bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-task Semantic Segmentation for Apolloscape based on CGAN
Authors: Lin Yuankai , Yu Xinjia , Cheng Tao 
Published: June 2020

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.