bluuebunny/crossref_metadata_2025 huggingface dataset:
Recognition of Fusion Processing Based on Infrared and Visible Images under the Framework of Semi-supervised Learning
Authors: Cao Zhuohang , Bian Shuhao , Chen Zhe , Ma Peili , Zhai Shuaishuai , Xu Lijun
Published: June 2020

In traditional convolutional networks, due to the lack of adequate information protection of traditional image fusion technology and the incomplete removal of redundant noise, the useful information of the fusion image is missing and the recognition success rate is low. In this paper, through the research of deep learning-based image fusion methods and traditional target recognition and SVM neural network, an image fusion processing recognition method based on infrared and visible light is designed. The coding network of this image fusion method consists of convolutional layers, fusion layers and dense blocks. The output of each layer needs to be connected to the next few layers by using a densely connected neural network, so as to obtain more useful features from the source image and fuse the data of the two images better. It is verified by simulation that the fused image has sound visual effects, and its edges and details have been completely preserved. Thus, the target object has strong recognizability compared with the surrounding environment. Research shows that this method will help to more accurately interpret target information in complex environments and achieve more effective results in target recognition.
Dangerous Behavior Recognition of Autonomous Vehicles at Intersection Based on Gaussian Mixture Model
Authors: Li Shaopeng , Wu Shaobin
Published: June 2020

Urban road intersections are a typical complex traffic scene, and they are also prone to road traffic accidents. Aimed at the safety problems of road intersections, this paper proposes a model of vehicle dangerous behavior recognition based on Gaussian mixture model method, which provides an empirical basis for autonomous vehicle behavior decision at intersections. By analyzing the characteristics and rules of driving behavior at intersections, we select parameters like PET as input parameters of the model, and then construct a simulation model based on Beijing’s Weigongcun intersection, which is used to obtain the dataset in intersection scene. The obtained data was subject to Kalman filtering and normalized as data input of a Gaussian mixture model. Dangerous scenes and general scenes at the intersection scene were screened out. The screening results were analyzed and evaluated, and proved to be reliable. The model could provide an empirical basis for the decision-making design of autonomous vehicles at intersection.
Hybrid Lion Swarm Optimization Algorithm for Solving Traveling Salesman Problem
Authors: Yang Yan , Liu Shengjian , Luo Lin
Published: June 2020

In view of Traveling Salesman Problem (TSP) is a NP hard problem, Lion Swarm Optimization (LSO) Algorithm is a new swarm intelligence optimization algorithm. Therefore, in this paper, it uses improved hybrid LSO algorithm to solve TSP. Because the basic LSO algorithm is mainly used to solve the continuous problem, and the TSP is a discrete combinatorial optimization problem, this paper improves the LSO algorithm, introduces the greedy strategy, and combines with the Population Competitive Search (PCS) algorithm in the later stage of the search. As a result, the convergence speed is accelerated and the search efficiency is improved. The classic TSP example is tested, and the simulation results show that the algorithm proposed in this paper has strong stability and is an effective method for solving TSP.
Study of Data Representation Methods for TCM Clinical Assistant Diagnosis
Authors: Liu Yin , Chen Hongguang
Published: June 2020

The unbalanced distribution of medical resources renders the research on TCM (Traditional Chinese Medicine) clinical assistant diagnosis more important to regions with less medical resources. In recent years, more and more clinical assistant diagnosis methods are deep learning (DL) based. The input data representation of these DL models is one of the most important factors for achieving better results. In this paper, different data representations methods are investigated using a multi-layer perceptron for a multi-class multi-label TCM clinical assistant diagnosis task. From the experimental results, it can be concluded that fast-Text representation is more suitable to this task since TCM clinical records are brief with limited information.
Discussion on Development Trend of Landscape Sculpture Based on Big Data Analysis Method
Authors: Zhou Xiaoping , Lian Xiaobo
Published: June 2020

As the whole society pays more and more attention to the construction of ecological civilization, landscape sculpture, as an interdisciplinary subject closely related to ecological civilization and keeping pace with the times, urgently needs to seek innovation and breakthrough. The existence of landscape sculpture can make the environment produce certain characteristics, not only can the landscape mold people’s sentiment, delight people’s body and mind, but also leave deep impression and unique feeling to the viewer, thus producing the effect of teaching landscape education and aesthetics. Landscape this paper analyzes the development and current situation of landscape sculpture, and based on the background of big data, discusses the development trend of the new era. With the development of landscape design, sculpture is more and more widely used in modern landscape design. The emergence of time and space big data brings new opportunities and challenges to landscape sculpture.
Research on English Translation of Computer-aided Classics Based on Natural Language Processing
Authors: Bi Shengqin
Published: June 2020

