bluuebunny/crossref_metadata_2025_split huggingface dataset:
Traffic Flow Prediction Method Based on Deep Learning
Authors: Jiang Luofeng 
Published: October 2020

Abstract
Accurate traffic flow forecasts provide an important data basis for traffic management departments. This paper proposes a traffic flow prediction model based on deep learning, which combines Convolutional Neural Network (CNN), Long Short-term Memory (LSTM) and Support Vector Regression (SVR) features: use CNN neural network to mine the spatial characteristics of traffic flow, and then input the time series features captured by LSTM neural network into the SVR model for traffic prediction. The actual traffic flow data of intersections in Mianyang City are selected to verify the CNN-LSTM-SVR hybrid model, and compare it with the CNN model, LSTM model, and SVR model. The results show that the proposed prediction model has higher prediction accuracy.
Prediction of Traffic Flow on Highway Ramp in Scenic Area
Authors: Du Chuanhong , Liu Licai , Shi Shuaishuai , An Jiao 
Published: October 2020

Abstract
Through the phase space reconstruction technology, it is proved that there are chaotic behaviors on highway ramp. The establishment of a traffic flow prediction model can also solve the impact of unexpected accidents or delayed management. Short term traffic flow prediction is also an important prerequisite for the intelligent traffic management system. The wavelet transforms neural network can be used to realize the short-term prediction of the traffic flow on the highway ramp.
Non-Crypto Authentication for Smart Grid Based on Edge Computing
Authors: Liu Wei , Song Jianfang , Wu Haitao , Lei Wenxin , Huang Fuquan , Hou Wenjing , Liang Honghao , Wen Hong 
Published: October 2020

Abstract
As a new computing architecture, edge computing has gradually penetrated into the smart grid and provide real time monitoring and control. However, as a new type of node arranged in the network, its own security protection has become an incredibly challenging task. Security authentication is the first line of defence for security protection. Due to its fast and light-weight characteristics, physical layer channel information access authentication technology for non-cryptographic packets is a potential identification method for the smart grid terminal access under the edge computing system, in which the real time response is incredibly important. This paper aims to investigate the physical layer authentication method compared with the traditional upper layer authentication technology, and an authentication scheme suitable for smart grid is proposed.
Bone Age Prediction Method Based on Convolutional Neural Network
Authors: Hu Bin , Shang Lidan , Cheng Feng , Sheng Hai , Pan Jiayan , Yin Dong 
Published: October 2020

Abstract
How to accurately observe whether children are developing normally has always been one of the focuses of medical research. Based on the convolutional neural network, this paper proposed a bone age prediction method. Firstly, the hand bone data set was constructed. Secondly, the prior frame required by the neural network was obtained by K-means clustering. Finally, fine-tuning, training, detecting and cropping were performed to achieve bone age classification. The experimental results show that our method achieves the smallest average error and has strong practicability.
Research on Integrated Algorithm Based on Convolutional Neural Network for Rice Disease Identification
Authors: Liu Zixi , Zheng Xingqiang , Xiao Zihan , Bao Junyu , Shuang Liang , Sheng Fang , Jie Lirui , Yu Tanzhou , Qiu Zhangyan 
Published: October 2020

Abstract
Rice is the staple food in many Asian countries. The effective intelligent identification of rice diseases is helpful to improve the industrialized management of rice, reduce the input of manpower and improve the efficiency. In 2018, China’s first intelligent identification and service system for rice pests and diseases was released, but its average identification rate is only 80%. This paper takes 4 diseases of rice as the research object, adopts supervised learning, uses convolutional neural network to extract the characteristics of each disease, and integrates the extracted characteristics with AdaBoost algorithm to improve the accuracy. According to the research quantity and the characteristics of the research object, a 10-layer convolutional neural network and three convolution kernels are designed to miss the important information of the image as much as possible. According to the method in this paper, the correct recognition rate of the four diseases of Bacterial Blight, Flax Spot, Sheath Blight and Leaf Sheath Spot was 95.64% on average, which is higher than 5.56% and 14.69% respectively when the convolution neural network and AdaBoost algorithm are stable. Therefore, compared with the classical convolutional neural network and AdaBoost, the integrated algorithm of convolutional neural network proposed in this paper has a higher accuracy in rice disease research.
Joint Proportional Task Offloading and Resource Allocation for MEC in Ultra-dense Networks with Improved Whale Optimization Algorithm
Authors: Zeng Yanling , Chen Wen , Tang Zhangbin , Wu Jiaxing 
Published: October 2020

