bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Digital Encryption Security Information System Based on Identity Authentication
Authors: Cheng Yunli 
Published: July 2019

Abstract
In order to solve the problems in the current authentication system, a new authentication system scheme is proposed. The user identity is used as a key production factor by using the CPK (Combined Public Key) combination public key system. From the perspective of security design, and combining the theory of CPK combined public key system, the overall design framework of CPK key management system is proposed. According to the functional requirements of the system, the main modules of the system are divided, and its main functions are stipulated. The system uses a secure authentication card as the only identity of the user. The authentication card has a certain computing power and safe storage space. It realizes the secure storage of the user’s key. Finally, from the actual application scenario, the data flow of the user in the process of registration and use in the system is given. The main modules of the system are tested successfully through the security authentication card.
Design of express delivery equipment based on STM32
Authors: Deng Wenzhe , Fang Haoteng , Qian Chengbing , Ding Zhenglong 
Published: July 2019

Abstract
this paper introduces the current situation of campus express service points, points out the problems existing in campus express service points and the working situation of express delivery personnel. Through the design of an automatic courier processor. In the hope of automation as a solution to find ways to improve the efficiency of express delivery and solve the pain points in the development of campus express service points. This scheme not only provides a solution to the low efficiency of express processing in campus express service points, but also improves the automation level of express terminal processing in the express industry
A Design Method of Active Damping for Gyroscopic Systems Using Direct Velocity Feedback
Authors: Yu Nan-Hui , Zhang Ke-Wei , Liu Hai-Min , Liu Xiang , Zhang Jia-Fan 
Published: July 2019

Abstract
For gyroscopic dynamic systems, active damping using direct velocity feedback control with collocated actuator/sensor pairs is discussed in this paper. The proposed method of designing the damping is actually to assign the negative real parts of the poles for specific modes of the uncontrolled system. The targeted values of the negative real parts are determined by desired damping ratios to be gained for the specific modes. The procedure is formulated as solving constrained nonlinear equations or a constrained nonlinear least squares problem with elements of the control gain matrix as variables. Finally, it is illustrated via an example rotor system that the presented method is effective and easier to implement.
Robot Dynamic Parameter Identification based on BBO Algorithm
Authors: Xu Yu-xuan , Ke Shao-yu 
Published: July 2019

Abstract
In this paper, a linearized robot dynamics model is first established. The DH parameters and physical constraints of the robot are given. The joint motion trajectory based on Fourier series is designed then. By setting the conditional number of the observation matrix as the objective function, an Biogeography-based optimization algorithm is applied to identify the dynamic parameters of the robot. The physical constraint calibration and predictive torque results are compared with the traditional genetic algorithm to verify the advanced nature of our method.
Radar Imaging based on Orthogonal Matching Pursuit via Sparse Constraint
Authors: Xia ChaoYu , Yu Jie , Gao YuXiang , Tian Xiang 
Published: July 2019

Abstract
This paper presents constraint generalized orthogonal matching pursuit (C-gOMP) approach to tackle the problem of clutter mitigation for radar imaging in the compressive sensing context. The generalized orthogonal matching pursuit algorithm (gOMP) in radar imaging process is susceptible to environmental noise interference. Therefore, a particular constraint condition is added to the recovery signal, and the noise component is restrained after the greedy iteration. The C-gOMP algorithm uses the cost function to impose more constraints on each coefficient to ensure the convergence of the whole function. To further analyze the performance of C-gOMP algorithm, the effectiveness of this algorithm is demonstrated by simulations. The results show that the proposed approach is very obvious at suppressing unwanted clutter and enhancing the desired targets, and C-gOMP algorithm brings an evident performance improvement and application prospect.
Vegetable Pest Image Recognition Method Based on Improved VGG Convolution Neural Network
Authors: Ye Haijian , Han Hang , Zhu Linna , Duan Qingling 
Published: July 2019

Abstract
Vegetables are one of the main crops in China, and pests are one of the important factors affecting the quality of vegetables. In order to improve the recognition accuracy of vegetable pest images, a vegetable pest image recognition method based on improved VGG convolution neural network is proposed. Based on the VGG16 and VGG19 models, the method optimizes the number of full connection layers, replaces the original SoftMax classifier in VGGNet with the three-label SoftMax classifier, optimizes the structure and parameters of the model, and uses the weight parameters of convolution layer and pooling layer in the pre-training model in transfer learning. Experiments were carried out on the self-expanding data set of vegetable pest images, and the performance of the method was tested. Tensorflow was used to train the network model. The experimental results showed that the pre-trained models (VGG16, VGG19, Inception V3, ResNet50) were trained on the vegetable pest image data set to adapt to the recognition of vegetable pest images. The experimental results also showed that compared with Inception V3 and ResNet50, the recognition accuracy of the pre-trained models using VGG16 and VGG19 were higher, and the test accuracy of the two models were 99.90% and 99.99% respectively. Finally, the methods were compared with the traditional VGG method in self-expanding data sets. The results showed that the accuracy of VGG16 model and VGG19 model were improved from 85.90% and 86.21% to 100% and 100% respectively; the classification accuracy of VGG16 model was improved from 64.02% to 99.90%, and the classification accuracy of VGG19 model was improved from 85.83% to 99.99%, which effectively improved the recognition accuracy.
Video Logging Casing Damage Image Recognition Based on Improved Convolutional Neural Network
Authors: Hu Hongtao , Cheng Yiyao 
Published: July 2019

