes Line classification processing. Therefore, it can effectively solve the problems of slow service response, high power consumption and frequent task interruption caused by large amount of data and redundant operations in traditional cloud computing systems.
Optimization Simulation Technology of Cement Packaging System Based On YL-235A Equipment
Authors: Zhou Quan , Wang Liqing , Liu Kai 
Published: July 2020

Abstract
Cement packaging is an important part of cement production and sales. This research is based on the automatic control of the cement packaging system, using the YL-235A automatic production line equipment to optimization and simulation the automatic control system of the process flow of cement packaging. The simulation study of cement packaging production line of a cement factory was carried out. Achieve the goal of optimizing production control, solving technical problems, improving equipment efficiency and continuously improving production capacity. The application of equipment simulation can greatly shorten the development cycle of the cement packaging control system, find problems in operation, save running cost. In order to solve some technical problems existing in the cement packaging control, an effective approach is provided.
A UWB-Based Low-Cost and Precise Localization Method for Mobile Robots
Authors: Shi Dongqing , Mi Haiyan 
Published: July 2020

Abstract
The need for automating cargo management in a manufacture environment is urgent. Recent advance in big data and artificial intelligence areas has made it possible. As we known, most cargoes are transported using autonomously guided vehicles or manpower. Many autonomously guided vehicles follow paths that are predefined and usually marked by labels that can be easily found by vehicles. The use of predefined path for vehicle navigation greatly limits the deployment of autonomously guided vehicles. A few vehicles use high accuracy Light Laser Detection and Ranging for sensing and guidance. In this paper, a low-cost and precise method for mobile robots has been developed. Considering the high nonlinearity of UWB ranging data, a least square method is used to find optimal locations and a gradient decent approach is applied for fast convergence to the optimal results. The proposed method is also able to diagnose the ultra-wide band data and will throw away any data corrupted by noise. Therefore, the robustness is obtained.
Cultivation of Solving Problems with Computing Thinking
Authors: Hu Yahui , Xiao Lei , He Ling , Fang Qiqing 
Published: July 2020

Abstract
Aiming to solve many people do not know how to use computers to solve practical problems in life and work, we design a series of detailed steps to analyze, design, represent, program and verify with computing thinking. We demonstrate our steps in detail with an example to help them which will encounter similar problems in the future.
Research on Optimal Shift Control Strategy of Two-speed AMT based on Fuzzy Control
Authors: Ma Ning , Hou Benjiang , Wang Yexuan , Ren Kai , Chen Shaoxin , Wang Peng , Lin Rongfeng , Ji Jianyi , Ji Yichun , Han Zhendong , Yang Chaohui , Wang Lingmin , Yu Nan 
Published: July 2020

Abstract
For a two-speed AMT pure electric commercial vehicle, the pros and cons of the shift control strategy directly determine the overall vehicle performance of the electric vehicle, such as power and economy. This article takes a certain type of commercial pure electric vehicle equipped with a two-speed automatic transmission as the research object, formulates a dynamic shift rule based on the torque map of the motor, structures an economic shift rule based on the efficiency map, and identifies road gradient and Car load quality, comprehensive consideration of the car’s loading quality, road slope, accelerator pedal opening, accelerator pedal opening change rate, vehicle speed and other parameters for optimal shift control strategy formulation. Comparing the dynamic shift strategy with the economic shift strategy, simulation analysis results show that the optimized shift control strategy can meet the requirements of dynamic performance and has the advantages of economy and energy saving.
Optimizing AGV Calculation MAP Path of Waste Smoke Recovery
Authors: Chen Haitao , Shu Meng , Lin Yizhen , Sun Shunkai , Shen Xue , Chen Ming , Hu Wangdi , Jiang Junjie 
Published: July 2020