The advantages of computer-aided translation of scientific and technological classics into English are becoming increasingly obvious. The production and development of English translation of computer-aided science and technology classics is closely related to computer information technology. International exchanges and cooperation have gradually increased. Computer-aided translation of scientific and technological classics can ensure translation quality, greatly improve efficiency, and reduce complex mechanical work. In the case of machine translation with certain defects, computer-assisted translation of classic English texts is a good alternative, providing effective choices for translators. Among the core technologies of mainstream computer-assisted translation of English texts for scientific and technological classics are translation memory, terminology management, translation project management and other technologies. Based on the principles and characteristics of computer-aided translation, this paper analyzes the technology of computer-aided English translation of classic books based on natural language processing technology, and provides a reference for the use and promotion of computer-aided translation of English classics.
Spark Parallel Optimization Algorithm Based on Improved BP Neural Network
Authors: Ge Yaru
Published: June 2020

In view of the slow convergence rate and the local minimum problem of BP neural network when it is applied to big data classification, this paper proposes a spark parallel optimization algorithm based on Improved BP neural network. On the basis of MapReduce cloud computing platform, the parallel acceleration is realized by segmenting data sets, and the parallel data is processed by combining the Bagging algorithm, so that the network data of each node can complete the training independently until the convergence is realized, and finally the set becomes the data parallel classifier. The experiment results show that the parallel optimization algorithm proposed in this paper can effectively improve the parallel data processing speed, the classification accuracy is high, and has good adaptability in spark environment.
Convolutional neural network model based on text similarity for customer service
Authors: Du Wenshuang , Ge Jiawei , Liu Xuchen , Ai Junjie
Published: June 2020

Customer service is a good way for companies to communicate with customers, high-quality customer service can improve customer satisfaction and dependence on the enterprise. Text matching is a core problem in natural language understanding, and it can be applied to a large number of natural language processing tasks, such as information retrieval, question answering systems, repetition questions, dialogue systems, machine translation. These natural language processing tasks can be approximately abstracted into text matching problems. This paper combines text matching and convolutional neural networks to build an intelligent customer service model, and finally achieves ideal results in F1 value, recall rate and accuracy rate.
Feature Extraction Method of Ball Mill Load Based on Adaptive Variational Mode Decomposition and Improved Power Spectrum Analysis
Authors: Gao Yunpeng , Qing Zongsheng , Wu Cong , Li Xuan , Wang Pingao , Cao Taotao
Published: June 2020

The real-time working conditions of the ball mill in the grinding process are complicated, which makes it difficult to accurately obtain the internal load status of the ball mill. In this paper, the energy difference between the original cylinder vibration signal and the intrinsic mode function is proposed as the evaluation parameter of the adaptive variational mode decomposition (VMD) layer number, and a new autocorrelation function is constructed. The intrinsic mode function is processed by introducing the energy centrobaric method of the Nuttall self-convolution window. Accordingly, a ball mill load feature extraction method based on adaptive VMD and improved power spectrum estimation is proposed, and the ball mill load identification system based on LabVIEW is developed. The number of layers of intrinsic mode function could be adaptively determined. And the algorithm’s ability to resist mode aliasing and false components of this method is improved, which improves the accuracy of ball mill load detection. The measured results show that internal load features of ball mill during the grinding process are effectively extracted, and the mill load status is accurately identified, which provide an accurate and reliable basis for grinding optimization control and efficiency.
Binocular Camera Calibration for Underwater Stereo Matching
Authors: Deng Zejun , Sun Zhifeng
Published: June 2020

This paper presents an efficient stereo matching method applied to underwater with a binocular camera. Since underwater calibration is an inconvenient task and the traditional imaging model in the air and the parameters of the camera are no longer applicable to the underwater because of the distortion caused by light refraction. To solve this problem, we proposed a way to calibrate the binocular camera in the air for underwater stereo matching. We studied the imaging principle of the camera, and derived the relationship of the camera between in the air and underwater. Then, we performed underwater stereo matching experiments using the parameters of the camera calibrated in the air and found it to be effective.
Research on the Innovative Management Mode in Enterprises during Big Data Era
Authors: Cai Mingyu
Published: June 2020

In China, information technology and network information technology has developed many years, people can get massive information in time and quickly on the network platform. During the big data era, enterprises must innovate management work in combination with the current situation all over the world, so as to make enterprises develop better. To be able to adapt to the big data era, managers need to consider how to innovate management from all aspects. In order to deal with the challenges that existing in the work of Chinese enterprises during the big data era, we proposed a optimization processing method of the enterprise management mode, hoping to be helpful for the enterprise managers to innovate the management mode.
Sentiment analysis of film reviews based on CNN-BLSTM-Attention
Authors: Shang Lu , Sui Liying , Wang Shusong , Zhang Dexue
Published: June 2020

