bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Implementation of Roadside Intelligent Information Interaction System Based on Edge Computing
Authors: Guang Yang , Dongbo Liu , Chaofeng Ma 
Published: April 2020

Abstract
Under the influence of artificial intelligence (AI), Internet of Things (IOT) and big data technology, V2X vehicle infrastructure cooperative technology is rapidly applied to intelligent traffic management and road condition services. However, the existing roadside traffic control equipment has difficulty in data aggregation and processing, that cannot meet the application requirements of the intelligent connection and edge calculation of the V2X vehicle infrastructure cooperative device. In this paper, the “edge computing + end-edge-cloud collaboration” mode is used to construct the roadside intelligent information interaction system. As an intelligent connection node between roadside traffic management system and V2X vehicle infrastructure cooperative system, the design makes roadside video analysis and real-time data fusion and push function come true. Because of its lightweight intersection deployment mode, regional-level road condition diagnosis and optimization control capabilities, it has broad application prospects.
The Trajectory Generation of UCAV Evading Missiles Based on Neural Networks
Authors: Zhang Hongpeng , Huang Changqiang , Zhang Zhuoran , Wang Xiaofei , Han Bo , Wei Zhenglei , Li Yingtong , Wang Le , Zhu Wenqiang 
Published: April 2020

Abstract
In order to solve the problem of evading air-to-air missiles of UCAV in autonomous air combat, the flight dynamics model and 3-dimensional guidance trajectory model based on proportional guidance were established and performance constraint conditions of missiles were constructed. According to the definition of basic maneuver library, 72 kinds of avoidance maneuvers were constructed. All 72 avoidance maneuvers were simulated while UCAV was at different relative yaw angles and relative pitch angles, and the optimal avoidance maneuvers under the corresponding conditions were selected out. Training samples and test samples were constructed by utilizing the acquired data, and the neural network for generating control parameters was trained. Under different conditions, neural network method and random selection method were simulated. The results show that the success rate of escape of the proposed method is higher and the time cost of generating the control parameters is less, which meets the requirements of effectiveness and real-time.
Research on Seasonal Prediction of PM2.5 Based on PCA - BP Neural Network
Authors: Zhang Yi-wen , Yuan Hong-wu , Wu Hai-long , Sun Xin , Dong Yun-chun 
Published: April 2020

Abstract
PM2.5 is a kind of data with strong time-series characteristics, so the current PM2.5 prediction methods mostly choose RNN, LSTM and other sequence models for prediction, but because RNN, LSTM and other models use the same weight to calculate the data input at different times, which does not conform to the brain-like design, resulting in a low accuracy of PM2.5 concentration prediction. In view of the above problems, a PM2.5 prediction method based on attention mechanism (at-rnn and at-lstm) is proposed. This method firstly establishes the encoder-decoder model based on deep learning. In the Encoder stage, attention mechanism is added, and attention weight is allocated to the input with time series characteristics, and then Decoder analysis and prediction are carried out. Through experiments, the effects of RNN, LSTM, at-rnn and at-lstm on the prediction of PM2.5 concentration in hefei are compared. The results show that the accuracy of the prediction method based on attention model is better than other methods, indicating the effectiveness of the prediction method based on attention model in the prediction of pollutants.
A Method for Improving Bandwidth of Multimode Optical Fiber Network
Authors: He Kun , Guo RiCheng , Cui Can Wang , Feng Guo Kai , Ying Wang 
Published: April 2020

Abstract
A high-speed data exchange network based on multimode fiber and RapidIO technology is widely used in existing projects. It consists of a fiber switch and a high-speed interface module. The fiber switch is used as the data exchange core to complete protocol conversion. The high-speed interface module is connected to the universal device node and is responsible for access conversion of the RapidIO bus between the optical network. The hardware design utilizes the commercial shelf architecture, the software design adopts a multi-buffer and multi-thread data exchange method to increase the entire network bandwidth from 300MB/s to 800MB/S.
Pig target detection method based on SSD convolution network
Authors: Deng Minghui , Yu Ran 
Published: April 2020

Abstract
The behavioral changes of pigs can not only reflect their own health and welfare status, but also have important significance in improving the breeding environment. Rapid and accurate detection of pigs’ targets is the basis of behavioral change analysis.In view of this situation, a target detection algorithm based on deep learning is proposed, and its model structure and working principle are expounded. Then, through the test of pig image data set, the results show that this method can accurately detect pig targets under complex background.
Bi-objective Optimization of Black Start Allocation for Regional Power Grid
Authors: Cheng Xue , Zhao Yanjun , Long Fei , Wang Qian , Wang Hongtao 
Published: April 2020