Abstract
With the emergence of Industry 4.0, Technological reform and technological model innovation will surely be launched in the industrial manufacturing industry. Based on the intelligent development direction explained by Industry 4.0 and the current status of the tobacco industry logistics, AGV for waste smoke recycling is proposed Example map route optimization topic. In the complex operation and maintenance environment of the tobacco industry, unmanned transportation technology is used to automatically recover the residual cigarettes in the wrapping workshop, replacing the traditional manual forklift recycling model. Focusing on the auxiliary production of intelligent production, with the automatic recycling of residual cigarettes as the core, we will further optimize and perfect the intelligent recovery or supply of production waste and other raw and auxiliary materials.
Semantic Segmentation of Remote Sensing Images Based on Multi-Model Fusion
Authors: Miao Zuohua , Liu Likun , Ren Lei , Tang Yang , Chen Yong , Li Jun 
Published: July 2020

Abstract
Convolutional neural networks have created a new field in the research of semantic segmentation of remote sensing images. However, different network structures have different effects on the semantic segmentation of different land types. In this paper, the original data set is expanded, and an improved U-Net model is used to train a model for each type of feature target. Then combined with conditional random field (CRF) and image overlapping strategy for optimization processing; Finally, the two binary classification models obtained by training are fused to obtain multi-classified semantic segmentation images. Solve the obvious problem of large-scale remote sensing image edge stitching. The experimental results show that this method has higher accuracy in solving the segmentation of large-scale remote sensing images.
A Panoramic View System of Guilin Tourism Based on Interactive Projection Technology
Authors: Yu Ke , Shi Zhuo , Feng Ye , Shen Tianlong , Huang Zhixiong , Gu Yinwei , Deng Yingzhuo , Liang Yushi 
Published: July 2020

Abstract
This paper introduces a panoramic view system of Guilin Tourism based on virtual reality and infrared image tracking. Users interact with images on the ground through body movements, the system can achieve unattended, interactive guided, automatic voice interpretation function, with virtualization, high efficiency, easy maintenance, good interactive experience and so on. Suitable for placing in exhibition venues, scenic spots exhibition hall and so on, so that tourists can enjoy Guilin landscape without leaving home.
Atrial Fibrillation Prediction Algorithm Based on Attention Model
Authors: Shen Mengting , Zhang Luqiao , Luo Xue , Xu Jing 
Published: July 2020

Abstract
Atrial Fibrillation is a common arrhythmia. when the heart is in the state of atrial fibrillation, can not deliver enough oxygen rich blood to the body. It may be asymptomatic when AF occurs. The patient does not know that AF has occurred. If the patient is not treated in time, the consequences of AF may be fatal. The diagnosis of AF needs a doctor with rich clinical experience, because the ECG signal of AF type is similar to that of other types of heart rate, which is difficult to confirm. The purpose of this paper is to train a better model through machine learning method, to realize the automatic detection of long-term AF ECG signal, to predict AF in advance, to automatically classify, recognize and inform patients. In this way, even if the patient himself can know the physical condition in time, early treatment. Automatic prediction and diagnosis of AF by machine learning method can play a positive role in the treatment of AF, but the existing scheme is not ideal for the detection and recognition rate of AF type. This paper proposes a deep learning framework based on attention mechanism. The segmented data are sequentially input into the deep convolution neural network for feature extraction, and then input into the two-way recurrent neural network with attention layer to predict the AF signal. The accuracy of 99.1% in the existing data set is better than the existing deep learning classification algorithm, which proves the validity and feasibility of the model.
Metrological Research on the Application Literature of AI Education in China: 2009-2019
Authors: Cheng Yun , Zhang Man , Wang Yanli , Liang Fenglin , Wang Feng 
Published: July 2020

Abstract
Based on the CSSCI (Chinese Social Sciences Citation Index) full-text database of CNKI, this paper selects 468 journal articles on the application of AI in education from 2009 to the present. Based on the measurement visualization software CiteSpace, the data mining and content analysis are carried out, and the in-depth analysis is carried out from six aspects: the overall trend, core authors, research institutions and fund distribution, research hotspot, evolution trend and research clustering, etc. It also shows the research situation in the field of AI education application in recent ten years by means of tables and visual knowledge maps, which can provide important reference for researchers in this field.
Study in Network Stability based on MC
Authors: Fan Hongbin , Deng Gehong 
Published: July 2020