In order to accurately analyse the emotional tendency of film reviews, help investors make decisions and improve the quality of works, an optimized CNN-BLSTM-Attention sentiment analysis model was designed. The CNN model has a strong ability to capture the local correlation of spatial or temporal structures. The RNN model can either process sequences of any length or capture long-range dependencies, but it is easy to cause the problem of gradient disappearance. The CNN-BLSTM-Attention sentiment analysis model designed in this paper, which combines the advantages of CNN and RNN, is more accurate when being used to analyze the sentiment characteristics of texts. The experimental results show that the accuracy of the CNN-BLSTM-Attention after optimization model is better than that of CNN and RNN models in the experiment, which proves the effectiveness of the analysis method in this paper and can provide some significance for the optimization of related sentiment analysis models.
Research on automatic mining and utilization of vulnerability in power web system
Authors: He Jin , Shang Boxiang , Li Yan , Yin Bo
Published: June 2020

With the promotion of the ubiquitous power IoT strategy, as well as the extensive application of advanced information communication technology and Internet + in the power grid, the power system has gradually broken the previous closure and exclusivity. The construction and deployment of open, interactive, extensive and interconnected power web business system are becoming more and more widespread. The inherent vulnerability and hidden danger of power web system make power face the risk transfer from network and information security to power system. Aiming at the potential security problems of vulnerability in the massive power web system, an in-depth study is made on the automatic mining and utilization technology of the vulnerability in the power web system. By carrying out researches on the framework design of automatic mining and utilization of vulnerabilities in power web system and the intelligent new mode of exploiting vulnerabilities in power web system, the overall design is made for the mechanism and model of automatic mining of asset vulnerabilities in power information system.
Evaluation Mechanism of Learning Achievement Based on Intelligent Learning Platform
Authors: Gong Xiqiang
Published: June 2020

In recent years, blended learning has received widespread attention in the field of college teaching. However, how to effectively evaluate the blended learning has not yet formed a mature theory. For this reason, on the basis of clarifying the current status of blended learning evaluation, this paper combines the individual achievement goals of students, and uses the analytic hierarchy process to explore a set of learning performance evaluation systems that use smart learning platforms as auxiliary tools. After years of teaching practice, the evaluation system is scientific and feasible, and it can effectively improve students’ learning results.
Combined Networks with Multi-level Attention for Distantly-Supervised Relation Extraction
Authors: Yuan Hongyang
Published: June 2020

Distantly-supervised relation extraction is expected to extract relational facts from very large corpora. However, it is inevitably accompanied by the problem of mislabeling, which affects the performance of relation extraction. To obtain richer information from sentences and make full use of the information of entity pairs, we propose the combined networks containing piecewise convolutional neural networks (PCNN) and bidirectional gated recurrent units (BiGRU) with multi-level attention. In this model, PCNN and BiGRU are utilized to obtain local features and sequence information. Then the multi-level attention is proposed to extract the most correlated information, which can affect the assignment of sentence attention weight. And it reduces the influence of wrong labeled instances indirectly. The experimental results show that our method can enhance the representation power of the network and prove the effectiveness of our model compared with several baseline methods.
A direction of arrival estimation method based on deep learning
Authors: Liu Yuji , Chen Huixiu , Wang Biao
Published: June 2020

Direction of arrival (DOA) estimation is a research subject of many experts and scholars in information, control and communication, and it is also a key technology in smart antenna and sonar array system. In this paper, the direction of arrival of the signal is estimated by establishing a classification network model of deep learning. After training, the network model can effectively identify the direction of arrival of the unknown signal. Under the same signal-to-noise ratio (SNR) environment, it is verified by software simulation that the accuracy is improved compared with the classical music algorithm.
Modal parameter identification of subsynchronous oscillation based on stochastic subspace algorithm
Authors: Shi Jie , Li Juan , Li Jing
Published: June 2020

At present, under the general trend of interconnection of power grid systems, the problem of sub-synchronous oscillation of power systems has gradually become the focus of attention. Stochastic subspace modal identification (SSI) algorithm plays an important role in vibration detection and damage identification of large motors. Finally, experiments are performed on the data collected in the field, and the results show that the SSI algorithm can better identify the mode of the signal. Compared with the traditional Prony algorithm and ARMA algorithm, the recognition accuracy of the stochastic subspace algorithm constructed in this paper is higher than that of Prony algorithm and ARMA algorithm, which has the advantage of strong anti-noise.
Multi-task Semantic Segmentation for Apolloscape based on CGAN
Authors: Lin Yuankai , Yu Xinjia , Cheng Tao
Published: June 2020

