bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on BOM multi-view mapping method orient to aircraft in-service data management
Authors: Cai Shuyu , Hu Die , Fei Yang , Xu Jianxin 
Published: June 2020

Abstract
In order to establish the framework for aircraft life-cycle data management and ensure the efficient implementation of maintenance information management of in-service aircraft, the characteristics of maintenance information management of civil aircraft in service were analyzed. BOM views were classified, and a BOM multi-view model was constructed. By analyzing the main mapping forms of BOM multi-views, the node types of BOM multi-view were defined, the mapping rules of BOM multi-view model were proposed, and the mapping algorithm of BOM multi-view model was designed and implemented. Finally, method validation was performed by the BOM mapping of 48-section torsion box of a certain aircraft. The results show that, the mapping of EBOM to M-BOM is realized effectively, ensuring the full integration of the information management structure in the in-service maintenance phase and the development phase, and ensuring the consistency of the aircraft life-cycle data management framework.
Estimate the number of triangles in real-world graph streams
Authors: Liu Changyu , Zhang Xin 
Published: June 2020

Abstract
Estimating the number of triangles in the graph streams is the basis of data mining, which aims to design an efficient graph stream algorithm to estimate the number of triangles in graph. Real-world graph is a multi-layer graph encompassing multiple distinct types of connectivity. The state-of-the-art approaches that counting triangles mainly focus on a general graph and cannot be applied for multi-layer graph, since duplicated edges across different layers exist. In this paper, we give the concept of several triads and triangles under the multilayer network, which truly reflect the real-world network topology. And we design a new two-stage sample algorithm based on reservoir sampling and triad sampling under real-world graph streams which solve the problem of more data brought by multi-layer networks. The algorithm is also a one-pass algorithm, and it can calculate the number of all types of triangles at the same time. We analyze the expectation and variance of the estimations and show that the algorithm is unbiased and stable. Our experimental results demonstrate that algorithm has good time efficiency and accuracy.
Design of UHF RFID Baseband Chip with Temperature Sensing
Authors: Chu Qi , Xiao Wan’ang , Gao Wanlin , Zhang Yan’e 
Published: June 2020

Abstract
The radio frequency identification (RFID) with temperature sensor can achieve accurate management and body temperature measurement in animal husbandry. It also can improve the efficiency and epidemic prevention. A ultra high frequency (UHF) RFID chip baseband integrated with a specific temperature sensor is designed compliant with “Information Technology Radio Frequency Identification 800/900MHz Air Interface Standard” (GB/T 29768). Firstly, according to the overall function, the unit division and structure design of digital baseband are given. Secondly, a baseband temperature sensor control unit connected with temperature sensor is proposed. Finally, the whole function simulation and experiments on FPGA are completed. The experimental results show that the designed UHF RFID baseband can satisfy the communication of both sides and realize the interaction with the temperature sensor.
An Image Denoising Method Based on Deep Residual GAN
Authors: Wang Ziyuan , Wang Lidan , Duan Shukai , Li Yunfei 
Published: June 2020

Abstract
As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects.
Compressive X-ray Tomosynthesis based on Exposure Sequence Optimization
Authors: Dong Wang , Xu Ma , Qile Zhao , Hao Xu , Tingfa Xu 
Published: June 2020

Abstract
X-ray tomosynthesis technique can reconstruct three-dimensional object of interest from two-dimensional projection measurements. This paper proposes a compressive X-ray tomosynthesis imaging method to inspect the moving object on the conveyor. Coding masks are used to modulate the intensity distribution of X-ray sources and reduce the radiation dose. Low-cost uniform linear array detector is used to collect measurement data. Considering the rigid motion of the object, the proposed method uses a dynamic imaging model and a reconstruction framework based on the variation of the sensing geometry. Subsequently, an optimization method is proposed to design the exposure sequence of X-ray sources based on the genetic algorithm. Optimizing the exposure sequence can ameliorate the condition of the ill-posed inverse reconstruction problem. Simulations show that the optimized exposure sequence can effectively improve the reconstruction performance of the compressive X-ray tomosynthesis.
Application analysis of image recognition based on multiple intelligence algorithm
Authors: Jiang Jiyue , Wu Hao , Cui Yang , Cai Yijia , Yu Chenglong , Zhong Ruitao 
Published: June 2020