Abstract
Computationally intensive and latency sensitive applications put forward strict requirements on mobile user equipments (UEs) to provide the required computation capacity and delay. Mobile edge computing (MEC) in ultra-dense network (UDN) is considered as a promising solution that can reduce the computation limitations of UEs and extend their battery life by computation offloading. But the dense deployment of UEs, and the limited resources of small cell base stations (SBSs), which imposes more delays on computation offloading in MEC environment. In this paper, using the characteristics of UDN, we study the computation offloading management scheme based on the weighted sum of task completion time and energy consumption in MEC system. Then, we propose a computation offloading framework and formulate joint task offloading and resource allocation problem in UDNs to minimize overall cost consumption. Based on Genetic algorithm (GA) and Whale optimization algorithm (WOA), we adopt a heuristic algorithm named as GAWOA to solve this problem. Numerical results show that the algorithm has a fast convergence speed and better performance than other benchmark algorithms.
Modeling of Reef Distributed Reconnaissance and Early Warning System Based on DoDAF
Authors: Wang Yan , Chen Jian , Na Chenxu , Li Zhicheng , Gao Song 
Published: October 2020

Abstract
In view of the new requirements of the reconnaissance and early warning system in the complex battlefield environment, a distributed architecture design method for the early warning and reconnaissance of reefs is proposed. This paper presents the design process of DoDAF based distributed reconnaissance and early warning architecture framework for reefs, constructs the architecture model of specific operational activities by using TD-CAP software, describes the relationship between various combat platforms from different perspectives through different views, and evaluates the consistency and operational effectiveness of the distributed detection and early warning system. The evaluation results show that the model has good integrity and consistency, and the combat effectiveness is significantly improved compared with the non distributed reconnaissance and early warning system.
Multi-PaaS Oriented IoT Dual Middleware Model in 5G Era
Authors: Huang Shuai , Hu Shejiao , Hou Zhengfeng , Niu Zhao , Wu Lin 
Published: October 2020

Abstract
The PaaS platform based on cloud computing technology provides a large number of professional and flexible services in the fields of intelligent voice and computer vision. The large-scale application of 5G technology enables the software and hardware of the Internet of Things to use the PaaS service to improve the intelligence level of the system and reduce development costs. However, the problem of service invocation and service selection in the process of combining the IoT system with multiple PaaS platforms directly affects the interaction between the IoT perception layer and the application layer. This paper proposes a 5G multi-PaaS-oriented IoT dual middleware model. The unified communication protocol DMCP is designed. DMCP implements unified parsing of messages based on variable-length data items, which reduces the protocol conversion between the perception layer terminal device and multiple PaaS platforms. Complexity and high communication efficiency. Designed the PaaS service selection algorithm DMPS, DMPS comprehensively according to the equipment demand preferences and the overall performance of PaaS services, to achieve accurate service selection in the multi-PaaS platform at the application layer. Finally, examples and comparative tests verify the feasibility and effectiveness of the proposed model, protocol and algorithm.
Architecture Design of Application Specific Power Chip Security for Power Terminal in Smart Grid
Authors: Lv Zhining , Tang Tao , Du Yu , Wang Huiqin , Zeng Xiangjun , Liu Dongqi 
Published: October 2020

Abstract
Power terminal based on chip is the development demand of smart grid. Aiming at the problems faced by the current power terminal based on chip, an architecture design scheme, which includes power security subsystem (PSS) and application special power system (ASPS) is proposed in this paper for power terminal in smart grid. Then, concrete subsystem architectures of the PSS and ASPS are designed and described. The PSS provides data encryption and decryption, identity authentication and other security services while ASPS can improve the efficiency of data parallel execution and processing efficiency of power terminal. Therefore, the proposed architecture design can solve the problem of security and efficiency better for power terminal in smart grid.
Research on Pedestrian Detection Algorithm Based on Deep Learning
Authors: Xu Zhengqin , Zhao Wendong , Peng Laixian , Chen Juan 
Published: October 2020

Abstract
Pedestrian detection is a kind of computer vision technology which is used to judge whether there is a pedestrian in a given image or video and estimate its position accurately. Pedestrian detection technology is widely used in intelligent security monitoring, driverless and robot fields. Pedestrian detection technology has nearly 30 years of development. Although it has greatly improved in accuracy and speed, there are problems such as lighting, pedestrian individual differences, occlusion and multi-scale pedestrian targets in the actual application scenarios, which lead to more missed and false detection in pedestrian detection. The algorithm is not robust enough, which makes the actual deployment difficult.
Comparative Study of Short-term Forecasting Models of Online Car-hailing Demand
Authors: Chang Shiyu , Liu Zhili , Kuang Dongyu , Zhao Hui 
Published: October 2020