China’s road traffic environment has the characteristics of mutual occlusion between targets, common use of motor vehicles and non-motor vehicles, etc., causing the problem of reduced accuracy of semantic segmentation. KITTI, Cityscapes and other datasets cannot meet the specific needs of semantic segmentation of traffic environment images in China, so Apolloscape dataset is used. The traditional method uses a single evaluation standard, and lacks the consistency check of the image semantic segmentation results. It ignores the relationship between pixels and pixels, which easily causes misidentification and leads to traffic accidents. On the basis of traditional cross-comparison evaluation indicators, this paper adds a composite evaluation of traffic environment semantic segmentation, emphasizing the role between pixels, making the results more consistent. Furthermore, it is through using the algorithm in perceiving the three typical traffic environment for verifying effectiveness. As a result, multi-task learning method is effectively applied in the three environments to achieve similar performance without increasing the computational overhead.
Application Research of Fast UAV Aerial Photography Object Detection and Recognition Based on Improved YOLOv3
Authors: Xia Yan , Ye Guoxiang , Yan Songsong , Feng Zhangwei , Tian Feng
Published: June 2020

UAV aerial photography is affected by weather, altitude, illumination, occlusion and other factors resulting in the objective fact that the object has many variations in scale and perspective. This paper proposes an improved YOLOv3 algorithm which achieve object detection and recognition in view of the above complex working conditions quickly and accurately. Firstly, the network framework of YOLOv3 is altered and the BN layer is integrated into the convolution layer. The network structure is simplified and the speed of model detection is greatly accelerated but the detection accuracy is almost the same as original. Secondly, GIOU is adopted as the loss function for bounding box regression to prevent the existence of zero gradient and improve mean average precision (mAP) of the detection model. Finally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes independently. The results of a large number of comparative experiments show that the improved YOLOv3 algorithm can achieve object detection for the UAV aerial photography faster and more accurately. The detection speed is increased by 27%, the mAP is increased by 2.32% and the detection performance is also improved for small object significantly compared with YOLOv3.
Multi-terminal Experimental System Based on Wireless Local Area Network
Authors: Fan Kaigang , Chen Xin , Zhao Biao , Wang Jiale , Cui Wenbin , Ding Junliang , Hu Tao
Published: June 2020

Aiming at the problems of single experimental data display, difficult data recording, poor human-computer interaction and low security for an experimental system, a multi-terminal experimental system based on Android operating system and wireless local area network was designed. Based on the Android operating system and wireless LAN, the system combines MySQL database, Web server, and LabVIEW software, and adopts modular design, realizes real-time collection and recording of experimental data during the experiment, and multi-parameters cross hardware display, good human-computer interface and remote status monitoring.
Automatic Pilot Ship Route Planning Based on A RRT Guided Genetic Algorithm
Authors: Xie Ping , Li Wenhao , Xie Ping
Published: June 2020

The reasonable and optimized navigation for an automatic pilot ship is one of the key technologies in ship bridge system. In this paper, a genetic algorithm based path planning method is introduced for this kind of problem. In order to build efficient chromosome to improve the searching efficiency, before the planning procedure of the GA, a traditional but efficient fast searching method - RRT is utilized to give a clue to build the chromosomes. In this paper, at first, a searching area is constructed based on the result of RRT and secondly, a GA based planning algorithm is proposed for the ship to find a path from the start to a target. The fitness function and genetic operator are selected by roulette to ensure the robustness of genetic algorithm. The diversity of population is enhanced by two point crossover operators. Finally, the simulation results show the effectiveness of the proposed algorithm.
Research on the Detection Method of HOG-SVM for Doping Modified Hardware Trojan
Authors: Sun Chaolong , Li Lei , Zhou Wanting , Tan Xianjun , He Yuanhang
Published: June 2020

With the globalization of the IC industry, hardware security has become an important topic, especially hardware Trojans (HT) hidden in ICs. In this paper, we presented the method based on histogram of oriented gradient (HOG) and support vector machine (SVM) to detect doping modified HTs, which is generally inserted by modifying the layout of some standard cells and is very difficult to detect. In order to address this problem, this paper firstly takes the standard layout without changing the doping polarity as the Trojan free (TF) layout, and regards the modified layout with changing the doping polarity as the HT layout, and then classifies the experimental data set into HT or TF by extracting the HOG features and using SVM training classification method. Experimental results demonstrate that the experimental accuracy of this method can reach 92.8%.
Research on 3D Visualization Simulation System of Deck Crew Based on OpenGL Technology
Authors: Yan Shiwei , Wang Wei , Geng Jianming , Wang Nengjian , Liu Qinhui
Published: June 2020

Based on OpenGL technology, a SGR simulation system which including virtual human operation was designed. The motion control of the virtual human was realized by using local finite state machine under the action of the control, and obstacle avoidance method for the virtual human was realized. Compared with the simulation programs developed with Vega Prime, this simulation speed obviously has advantages and the requirements on the computer are also lower.
Video Logging Casing Damage Image Recognition Based on GA-BP Neural Network
Authors: Hu Hongtao , Cheng Yiyao
Published: June 2020