Abstract
Image recognition technology based on artificial intelligence algorithm has entered a rapid development stage. According to the development and research status at home and abroad, we can see that PCA, ANN, SVN and other technologies can effectively realize the recognition and generation of images, especially the extraction and prediction of face features, classification and recognition. The analysis and application of the generation countermeasure network (GAN) technology is mainly embodied in the realization of the generation and analysis of handwritten numbers, and the accurate prediction of the promotion and application of SVM technology, as well as the comprehensive and accurate analysis of the generation effect. This paper mainly studies the comparative analysis and application of human face database (ORL) through the relevant data. By extracting 50 face samples, 500 face images are produced for recognition analysis and application.
Research on a convolution operation method based on domain transformation in deep learning
Authors: Wang Zhichen , Li Hongliang 
Published: June 2020

Abstract
In deep learning, convolution operation is an important part of convolutional neural network. Various transformations have been used to deal with convolution problems, but complex number operation and the inability to carry out convolution in the DCT domain have been introduced. In this paper, the relationship between DCT transform of output matrix and DCT transform of input matrix is found. At the same time, in view of the reality of partitioning operation in the case of large matrix, the method of overlapping of blocks is introduced. Finally, experiments on LeNet show that the proposed method can achieve the accuracy of the traditional method and can be used in practical applications.
Multi-cognitive network resource allocation based on improved artificial bee colony algorithm
Authors: Li Lu , Zhang Luyong 
Published: June 2020

Abstract
This paper introduces a graph theory model for resource allocation in multi-cognitive wireless network scenarios. Aiming at the problems of low search accuracy and slow convergence speed of the basic artificial bee colony algorithm, an improved bee colony algorithm is proposed. The improved algorithm introduces an adaptive t-distribution mutation strategy. Compared with the original algorithm, the performance of the improved algorithm has greatly improved. At the same time, the improved bee colony algorithm is applied to multi-cognitive wireless network scenarios for resource allocation in the model. The experimental results show that the improved artificial bee colony algorithm can obtain greater system throughput and effectively reduce the performance of multi-cognitive wireless network systems, and can allocate resources more reasonably.
RETRACTED: Construction of Interactive Integrated Communication Marketing System in Big Data Era
Authors: Tang Qi 
Published: June 2020

Abstract
In the era of big data, the interactive communication and marketing framework integrated with massive data and information technology systems is becoming increasingly sophisticated. From the perspective of the overall architecture, to construct an interactive integrated communication marketing system, we must actively expand the big data communication marketing platform and do a good job of analyzing consumer consumption orientation. We need to fully optimize the interactive integrated communication marketing strategy, and do a good job of feedback of marketing results, so as to comprehensively improve marketing profits. Generally speaking, the core of integrated communication marketing is to take the consumer as the guide, make full use of various forms of communication, promote the organic combination of internal communication and external communication, optimize the marketing strategy management mechanism, and ensure long-term marketing effectiveness. This article will briefly discuss the construction plan of the interactive integrated communication marketing system in the era of big data, hoping to provide reference and reference for network marketing management.
Research on Inquiry Teaching of Database Course Based on Big Data
Authors: Mengxia Wang 
Published: June 2020

Abstract
Nowadays, China’s social economy has achieved rapid development compared with the past, and has achieved remarkable achievements. At present, the entire society has entered the era of big data. Based on the background of big data, this article has conducted a full study on how to determine the teaching content and experimental content of database courses. In combination with the actual teaching situation, an inquiry-based teaching method was formulated, and the problems existing in the database classroom teaching were analyzed and effective solutions were put forward. At the same time, it can help students better organize, manage, and store big data, do a good job of technology development, and promote a significant improvement in the comprehensive literacy of software talents.
Research on Life Prediction of IGBT Devices Based on Elman Neural Network Model
Authors: Xing Chao , Xi Xinze , He Xin , Li Shengnan , Liu Mingqun , Li Lie 
Published: June 2020

Abstract
IGBT, as a new generation of composite full-controlled voltage-driven power semiconductor devices, has been widely used in the field of modern power electronics. The study of IGBT device life prediction has important guiding significance for the stable operation and reliability management of power system. In this paper, the Cofin-Manson-Arrhenius extended exponential model based on thermal load is analyzed, and the model parameters are trained and modified by the Elman neural network in order to improve the prediction accuracy of life prediction model. The Coffin-Manson-Arrhenius extension exponential model and the new improved model are simulated and validated by experiments. The results are compared with the actual life of IGBT. It is concluded that the Coffin-Manson-Arrhenius extension exponential model based on Elman neural network has higher accuracy in life prediction than the Coffin-Manson-Arrhenius extension exponential model.
Named Entity Recognition Method of Brazilian Legal Text based on pre-training model
Authors: Wang Zhili , Wu Yufan , Lei Pengbin , Peng Cheng 
Published: June 2020