Abstract
Based on the time sequence of the online car-hailing data and the change trend characteristics of the online car-hailing data, the short-term prediction of the demand for online car-hailing is carried out. Established the short-term prediction model of the online car-hailing demand, which is based on ARIMA, single feature LSTM and multi feature LSTM respectively. The optimal parameters and structure of the model are determined by using the actual demand data of drip-drip taxi platform, and the comparative experiments are carried out. The experimental results show that the multi feature LSTM model is the best.
Intelligent Pipelining Method and Its Application in C-TSP Problem
Authors: He Dingjia , Peng Hui 
Published: October 2020

Abstract
At present, researches on intelligent optimization problems such as China travel salesman problem (C-TSP) have obtained research results such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Simulated Annealing Algorithm (SA), etc., but the single algorithm also exposes practical problems such as low efficiency and accuracy in the solving process. In order to improve the present situation effectively, this paper proposed an intelligent optimization pipeline method based on the idea of industrial production line flow. This method solved the C-TSP problem by combining the genetic simulated annealing (GA-SA), ant colony optimization simulated annealing (ACO-SA) algorithm pipeline. Experiments show that the efficiency of this method is improved by 4%-10%.
Research on the Teaching Reform of Equipment Guarantee in Special Environment
Authors: Deng Bin , Hu Yamin , Wang Jingjing , Hu Yahui 
Published: October 2020

Abstract
The special topic of equipment guarantee under special environment is one of the special topics in a certain course of a major in the college. Combined with the characteristics of the project, the problems in the teaching of the project are analyzed. In view of the existing problems such as the thin teaching content, the limited teaching resources and the single teaching methods, this paper puts forward the specific teaching reform measures for the special equipment guarantee topic in the special environment from four aspects: reconstructing the teaching content system, improving the teaching methods, enriching the teaching resources and improving the teachers’ teaching ability.
A Text Classification Method Based on the Merge-LSTM-CNN Model
Authors: Wang Kunfu , Zhang Pengyi , Su Jian 
Published: October 2020

Abstract
An MLCNN (Merge-LSTM-CNN) based text classification model combining a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is proposed due to CNN’s deficiency in obtaining context dependency in text and feature loss of the deep neural network in terms of text character extraction. First, the vector representation of input text is realized through word embedding, then full-text semantics are integrated by extracting the local features of the text through a three-layer CNN. Meanwhile, LSTM is used to store the features of historical information in the text to obtain its context-related semantics. Second, input vectors are integrated with the outputs of CNN in each layer respectively to allow the reuse of original features.
A Study on the Three-dimensional Transformation Strategy of Guide Interpretation of the Site of Haihunhou State in Han Dynasty in Nanchang City under the Background of Artificial Intelligence
Authors: Tan Jun , Hu Min 
Published: October 2020

Abstract
With the further development of Artificial Intelligence technology, its influence in all walks of life has gradually increased. AI is playing an irreplaceable role in translation industry. Based on the theory of eco-translatology, this paper will analyze texts of the machine interpretation and human interpretation in Haihunhou Archeological Sites in Nanchang, and discuss how to use the three-dimensional transformation strategy to improve the interpretation quality in the specific interpretation environment.
Computer Network Information Security Protection Based on Virtual Private Network
Authors: Shi Baohui 
Published: October 2020

Abstract
In order to better realize the protection and management of computer network information security, the virtual private network technology introduced has played an important role. This paper summarizes the characteristics and common techniques of virtual private network technology, analyzes the specific application of virtual private network technology in the field of network information security technology, and summarizes the application effect, so as to provide reference for similar research.
RUL Prediction by LSTM Model with Bayesian Parameter Optimization for Turbine Engines
Authors: Hu Shuli , Zhang Shunong , Xu Xing 
Published: October 2020

Abstract
Effectively predicting the remaining useful life (RUL) of a product is significant for reasonable reliability planning and maintenance activities. The long-short term memory (LSTM) model, which belongs to deep learning methods, was applied for RUL prediction of turbine engines, and a parameter optimization method with Bayesian theory was studied.
Research on Traceability Modelling of Tobacco Production Quality Based on Information Synergy
Authors: Li Chun , Ji Daoyuan , Lv Bingyang , Zhang Shun 
Published: October 2020

Abstract
To improve the quality of tobacco production accurate traceability, this paper is based on “Quality Information Resource Pool” formed by tobacco production management information system, constructing a causal traceability model of tobacco production quality, researching and development of traceability algorithm for tobacco production quality based on extended event graph. The quality traceability of raw materials, silk, tobacco and cigarette batches was realized successfully, the real-time and accuracy of quality tracing in tobacco production are improved.
A Study of Computer Aided Translation Based on Artificial Intelligence Technology
Authors: Zheng Saisai , Zhu Shengwen 
Published: October 2020