Oil casing damage detection is an important work to maintain the normal production in the oil field. Currently, the Back Propagation (BP) neural network is a frequently used method to automatically identify the casing damage. However, the BP neural network can only approach the local minimum, furthermore the learning speed is slow, and the structure selection can’t be determined accurately. Therefore, in this paper, a recognition method based on the optimization of initial threshold and initial weight of BP neural network through genetic algorithm (GA) is proposed to improve the accuracy and efficiency of neural network recognition. Based on the oil casing damage image samples, this paper firstly constructs a feature parameter table of three damage types, and then analyzes the main components of the obtained feature parameters to reduce the dimensions, and finally builds a GA-BP neural network classifier. Using the construction result of feature parameters as the network’s input parameters, the experimental results show that the accuracy rate of GA-BP neural network is 78.67%, while the accuracy rate of BP neural network is 75.22%, indicating that the recognition result based on GA-BP neural network is better.
Design and implementation of FATFS data exchange with multiple storage media based on single-chip microcomputer
Authors: Chen Zerui
Published: June 2020

At present, a single-chip microcomputer without an operating system is used for data exchange with single storage media and the transmission speed is limited. In this paper, FATFS module is transplanted on a single-chip microcomputer based on an ARM Cortex-M7 micro-controller. The data acquisition and storage are carried out in parallel with multiple storage media such as SD, the NAND FLASH and the U disk on a high-performance STM32H7 platform. The read-write speed is tested finally. The results show that the file management function can be completed well and the operation is stable.
Research and Improvement of Adaptive Modulation and Coding Scheme for VDE-TER System
Authors: Li Zhe , Li Hai , Zhang Qin
Published: June 2020

VHF data exchange system (VDES) is an enhanced and updated system of AIS (Automatic Identification System) focused on maritime mobile communication, and VDE (VHF Data Exchange) is a main function of VDES. In this paper, we analyze the original adaptive modulation and coding (AMC) scheme of terrestrial VDE (VDE-TER). Because the original AMC scheme reserves several modulation and coding schemes for future use, we present an improved AMC scheme with more MCSs to increase system throughput. Simulation results have shown that the throughput performance of the proposed AMC scheme is improved.
Analysis of Landscape Sculpture and Its Environment Based on Artificial Intelligence Era
Authors: Zhou Xiaoping , Lian Xiaobo
Published: June 2020

With the continuous expansion of the application field of artificial intelligence, the exploration of the unknown world and the limit field has been deepened, and the life and production mode of people have also undergone tremendous changes. Based on this, this paper analyzes artificial intelligence and landscape design, and studies the influence of artificial intelligence era on landscape design, and through theoretical understanding and exposition of the current concept of landscape sculpture under the interaction of multiple cultures and the interaction between its environmental art forms. As a sculpture placed in a specific outdoor environment, it is relatively irreplaceable and should not only be limited to the performance and artistic treatment of the body itself, but also consider the coordination of various factors in the specific environmental space. Modern sculpture and landscape design can be better reflected in the natural environment, the humanistic environment, the public, the city, the inheritance of national culture and modernity.
Deconstruction and Reconstruction of Marketing System under the Background of Big Data
Authors: Jian Zhou , Zhenyi Zhao
Published: June 2020

The continuous development of communication technology at this stage has brought many opportunities for mobile terminal equipment. In marketing, the whole construction of system optimization needs to use relationship chain as its starting point to establish a complete data analysis system. At present, the continuous application and development of Internet technology has brought about major changes in its environment. “Big data” has become an important part of the development of the times and has a direct impact on marketing management companies. Because there are certain differences and particularities in the data, it is necessary to pay attention to the analysis, management, and storage of the economy in the construction of the system.
Application Analysis of Embedded System in Vehicle Electronics
Authors: Han Chengzhi
Published: June 2020

With the continuous improvement of people’s living standards, the demand for automobiles has also increased. Cars have become an important tool for people to travel. The automotive industry has also achieved very good results after many years of development, especially in the area of electronic technology. As a necessary part of automobiles, automotive electronics, and the application of embedded systems to automotive electronics has become the focus of development of the current automotive industry. Looking at the majority of controllable systems in automobiles, such as body control, engine management, in-vehicle entertainment systems, Bluetooth systems, they all rely on the help of embedded systems. For this reason, this article will focus on the application status of embedded systems in automotive electronics.
Research on order-preserving encryption scheme based on CryptDB
Authors: Jiang Xiaoning , Kong Xiangfeng , Xu Zhenchi
Published: June 2020