Abstract
Monte Carlo is a convenient method for statistics sampling theory of mathematical and physical. It is widely applied to the field of staff performance in geological, electric, medical, optical, water conservancy and so on. In order to effectively assess the stability and reliability of the network, this paper put forward an improved Monte Carlo evaluation method on the basis of the traditional Monte Carlo method. First of all, the paper elaborated the principle of improved Monte Carlo method and the specific method of how to calculate the stability of the network. The improved Monte Carlo method employed the recursive variance reduction method to get a smaller sample in the original sample, to ensure the estimate is unbiased. Secondly, the paper applied the ideology of permutation and combination to the network nodes and links in the model, and combined with the upper and lower boundary of network performance, to calculate the connected stability. Finally, the paper carried out experiments between the improved and traditional Monte Carlo method in Matlab simulation to contrast and analyze. The results show that the improved Monte Carlo method to evaluate the stability of the network has higher accuracy and smaller variance. So the experiment proved that it has the high feasibility and validity.
A Hybrid GCN and RNN Structure Based on Attention Mechanism for Text Classification
Authors: Gao Lingchao , Wang Jiakai , Pi Zhixian , Zhang Huaixun , Yang Xiao , Huang Peizhuo , Sun Jiasong 
Published: July 2020

Abstract
In the field of deep learning, for problems and tasks that are sensitive to time series, such as natural language processing or speech recognition, the recurrent neural network is usually more suitable. Long short-term memory (LSTM) is a representative network structure in recurrent neural network. It is time-dependent and enables a global representation of features. However, some problems such as the network parameters of LSTMs limit the applicability of their solutions. This paper proposes an improved hybrid structure of graph convolutional neural network and recurrent neural network. In the input layer, a two-dimensional convolutional neural network is used to build a text corpus map. Graphic embedding is used to preserve the global structure of the entire text graph structures. The LSTM layer and attention mechanism are used to fully implement text classification and improve the computational efficiency. The test results show that the hybrid network structure has better operation speed on the IMDb dataset.
Research on User Behavior with Collaborative Recommendation Based on Social Network
Authors: Wang Hongxia 
Published: July 2020

Abstract
With the rise of social networks and the existence of a large number of active users, a large number of data recording daily life are constantly generated. This kind of data has the characteristics of interactivity, real-time and sociality, which implies a lot of valuable information. Therefore, social network data has a very positive role in promoting the construction of smart city. In this paper, users’ annotation information of the item in social network is used. Similarity model is established with appropriate similarity calculation method. Users’ social relationship in social network is used to establish trust relationship and model for users. Personalized recommendation algorithm is improved to dig user behavior data, analyze users’ interests and hobbies. Information of interest or item can be recommended to meet the needs of users to the maximum extent. And the stickiness of users to the website is increased. Based on the widely used recommendation algorithm, this paper analyzes the user behavior with trust-based collaborative recommendation algorithm based on user relationship of social network.
A Robot Self-Positioning System based on Robust EKF Using Degree of Confidence
Authors: Shi Hui , Tan Yingzi 
Published: July 2020