Abstract
Under the influence of extreme weather events, black start allocation is more and more important for regional power grids lack of hydroelectric units and pumped-storage aggregates. This paper proposes a bi-objective optimization model considering both the time of the whole restoration and the risk of line recovery, an improved TOPSIS method is employed to solve the problem based the non-inferior solution. The results of the Dongguan power grid verify the effectiveness of this method.
An Improved B-A Model for Scale-free Network Evolution Model
Authors: Wei Hongyan , Wang Jiangong , Wang Tianqi 
Published: April 2020

Abstract
Scale free network can describe complex network very well. Barabasi and Albert proposed B-A model, which reveals the nature of many complex phenomena in the real world. The generated network degree distribution satisfies power-law distribution. In this paper, two shortcomings of the model are found through careful study, and an improved scale-free network evolution model is proposed. The experimental data show that the improved model has significant scale-free characteristics and can better reflect some characteristics of complex networks.
Crowd Counting via Multi-Column Deconvolution Networks
Authors: Jiang Jibing , Bai Mengxuan , Liu Bo , Zhou Guihong 
Published: April 2020

Abstract
The difference in distance between the crowd and the camera results in different scales of the population in the image. In the study of crowd counting, this paper first proposed the method of adding deconvolution to multi-column network for the problem that the population size in the image does not affect the accuracy of counting, namely multi-column deconvolution neural network (Mutil-Column Deconvolutinal Neural Network, MCDNN). Compared with the results of the Multi-Column Convolutional Neural Network (MCNN) on the ShanghaiTech dataset, the MSE is reduced by 42.8 and the MAE is reduced by 26.6. On the UCF_CC_50 dataset, the MSE is reduced by 109.4 and the MAE is reduced. 86.3. Compared with other advanced methods, the proposed MCDNN network achieves better performance on the data set, effectively solves the problem of inaccurate population counting accuracy due to scale changes, and can generate a better quality density map.
The Research and ImplementationOf The Application Technology Of Domestic Basic Software In The Security Information System
Authors: Chen Hongyan , Wang Sihong , Wan Junwei , Li Zhen 
Published: April 2020

Abstract
This article aims at the typical application scenarios of high-density integrated application information systems. It investigates, sorts out, and analyzes the specific application requirements of domestic software such as middleware, databases, operating systems, and office software. According to the characteristics of the high-density integrated application information system, a set of system architecture based on domestic basic software is constructed, the feasibility of the domestic basic software applied to the high-density integrated application information system is analyzed, and its application in the high-density integrated application information system Models and solutions for different application scenarios (reporting, information services, information sharing, collaborative office, etc.).
Application of Information Technology in Construction Machinery Management
Authors: He Yizhou 
Published: April 2020

Abstract
Construction machinery is a key factor in the construction of construction projects, which is of great significance for ensuring the quality and schedule of construction projects. Many problems have arisen in the current management of construction equipment, which have a negative impact on the reliability and sustainability of construction equipment. Emphasizing the quality of construction equipment management work and optimizing the application of information technology in construction machinery management can effectively improve the continuity of machinery equipment operation. This article will systematically discuss the application research ideas of information technology in the management of construction machinery, and put forward related suggestions based on the actual situation.
Wafer SEM Image Generation with Conditional Generative Adversarial Network
Authors: Du Huqiang , Shi Zheng 
Published: April 2020

Abstract
Scanning Electron Microscope (SEM) images play an essential role in the analysis and evaluation of the defects of the circuit in advanced integrated circuit manufacturing. Currently, the image generation method is a useful means to solve the insufficiency of wafer SEM images due to the high cost of getting a large number of labeled SEM images. In this paper, an algorithm based on conditional generative adversarial network (cGANs) is proposed for SEM image generation. Firstly, a Sobel operator is used to calculate the gradient information of images to guide the discriminator. Then we apply two discriminators to discriminate images of different resolutions. Finally, Wasserstein distance and smooth L1 loss functions are applied to accelerate network convergence. Experimental results show that this method could learn to mimic the distribution of wafer SEM image data effectively. Compared with other image generating models, our model improves the quality of the generated image, and its 1-Nearest-Neighbour (1-NN) classification score is increased by 0.3. Therefore, this algorithm is more suitable for generating images to alleviate the shortage of wafer SEM images.
Analysing the efficiency and robustness of gossip in different propagation processes with simulations
Authors: Yang Ruiqi 
Published: April 2020