With the rapid development of cloud computing technology, cloud database, as an important part of cloud computing services, has gradually become necessary for daily work of enterprises or individuals. However, entrusting data to third-party managing can lead to security issues such as data leakage, and users cannot guarantee data security. To this end, this paper is based on the CryptDB, an open source database encryption proxy system designed by MIT, in this thesis we propose an improvement scheme for the shortcomings of the original CryptDB system. Specific contents including: By studying the CryptDB system, we find that the system is lack of scalability for different databases and does not involve the management of system keys. For the inefficiency of the mutable Order-Preserving Encryption (mOPE) in CryptDB system, we propose an improved additive Order-Revealing Encryption (aORE) scheme by combining the Practical Order-Revealing Encryption (P-ORE) and mOPE. The scheme is based on pseudorandom function and double encryption. Compared with mOPE, it can improve the execution efficiency of the Order-Preserving scheme at the expense of security.
Radiator threat assessment based on MEA-BP neural network
Authors: Xu Yuheng , Cheng Siyi , Zhang Hubiao
Published: June 2020

In order to promote the effectiveness of radiation threat assessment, this article builds the mind evolutionary algorithm (MEA) to optimize the BP neural network model of radiation threat assessment. The mind evolutionary algorithm was used to optimize the BP neural network model, through global population convergence and local populations of alienation, the network’s initial weights and threshold and then training the BP neural network, overcoming the random initial value when the disadvantages of neural network fall into the local optimum. The experimental results show that the evaluation results of the BP neural network optimized by the thinking evolution algorithm are significantly smaller than the mean square error of the BP neural network, this algorithm can quickly and effectively implement radiation threat assessment.
Survey of RDF Keyword Query Techniques
Authors: Dong-yang Jiang
Published: June 2020

With the development of the Semantic Web and information extraction technologies, more and more available RDF data has emerged on the Web. It has become one of the research hot spots in semantic Web technology. In this paper, in terms of RDF data organization and storage, as well as query expression, query processing. The analysis and comparison of RDF keyword query approaches are presented deeply and future research challenges are put forward.
Research on Thermal Stress Birefringence of Longitudinal Modulated OVS
Authors: Zhu Defeng , Xu Qifeng , Xie Nan
Published: June 2020

Thermal stress birefringence is a key factor affecting the stability of optical voltage sensors. The study of thermal stress birefringence is of great significance for the development of optical voltage sensors. In this regard, this article first analyzes the thermal stress birefringence theory systematically based on the theory. Then, by adopting finite element method, ANSYS software is used to establish a model of the longitudinal modulation optical voltage sensor, and the thermal stress in the BGO crystal is simulated and analyzed. Finally, the distribution and change law of stress birefringence in crystal are obtained by combining theory and simulation. The results show that the average linear birefringence on the central clear path is 4.8004×10−7, and the resulting phase delay is 0.0476 (2.7273deg). When the clear path is off-center, the birefringence will increase, and increasing the spot size also increase the birefringence.
A Spectrum Sensing Method based on Antieigenvalues and Stochastic Resonance
Authors: Lu Jin
Published: June 2020

Cognitive radio is an influential technology to solve the issue of spectrum scarcity caused by the massive number of wireless mobile devices. Spectrum sensing can alleviate interference situations among cognitive radio devices, and efficiently utilize the available radio spectrum. This paper proposes a novel spectrum sensing approach based on antieigenvalue and stochastic resonance. Stochastic resonance is used to enforce the detecting signals from multiple antennas in low SNR condition. Then the sample covariance matrix and its antieigenvalues of the enforcing detection signal are computed for constructing test statistic. The simulation results demonstrate that the proposed detector is superior than the existing antieigenvalue based detector, and is robust in strong noise background.

Index Terms - spectrum sensing, stochastic resonance, multiple antenna, antieigenvalue.
Specific emitter identification of radar based on one dimensional convolution neural network
Authors: Xiao Yao , Wei Xi zhang
Published: June 2020

Specific Emitter Identification (SEI) of radar has become a focus point of research and difficulty in the field of electronic reconnaissance, and the application of Automatic Dependent Surveillance Broadcast (ADS-B) signal in Identification Friend or Foe (IFF) system is beginning to raise increasing concern. At present, the research of deep neural network in the field of target recognition mostly focuses on the modulation pattern recognition of signal, but there are few applications for the specific emitter identification. In order to overcome the shortcoming that the effect of the traditional method is not excellent, a radar emitter target recognition method based on one-dimensional(1D) convolutional neural network is proposed for ADS-B signal in this paper. Taking the sequence data of ADS-B pulse signals as the training and testing samples of emitter target identification, the frequency domain features of ADS-B signal are obtained by Fast Fourier Transform (FFT), and the target identification is achieved by combining the convolution neural network with the fine feature extraction. The result of simulation demonstrates that the specific emitter identification based on frequency domain and one-dimensional(1D) convolutional neural network has an excellent recognition performance, which effectively resolves the shortcomings of the traditional method of feature extraction and low classification accuracy.
Research on Application of Data Mining Technology in Network Intrusion Detection
Authors: Song Haibo , Yin Yilin
Published: June 2020