Abstract
Self-positioning system is the key link of humanoid soccer robot, in which the robot needs to estimate its position and direction in the football field based on non-probing sensors. However, the modeling error of the robot system and the deviation of information acquisition bring the uncertain noise to the self-positioning system, which makes it difficult to carry out effective real-time positioning by using the traditional methods such as geometric measurement and track prediction. Existing filtering methods such as Kalman Filter and Monte Carlo Filter estimate the position and direction of the robot through observations, reducing the influence of uncertain factors. Although the accuracy of positioning is improved, the state estimation method relies on the accuracy of observations. The deviation in blurred image processing and camera ranging during the motion of the robot results in the deviation of the observations, which is more obvious in the case of multi-target observations. In this paper, a self-positioning method based on robust EKF using confidence is proposed to improve the accuracy of the robot’s self-positioning by integrating observations.
Classification Method of Ethnic Minority Patterns Based on Faster R-CNN
Authors: Kong Qian , Shi Zhuo , Feng Ye , Yang Ming , Zhang Mengxue , Zeng Shuzhen , Li Rongbin , Yu Ke , Shen Jing 
Published: July 2020

Abstract
The pattern on the costumes and brocades of ethnic minorities are of profound significance, but they are various and often composed of many kinds of patterns. It is not accurate and time-consuming to classify them only by artificial methods. Taking Yao’s pattern symbols as an example, this paper collects and arranges the pattern pictures on clothing and brocade, preprocesses the pictures and labels the patterns on the processed pictures according to the preliminary classification. After the data set is made, the data set is trained and tested by Faster R-CNN algorithm. The results show that this method can effectively identify and classify the patterns of Yao nationality while reducing the time-consuming, and the average accuracy can reach 88.71%. It provides a useful exploration to help more ethnic minorities realize the intelligent classification of patterns.
Research on the Focused Crawler of Mineral Intelligence Service Based on Semantic Similarity
Authors: Deng Shiqi 
Published: July 2020

Abstract
Large-scale general search engines have been unable to meet the needs of “specialized, sophisticated and deep” information in the field of mineral intelligence services. Vertical search engines have emerged at the historic moment, and the focused crawler is the key technology to achieve vertical search engines. This paper proposes a hybrid topic strategy based on text content and web link structure for the characteristics of mineral information field. In order to improve the topic relevance determination ability of the focused crawler, this article introduces HowNet and the word embedding technology in the field of natural language processing, and combines them to carry out the text-based topic relevance determination; at the same time, it also introduces the HITS algorithm based on the link structure of web pages. The topic strategy based on text content is organically combined with the topic strategy based on the webpage link structure to realize the recognition and prediction of the webpage topic. Simulation experiment results show that the method proposed in this paper can achieve a high recall rate and precision rate for the acquisition of mineral intelligence information on the Internet.
Trajectory Planning of Multi-arm Robot for Mandible Reconstruction Surgery
Authors: Zhao Honghua , Gong Xuyin , Sun Dianmin , Zhao Jian , Lin Yifei 
Published: July 2020

Abstract
The task of the surgical robot for jaw reconstruction is to complete the accurate and stable lifting and positioning of the restoration implant, and the continuous trajectory of its space is not high. Therefore, joint space is used for trajectory planning. The task of mandible reconstruction robot is to set the constraint of trajectory function at the beginning and the end. There is no mandatory requirement for the middle path. Therefore, the PTP (point to point) method with middle point is chosen. The middle point needed by PTP is far less than CP (continuous path), and it does not need frequent interpolation. The calculation avoids the singular position on the continuous trajectory, improves the safety and real-time of the robot control, and the reasonable selection of the middle point is also conducive to the realization of collision avoidance between the arms.
Design and Research of Intelligent All-area-advancing Tourism Cloud Platform in the Era of Big Data
Authors: Lin Qian , Wei Wei 
Published: July 2020

Abstract
In the era of big data, it is one of the important ways to make intelligent all-area-advancing tourism by informatization. This paper, through big data, cloud computing, mobile, Internet and other information technologies, combined with the practical application of tourism information in Shandong Province, puts forward the construction content of intelligent all-area-advancing tourism cloud platform, and designs the overall framework, system structure and database system of the platform, which is of great significance to integrate the tourism resources of the whole province to build an information resource sharing system of intelligent all-area-advancing tourism that integrating intelligent tourism management, intelligent tourist service and intelligent tourism marketing.