Abstract
Artificial intelligence technology has been widely used in various fields, and has become the inevitable trend of social development. Based on artificial intelligence technology, this paper studies the computer-aided translation. Firstly, the concept and development of artificial intelligence technology are briefly introduced. Secondly, the function and application of computer-aided translation under the background of artificial intelligence are analysed. Thirdly, the application of artificial intelligence in computer translation is expounded. Finally, the development of computer-aided translation is prospected.
Construction of Vehicle Operating Mode Based on K-Means Algorithm
Authors: Yuan Liping , Li Yang 
Published: October 2020

Abstract
The vehicle driving condition(Driving Cycle), also called the vehicle test cycle, is to describe the speed-time curve of the vehicle driving process, which can reflect many kinematic characteristics of the vehicle’s exercise process on the road. At present, the operating conditions of automobile used in China can not meet the requirements of certification and calibration of automobile products, so it is urgent to develop the test conditions that can reflect the characteristics of automobile exercise in China. Using the method of neural network training to amplify the data, we can accurately fill the missing data in the course of driving find out the principal component of the kinematics segment of the vehicle by using principal component analysis, select some of the most expressive kinematics fragments by using the K-means clustering (K-Means Cluster) analysis method, and construct the model of the Vehicle working condition with little error from the real operating condition, thus effectively reduce the error caused by various environment, traffic, road surface and other external factors.
Design and Application of Special Sensors and Internet of Things (IoT)-based Wireless System for Agricultural Information Monitor
Authors: Li Ganqiong , Li Denghua , Chen Wei , Zhang Yongen , Xu Shiwei 
Published: October 2020

Abstract
Efficient and accurate monitoring of agricultural information is the basis of implementing precision agriculture and improving agricultural productivity. In order to monitor agricultural production information comprehensively, accurately and in real time, an agricultural information monitoring system based on Internet of things (IoT) was developed, which could realize online collection and intelligent management of agricultural information. The system is composed of three layers: perception layer, network layer and application layer. The perception layer consists of various agricultural special sensors, radio-frequency identification (RFID) tag module, ultrahigh frequency RFID read/write device and other measurement modules. The network layer is composed of several ZigBee wireless communication nodes, gateway modules, coordinator modules and remote servers. The application layer includes information application system and user terminal. Sensors were developed and connected by wireless networking technology, and the sensor data was gathered and transmitted to the server terminal in real-time. Mobile terminal system was developed synchronously. The system could be applied to various agricultural scenes, such as field crops, animal husbandry, and has the characteristics of high scalability, intelligence, easy to management and use.
A Modeling Method for Integrated Modular Avionics Dynamic Reconfiguration Process Based on AADL
Authors: Liu Lei , Zhao Wei , Jiang Zeyong , Wang Shihai 
Published: October 2020

Abstract
With the great development of integrated modular avionics (IMA), the dynamic reconfiguration of IMA not only provides great advantages in resource utilization and aircraft configuration but also acts as a valid means for resource failure management. It is very vital to ensure the correction of the IMA dynamic reconfiguration process. The analysis of the dynamic reconfiguration process is a significant task. The Architecture Analysis & Design Language (AADL) is widely used in complicated real-time embedded systems. The language can describe the system configuration and the execution behaviors, such as configuration changes. Petri net is a widely used tool to conduct simulation analysis in many aspects. Therefore, we proposed a modeling method of IMA dynamic reconfiguration based on AADL mode, behavioral annex and error model annex in the system level. The model is our first step to analyze and solve the safety problem of IMA system. It can benefit deep analyzing about dynamic reconfiguration of IMA system as the foundation.
A Intelligent Evaluation Model Framework of Online Public Opinion Crisis in Higher Vocational Colleges
Authors: Ren Yongmei , Li Xuejiao 
Published: October 2020

Abstract
Higher vocational colleges should establish a real-time online public opinion monitoring mechanism. Before deterioration trend occurs, take relevant measures to relieve and guide. The current ability to deal with online public opinion crisis needs to improve. This paper puts forward the principle of the warning index system of the online public in higher vocational colleges, designs a new evaluation index system, builds the intelligent evaluation model framework based on AHP, fuzzy evaluation method and BP neural network which can effectively learn the knowledge and experience of evaluation experts. This model framework can make full use of available data in the big data environment and reduce manpower.
Traffic Light Control System Based on SIMATIC S7-300 and WinCC Configuration Software
Authors: Zhang Chunming , Sun Yiyue 
Published: October 2020

Abstract
The old traffic light control systems are mostly realized by digital circuits. With the development of social economy, traffic lights with digital circuit can not meet the increasing traffic pressure. Therefore, a new method must be sought to replace the complex and unstable control system. Nowadays, PLC technology is developing rapidly, and its applicationgs are becoming more and more extensive. It plays an extremely important role in industrial automation and is widely used in various industries. With the development of science and technology, the function of programmable controller is becoming more and more perfect, coupled with miniaturization, low price, and high reliability, tits role in modern industry has become more prominent.