In order to improve the efficiency of intrusion detection systems, data mining technology is applied to network intrusion detection. This article introduces the basic concepts of intrusion detection systems, describes the techniques commonly used in data mining research in intrusion detection systems, proposes an intrusion detection system based on data mining, and an improved k-means algorithm.
Research and Design of Network Situation Awareness System Based on Big Data
Authors: Meng Ran , Pei Xiaobing
Published: June 2020

Network situational awareness has become a new hotspot in the research of network security. Based on the analysis of existing network situational awareness models, this paper establishes a network situational awareness model based on the SimHash algorithm in a big data environment, which provides reference value for the next step of the network situational awareness system.
Routing Algorithm for Elastic Optical Network Based on Machine Learning assisted Traffic Prediction
Authors: Wang Yutong , Cheng Bokai , Hang Cheng , Yu Jiaoliang , Liu Shixin , Hu Yufei , Shen Jianhua
Published: June 2020

Elastic Optical Network (EON) is the promising technology for future network which may suffer congestion or fragment problems when huge amount of burst traffic arrives. This paper proposes an improved routing strategy for EON based on Artificial Intelligent (AI) assisted traffic prediction. Theoretical analysis and simulation results show that compared with traditional algorithms, the improved algorithm can effectively reduce the blocking rate of the network especially under heavy network load.
Dynamic Tracing Buffer for Intrusion Detection by XGboost
Authors: Chen Lin
Published: June 2020

Dynamic tracing buffer for the intrusion detection program or service is a novel method to design the system guard process. However, due to lack of human monitoring and its default signature-based security measures are ineffectual for detecting stealth attacks, the server distributed system needs an intelligent intrusion detection solution. This paper proposed a novel method to achieve the intrusion detection task through a dynamic contiguous system call tracing buffer.
Data Asset Value Assessment Literature Review and Prospect
Authors: Yanlin Wang , Haijun Zhao
Published: June 2020

With the development of the internet, Cloud Computing and other new generations of information technology, the society has entered the era of Big Data from the Information Age, the role of Big Data is becoming more and more powerful in human decision-making support, and the exchange of big data property rights value has become a new hot spot for social and economic development. This paper analyzes the connotation, extension and the basic characteristics based on the value dimension of data assets, combs the research results of scholars in the value evaluation of data assets at home and abroad, and puts forward the development direction of the value evaluation of data assets in the future around the natural value-added, externality and multi-dimensional value characteristics of data assets, in order to pave the way for future scholars.
A spatiotemporal compressive data gathering method combining block-wise compressed sensing with logical mapping
Authors: Li Canhua , Bie Hongxia
Published: June 2020

To fully exploit the spatiotemporal correlation of big sensory data towards transmission reduction, we propose a spatiotemporal compressive data gathering method combining block-wise compressed sensing (BCS) with logical mapping (LM). The method explores data correlation according to both the locations and contents of sensors and performs the dual compression of spatial and temporal domains. Specifically, temporal compression is achieved by random sampling at each sensor, as the temporal correlation of data is neglected by traditional BCS-based methods. Additionally, the spatial correlations caused by the contents of sensors and clusters have not been wholly exploited by the existing compressive data gathering methods yet, thus at edge devices, the temporal measurements of sensors are roughly sorted by the intra-cluster LM before the BCS-based spatial compression is performed; while at sink, the inter-cluster LM is used to coarsely order the data of clusters with joint reconstruction done afterward. The simulation results based on real-world data show that LM can effectively improve the compressibility of data. Our method not only guarantees the reconstruction quality but greatly reduces the transmissions and energy consumption of the sensor network.
A Low Complexity joint synchronization algorithm based on CAZAC sequence for OFDM system
Authors: Chen Ying , Nie Wei
Published: June 2020

To solve the problem of timing ambiguity and reduce the complexity of integer frequency offsets (IFO) estimation for OFDM system, this paper proposes a novel synchronization algorithm based on CAZAC sequence in time domain. The algorithm constructs a training sequence with conjugate and symmetry properties. Firstly, timing synchronization and IFO estimation are completed simultaneously by utilizing the cross-correlating of the training sequence and local signal based on the analysis of the impact of IFO on the timing of meric peak positions. Then the fractional frequency offsets (FFO) estimation is done using the repeatability of the symmetrical training sequence. Compared with the existing representative algorithms, the proposed algorithm achieves better synchronous performances with lower computational complexity and acceptable transmission efficiency.
Application of BA-BP neural network in surface reconstruction
Authors: Wang Hai-jun , Jin Tao
Published: June 2020