Abstract
Named entity recognition (NER) is a common task in Natural Language Processing (NLP). To this end, we propose a novel approach based on pre-training model to complete the sequence labeling tasks by learning the large-scale real-world data from Brazilian legal documents. Especially, combining iterated dilated convolution[1] (IDCNN) and Bi-LSTM, we develop the scalable sequence labeling model named Sequence Tagging Model (STM) and extensive experiments validate the effectiveness of STM for NER tasks. Furthermore, compared with the IDCNN-CRF model, the experimental results show that the STM is better and the F1 score is 93.23%, which provides an important basis for NER tasks.
MMV-based OMP for DOA estimation with 1-bit measurement
Authors: Chen Dan , Tian Longwei , Xu Changqing 
Published: June 2020

Abstract
This paper shows a novel scheme for direction-of-arrival (DOA) estimation of narrowband signals. In the scheme, the 1-bit measurement is used to reduce the system cost. A recovery compressed sensing (CS)-based algorithm with multiple measurement vector (MMV) is proposed to make up the information loss. The purposed MMV-OMP algorithm provides a new idea which make the compress sensing framework applicable directly to an MMV model. The effectiveness of the proposed approach is assessed through numerical simulations under different environment configurations. Comparisons with other methods are reported as well.
An Evaluation Method of Combined Forces Electronic Countermeasure Capability based on Grey Relational Analysis
Authors: Fan Ji-song , Xu Jia-wei , Ren Hui 
Published: June 2020

Abstract
Electronic warfare in battlefield is a dynamic game process with incomplete information, in which there are not only complex technical factors, but also a lot of uncertain factors, fuzzy factors or human factors, so it is difficult to model and evaluate its capability. In this paper, the index system of the electronic countermeasure capability of the synthetic army is established, and a method of multi-level grey relational analysis and AHP is used to analyse and evaluate the electronic countermeasure capability of the synthetic army. A complete set of evaluation method steps is put forward and verified by an example. Compared with the past models and methods, they are easy to operate, applicable and scientific in conclusion, which is convenient to analyse and evaluate the electronic countermeasures capability of multiple units. It is of great theoretical significance and practical value for the development of electronic countermeasure capability construction and scientific assessment and evaluation of synthetic forces.
Waste recycling 3D printing and silk-making system
Authors: Huang Ruiting , Yao Qingren , Liu Yi 
Published: June 2020

Abstract
With the continuous development of 3D printing technology, 3D printing has been widely used in various industries. 3D printing has more and more consumables, more and more wastes are generated, and more and more pollution is generated to our environment. This system is based on the single-chip microcomputer, and uses the fuzzy PID algorithm to melt, shape and wire various types of 3D printing consumables at different temperatures. It can recycle 3D printing waste and directly use consumables for printing to reduce environmental pollution and printing cost.
Design of 1Gsps high-speed data acquisition card
Authors: Sun Xiunan , Liu Ming , Liang Hao 
Published: June 2020

Abstract
In order to digitize the High-Frequency signal, a 12-bit 1Gsps RF Sampling data acquisition system is designed. The digital acquisition system includes an Analog-to-Digital Converter (ADC) daughter card and a data processing mother card equipped with Field Programmable Gate Arrays (FPGA) for communication, which has an external DDR3 memory module and a Gigabit Ethernet Interface. It focuses on the hardware system implementation of the daughter card and the realization of data receiving, transferring, and sending based on the FPGA. The test results show that the Effective Number of Bit (ENOB) of the acquisition card is above 9.1 Bits. Both Integral Non-Linear (INL) and Differential Non-Linearity (DNL) are anticipated. It accomplishes the requirements for engineering applications in many fields excellently.
Research on Acoustic Model of Multi-Task Learning for Speech Recognition
Authors: Gong Chunwei , Li Guanyu , Luo Lisai 
Published: June 2020

Abstract
Applying the idea of multi-task learning, this paper proposes a speech recognition acoustic model structure, which can share information between multiple tasks to improve the performance of each task. Applying this structure to the acoustic model modeling of resource-poor languages has achieved good recognition results.
Activity Recognition using 1D convolution from Accelerometers Data
Authors: Wang Jian , Hua liu Xue 
Published: June 2020

Abstract
Aiming at the problem of activity a recognition method based on a convolutional neural network was proposed in this papaer, which can effectively classify 6 types of human movements: Downstaris, Jogging, Sitting, Standing, Upstairs and Working. The network consists of an input layer, two convolutional layers, two pooling layers, a fully connected layer, and an output layer. The sliding window is used to transform the sensor data into a three-channel RGB image format, and the features of the three-axis speed sensor data are automatically extracted to classify each action. The model was reproduced using Tensorflow, and the recognition rate of 89.35% was achieved on the open source database WISDM. The experimental results show that the amplifier has a better effect on human movement recognition.
Chinese long text summarization using improved sequence-to-sequence lstm
Authors: Yao Zanjie , Chen Aixiang , Xie Han 
Published: June 2020