Abstract
This paper uses the probability theory to analyse the ultimate consistency of the gossip protocol propagation process by dividing it into two phases. The time complexity and information complexity of the flooding, tree and gossip methods are compared by simulation with different variants. Also, three ways of communication between nodes in the network are introduced: only-push, only-pull, and push-and-pull. The paper analysed the simulated results of variable nodes using those three ways and discussed their corresponding time and information complexity. The simulations and analysis compared performances of information transmission under different transmission loss rates and concluded that the push-and-pull has an effect on transmission structure robustness. Finally, an effective method of information transmission was proposed to deal with transmission loss.
An analysis on the current researches in encryption algorithms based on neural networks
Authors: Ouyang Jin 
Published: April 2020

Abstract
To perform an in-depth analysis on the current researches concerning the application of neural networks in the encryption algorithms, this paper used some research methods such as literature review and algorithm analysis to review the proposal and application of neural network technologies as well as the proposal and research and development of encryption algorithms through. The importance of neural networks in the encryption algorithms is highlighted. The current researches on encryption algorithms based on neural networks are reviewed from three dimensions, namely, algorithm framework, algorithm model and algorithm application. Through the research, it shows that the neural network technology has become the preferred choice for developing the encryption algorithms. This paper also proposed the direction of improvement through fusion application of multiple information processing technologies. This work lays the basis for improving the encryption algorithms based on neural network.
Analysis of and Reflections over the Development of New Energy Automobile and Intelligent Manufacturing
Authors: Yayu Qiu 
Published: April 2020

Abstract
With the rapid socio-economic development and technological upgrading, various advanced technological achievements have emerged. Nowadays, people are paying more attention to a low-carbon environment, especially the new energy, when faced with energy shortages and serious environmental pollution. It is known to us all that cars can cause serious environmental pollution. That’s why great attention has been paid to the development and application of new energy vehicles, which can not only save energy and protect the environment, but also meet people’s basic needs. The Chinese government has given strong support to new energy vehicles in terms of funding, technologies, policies, and incentive measures. At present, new energy vehicles are in the stage of development and manufacturing. Its industrial chain has been formed and is now moving towards intelligent manufacturing, with various supporting units progressing rapidly. This paper focuses on the issues related to the development of new energy vehicles and intelligent manufacturing.
An Enhanced Nonlinear Hybrid Precoding Algorithm for Millimeter Wave Massive MIMO Systems
Authors: Liu Fulai , Zhang Yanshuo , Du Ruiyan 
Published: April 2020

Abstract
Hybrid precoding has recently received considerable attentions in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Especially, a nonlinear hybrid Tomlinson-Harashima precoding (THP) algorithm has recently received considerable attentions since it can effectively improve the performance of the hybrid precoding. This paper presents an enhanced nonlinear hybrid Tomlinson-Harashima precoding algorithm based on the column-by-column refinement (named as E-THP-CR algorithm). Compared with the previous works, the proposed algorithm offers a numberof advantages.The advantageof the proposed algorithm lies in achieving the column-by-column refinement of the analog precoding matrix. Afterwards, the digital precoding matrix is decomposed into two separate sections which respectively belong to the encoding part and effective channel part. This design can effectively remove the inter-user interference as expected in the THP module. Simulation results verify that the proposed algorithm provides higher spectral efficiency, compared with the other relevant algorithms.
Ordering of matching energy and Hosoya index of tricyclic graph
Authors: Suo Langwangqing , Tian Shuangliang , Yang Qing 
Published: April 2020

Abstract
The matching energy of a graph G is defined as the sum of the absolute values of thezeros of the matching polynomial of G. The Hosoya index of a graph G is defined as the total number of matchingsof G. In this paper, the matching energy and Hosoya index of the a special class of tricyclic graphs G(m
1, m
2, m
3) were investigated, and orderings of tricyclic graphs G(m
1, m
2, m
3) with respect to matching energy and Hosoya index are obtained.
Design of Traffic Coordination Control Structure Based on Multi-Agent
Authors: Yong Huang , Li Gan 
Published: April 2020

Abstract
Urban transport, one of the city’s important infrastructures, which plays an important role in the normal functioning of the city, has become the focus of attention and discussion in the whole society. Restricting to the sustainable development of urban economy, the traditional solution to the urban traffic problems can no longer meet the development requirements of existing urban traffic. Given the complexity of urban transport, the limitations of traditional traffic control methods and the popularization and application of mature artificial intelligence technology, this paper discusses the collaborative framework of traffic control and induction on the base of multi-Agent technology and make researches on the structural design of urban traffic coordination control, thus builds a simulation model for the single intersection. The simulation experiment was carried out by using the Q learning algorithm.
Intelligent Deformation Control Algorithms for Mechanical Metal Parts Based on Internet of Things
Authors: Li Bohua 
Published: April 2020