Aiming at the problems of current BP neural network model for surface reconstruction that are sensitive to the initial weights and thresholds, easy to fall into local minima, and the reconstruction accuracy is not high, a BA-BP surface reconstruction algorithm is proposed. The model weights and thresholds are optimized by bat algorithm for the first time, and then the BP algorithm is used to further optimize the weights and thresholds of model. Then, a surface reconstruction model based on the BA-BP neural network is established. By comparing with the reconstruction effect based on BP algorithm, quadratic fitting algorithm, wolf pack neural network algorithm and cuckoo neural network algorithm model, it proves that the idea of using bat algorithm to optimize BP model to construct surface reconstruction model is feasible. Bat algorithm, like other swarm intelligence algorithms, can effectively improve the efficiency of BP algorithm surface reconstruction model.
On curriculum relevance of the information management and information system major in the age of big data
Authors: Wu Weiwei , Ao Xiqin , Li Fan
Published: June 2020

This paper aims to study the curriculum relevance of the major of information management and information system in the age of big data by analysing the curriculum scores of students via Pearson correlation coefficient and Apriori algorithm employing the software IBM Statistical Product and Service Solutions (SPSS) Statistics 26.0. The correlations between different courses are presented from the multiple perspectives by statistical description. The results could provide a theoretical basis to improve the training program of information management and information system major.
Overview of Machine Tool Error Detection Technology
Authors: Li Xinkai , Li Dehua , Lao Yingying , Zhang Ruijie , Han Yi , Yao Xing
Published: June 2020

With the rapid development of modern mechanical manufacturing technology, precision and ultra-precision processing technology has become the main trend of the development of modern mechanical manufacturing industry. As a basic tool in mechanical manufacturing, the accuracy of CNC machine tools is an important index affecting the accuracy of workpiece processing. Therefore, the research on improving the accuracy of CNC machine tools has received great attention. In all kinds of high-speed and precision machine tools, the problem of the decline of machining accuracy caused by machine tool errors has become increasingly prominent. How to identify and eliminate machine tool errors quickly and efficiently has become an increasingly urgent task. In this paper, the research status of machine tool error detection technology at home and abroad is analyzed, in order to provide a reference for the development of machine tool error detection technology.
Electrical hysteresis of single-walled carbon nanotube field effect transistors and its modulation by an external magnetic field
Authors: Peng Zhisheng , Cai Jinzhong , Wei Haonan , Yue Junyi , Liu Jia , Sun Lianfeng , Wang Gongtang
Published: June 2020

Electrical hysteresis in single-walled carbon nanotube (SWNT) field-effect transistors has been observed in 2002, which is a key factor to determine the performance of carbon nanotube field-effect transistors and related systems. Meanwhile, the mechanisms of the hysteresis are controversial and in-depth understanding is still required. In this work, we report the observation of hysteresis in a three-terminal SWNT FET at room temperature under atmospheric conditions. The hysteresis is found to be modulated by an external magnetic field. And there is a significant difference for the modulation of the hysteresis between the positive and negative magnetic fields. Possible mechanism is proposed and discussed.
The Elimination and Detection Method of Ballistic Missile Infrared Images Bright Line Noise in Middle Trajectory
Authors: Zhu Min , Guo Ming , Shao Zhaoqun
Published: June 2020

The bright line noise of the infrared images in middle trajectory of the ballistic missile is similar with the track of the target. This will increase the difficulty in the detection and recognition of the target. So it is very important to eliminate the bright line in the infrared images. Firstly, the paper analyzes the characters of the bright line of the infrared images in the middle trajectory of the ballistic missile in the time domain and the frequency domain respectively. Secondly, the innovative method is put forward to detect the bright line automatically. Finally, the elimination and restoration method of the bright line is proposed in the time domain and the frequency domain. Numerical simulations are given to demonstrate the efficiency of the proposed control schemes and the experimental results show that the proposed method can eliminate the bright line effectively and improve the performance of the infrared images.
AutoSkin: Skeleton-based Human Skinning with Deep Neural Networks
Authors: Ouyang Xuming , Feng Cunguang
Published: June 2020

Articulated character animation has received increasing attention in applications like game or film production. One of the main challenges is the task of skinning that animator should specify the association between skeleton structure and skin surface which involves large amounts of manual weight painting and deformation tuning. In this paper, we propose a deep learning based framework for skinning. By using our framework, the skinning is seen as a multi-regression task and the network can infer the skeleton-skin association and compute the accurate weights automatically while only requiring minimal manual tuning. One significant advantage of our framework is that the impressive performance can be also achieved while dealing with complicated 3D characters, even these characters exist over a hundred bones or dress delicately. Our experiments have indicated that our framework could generate competitive results compared with the commercial software and we aim to apply it in the industry such as games or video applications.