Abstract
Text summarization is an important issue in natural language processing. The existing method has the problem of low accuracy when performing long text summarization. In this paper, We use the LSTM to construct the sequence-to-sequence model, and combine the attention mechanism to process automatic Chinese long text summarization.The experimental results indicate that our method can accurately extract key information from long text, generate high-quality summary.
Joint Delay Reduction and Power of Device for Differentiated Data Transmission in D2D Network
Authors: Song Lan 
Published: June 2020

Abstract
In wireless communication, Device-to-Device (D2D) networks will become more and more common with the popularity of mobile terminals. However, in a D2D network, the processing capabilities of different terminals may be different, and the energy of different terminals is different due to battery life. In order to improve the quality of experience (QoE) of terminals with different processing capabilities and energy, and realize the transmission of differentiated data streams, this paper combines the existing base station retransmission and terminal retransmission algorithms in D2D network. In the transmission process, the data packet is divided into a basic layer and several enhancement layers according to the definition of the data. Based on the link quality, the basic layer data packet is transmitted firstly, then the enhancement layer data packet is transmitted. Moreover, not only the base station retransmits the data packet, but also the terminal that has received the corresponding data packet also participates in the retransmission. In order to extend the average life of the network, we propose the maximum weight packet selection algorithm based on channel quality and terminal energy (MWP-QE). The simulation results show that compared with the existing retransmission algorithm, this algorithm can realize the transmission of differentiated data, reduce the retransmission delay and extend the life of the network.

A post-publication change was made to this article on 17 Jun 2020 to amend the author list.
Information Retrieval Method for Mixed Intrusion of Surveillance Network Based on Big Data
Authors: Zhu YuLin , Meng QingChuan , Hu DaYong , Lu Ziyuan , Li ZhiGang 
Published: June 2020

Abstract
The current surveillance network intrusion information retrieval methods have problems such as low retrieval accuracy, high recall rate, and long retrieval time. Therefore, this paper proposes a hybrid intrusion information retrieval method for monitoring networks based on big data. The genetic algorithm is used to optimize the feature set, and the partial F test is introduced. The optimal subset is selected to form the optimal feature set and the redundant information elimination model is constructed to eliminate redundant information in the mixed intrusion information. Based on the information retrieval theory, the LDA model is used to build the feature set, and model the subject of the document, establish an intrusion information retrieval model, then complete the hybrid network intrusion information retrieval of the monitoring network under big data. The results show that the method proposed in this paper has higher retrieval accuracy, can effectively improve the retrieval efficiency of intrusion information and the average recall rate is about 24%, which is better than other methods.
An Empirical Study of Observation-weighting Method for Mining Actionable Behavioral Rules
Authors: Su Peng , Wang Yufeng 
Published: June 2020

Abstract
Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among a variety types of actionable knowledge, the actionable behavioral rule plays an important and unique role because it can directly and explicitly suggest actions to users to positively influence their behaviors of concerned entities. The problem of mining such rules is a search problem within a framework of support and expected utility. The previous mining approach assumes that each instance which supports a rule has the uniform contribution to the support. However, this assumption is usually violated in practice, and thus will hinder the performance of algorithms for mining such rules. To handle this problem, in this paper, we propose several observation-weighting models for support based on different functions. We further empirically investigate these models. Based on the results of our experimental study, we gain a thorough insight into the selection of various observation-weighting models for this domain.
Modified Minimum Approach Distance Formula for Five Working Used for 500kV Insulated Aerial Vehicles with Double Circuits on the Same Tower
Authors: Chao Chen , Zhenyu Ma , Leijiao Ge , Binbin Wen , Yipin Xu , Jinhui Zhou 
Published: June 2020

Abstract
500kV double circuits on the same tower can effectively improve the natural transmission power and reduce the width of the line corridor, but the electromagnetic environment under the conductor is deteriorated. In order to ensure the safety of the workers in the insulated aerial vehicles and equipment during the live working, this paper firstly calculates the electric field distribution on the operating surface of the 500kV double-circuits truck with the actual line parameters, and then defines the distance coefficient. The existing minimum safe distance formula is modified, and a reference calculation method of the distance coefficient is given in combination with the data in the paper. Finally, the simulation data is used to verify.