Abstract
In order to solve the problem of serious damage during the operation of mechanical metal parts, the intelligent control algorithm of deformation of mechanical metal parts is studied based on the Internet of Things technology. Firstly, the characteristic parameters of deformation of metal parts are collected by the fuzzy algorithm, and the extreme value of damage degree of parts is evaluated and calculated by the artificial ant colony algorithm. According to the calculation results, the deviation parameters of the moving trajectory of the mechanical metal parts are corrected to alleviate the damage of the metal parts, so as to realize the intelligent control algorithm for the deformation of the mechanical metal parts. Finally, the experiment proves that the intelligent deformation control algorithm based on the Internet of Things has better practical value than the traditional algorithm.
A mobile robot path planning algorithm based on improved A*
Authors: Shi Jinzheng , Su Yifan , Bu Chunguang , Fan Xiaoliang 
Published: April 2020

Abstract
Global path planning of mobile robot aims to provide a safe and smooth path for mobile robot navigation. Traditional A * algorithm is planning the path of more turns, and not smooth. Moreover, for the u-shaped terrain, the path stick to the obstacles. Aiming at the shortcomings of A* algorithm, cosine distance is selected as the heuristic function, and direction information is added and normalized. The 36-order neighborhood search matrix is selected to solve the problem of fitting u-bend. In addition, a post-processing method based on Bessel curve is proposed. Make the planned path curvature change continuously. Simulation results show the effectiveness of the improved algorithm.
Real-time Online Fault Diagnosis of Rolling Bearings Based on KNN Algorithm
Authors: Wang Haiya , Yu Zhongqing , Guo Lu 
Published: April 2020

Abstract
In order to realize the predictive maintenance of rolling bearings in industry, this paper proposes a real-time online fault diagnosis method for rolling bearings based on KNN algorithm. The method mainly includes two steps: fault diagnosis model training and real-time online fault diagnosis. Firstly, the vibration signal is preprocessed: data classification, data cleaning, data segmentation and feature parameter extraction, and then training and optimizing the fault diagnosis model. In the real-time online fault diagnosis part of the rolling bearing, the real-time online extraction of the characteristic parameters of the vibration signal is used to realize real-time online fault diagnosis through the fault diagnosis model. The results show that the fault diagnosis model based on KNN algorithm is better than the fault diagnosis model based on C4.5 algorithm and CART algorithm, which is more suitable for fault diagnosis of rolling bearings. Using this method to diagnose rolling bearings can help predictive maintenance before rolling bearing failures and reduce the economic losses caused by unplanned downtime of critical equipment.
RETRACTED: Building Logistics Block Chain Platform Based on Cloud Computing
Authors: Chen Huihong , Liu Shiming 
Published: April 2020

Abstract
With the rapid development of the new generation of information technology and e-commerce, the modern logistics industry chain has emerged. The logistics blockchain platform enables all kinds of users to participate in the whole information transmission process together. It establishes the logistics blockchain cloud platform deployment jointly participated by the government, enterprises, schools, institutions and communities. According to the practice, different blockchain deployment modes are adopted, A new multi agency data sharing framework is designed to promote the application of blockchain in the logistics industry.
Target part detection based on improved SSD algorithm
Authors: Yu Yongwei , Han Xin , Du Liuqing 
Published: April 2020

Abstract
Aiming at the problem that the traditional target detection algorithm cannot balance the target detection accuracy and real-time detection, and the detection effect is poor under the actual complex production conditions. A deep learning detection method based on Inception-SSD framework is proposed. The Inception-SSD, which integrates the Inception prediction structure, introduces the Inception block into the extra layer of the SSD network. Then it uses batch normalization (BN) and residual structure connection to capture more target information without increasing the complexity of the network. It will improve its accuracy without affecting its real-time detection. The result of the experiment show that the detection accuracy of this model is 97.8% in the actual production process, which is largely improved comparing with the original SSD algorithm. The detection time is 48ms, which improves the detection accuracy and ensures the real-time performance. It can meet the detection requirements of parts in actual production.
An algorithm based on the dichotomy for quickly identifying whether a sequence is a PAPR sequence
Authors: Yang Yaoqi , Zhang Lei , Guo Xiao , Xu Renhui 
Published: April 2020

Abstract
With the development of communication technology, OFDM (Orthogonal Frequency Division Multiplexing) technology has become one of the core technologies in the field of wireless communication. Although OFDM has many advantages, it has a problem that the ratio of peak-to-average ratio is too large, that is, the PAPR problem. Despite the achievements in the technical means of suppressing PAPR (Peak to Average Power Ratio), the implementation of new technologies often requires the introduction of excessive IFFT calculations. Based on this situation, this paper proposes a new algorithm based on binary search to solve this problem.