Abstract
Oil casing damage detection is the key point to ensure the smooth production of oil fields. In recent years, the automatic image recognition technology based on deep learning has become a researchful hot topic. But the common deep learning models have some defects in identifying the target features of casing damage images in the complex environment. This paper proposes an oil casing damage image recognition model based on DS-CNN(deep and shallow convolutional neural network). Based on VGG19, this model integrates the shallow convolution neural network. It combines global features extracted by the shallow network and the local features extracted by the deep network to form the input of the fully connected layer. The joint training of the shallow network and the deep network enables the image to be expressed in multiple scales to improve the recognition accuracy of the entire model. The experimental data is obtained from the downhole casing image dataset of an oil field in Sichuan. Experimental result shows that the macro-average F1 scores of the DS-CNN are 4.41 and 5.74 percentage points higher than those of the VGG19 model and the GoogleNet model, indicating that this model improves the recognition accuracy of oil casing damage images.
Object Detection under Natural Illumination Conditions using Superpixels and Local Binary Pattern Feature
Authors: Zhao Chuanyuan , Li Xiangjuan , Zhao Yue , Shi Xiaomin 
Published: July 2019

Abstract
This paper proposes a novel object detection method under natural illumination conditions in which a set of features, texture features and Local Binary Pattern (LBP) features, are extracted from the images acquired by a colour camera. The goal of this study is to develop a robust and fast algorithm to detect immature green citrus fruit in individual trees from colour images acquired under natural outdoor conditions. Colour and shape features are used to remove the complex background as much as possible. Since leaves share much more similarities with green citrus fruit in colour and to some extent in the shape, texture features are used for citrus detection. Statistical features, Tamura features and LBP features are used to build the KNN classifier. Experimental results show that the proposed approach provides fairly good object detection performance and confirms an efficient way for outdoor green citrus detection.
TDOA Location Accuracy Experiment
Authors: Wen Xiping , Wang Jinxia 
Published: July 2019

Abstract
TDOA-based wireless signal location technology has been extensively used in engineering applications. Due to the lack of quantitative TDOA location accuracy index benchmark, there is the absence of reference basis in the development of TDOA application system indicators and performance evaluation. In this paper, the engineering radio frequency receiver network is used to form the TDOA location system. Under the condition of non-multipath, TOA time is used to simulate TODA to monitor the signal of the location target radio frequency signal source, and the relationship between the location accuracy of TDOA and the signal bandwidth is evaluated. The experimental results can be used as a benchmark for performance evaluation of TDOA location system.
Low Profile Microsrip Antenna with Broadside Radiation Patterns and Low Cross Polarizations
Authors: Lai Ping , Xu Chao , Xu Zhuo , Luo Kang 
Published: July 2019

Abstract
A low profile microstrip antenna with adjustable bandwidth around 3 GHz is proposed. The compact antenna consists of a square patch with a square aperture to tune the resonance center frequency. The input impedance bandwidth is about 2.67% with stable gain around 3.2 dBi. Besides, the 3-dB beam width is about 100°. Both the side-fed and bottom-fed modes are analyzed. According to the simulation, good broadside radiation patterns with low cross polarizations are observed in the two principal planes. The detailed design considerations are described and discussed while the surface current distribution and radiation characteristics are analyzed. This design is suitable for mobile wireless communication.
Trademark Image Retrieval Based on Faster R-CNN
Authors: Wang Wenmei , Xu Xinxin , Zhang Jianglong , Yang LiFang , Song Gege , Huang Xianglin 
Published: July 2019

Abstract
Automatic retrieval of digital trademark images is significant for improving the efficiency of trademark examination and management. In this paper, we proposed a method based on deep learning for trademark retrieval. Faster R-CNN was first applied to trademark retrieval. The global feature descriptor of the image is extracted by Faster R-CNN and the local feature of the image is extracted through the object proposal regions by the Region Proposal Networks (RPN). Retrieval strategies consist of initial ranking and spatial reranking. The experimental results show that our proposed method achieves remarkable performance in trademark image retrieval.
Unsupervised Image Feature Extraction Based on Scattering Transform and Self-supervised Learning with Highly Training Efficiency
Authors: Zheng Guoshu , Zhu Qiuyu 
Published: July 2019

Abstract
Convolutional neural networks (CNN) can effectively extract high-level semantic features of images, however, to learn these features, a large number of labelled data is required. In order to extract image features without labelled data, this paper proposes an unsupervised image feature extraction method based on self-supervised learning. To learn image features, we train the neural network to identify the two-dimensional rotation applied to the image. The first few layers of the convolution network are replaced with a scattering network to speed up training process, and get good image features in the last few layers of the convolutional network. We input the extracted features into convolutional network for supervised learning, and take recognition accuracy as a criterion of feature validity. The experimental result show that the recognition accuracy gets 84% on CIFAR10, reaching the mainstream results of unsupervised method; and 62.17% on CIFAR100, which is very close to the rate of supervised learning. This method can be applied to the applications that do not have massive labelled training data and have limited computing resources.
Research on Online Junction Temperature Measurement of MOSFET Devices
Authors: Jiang Bo-yang , Guo Chun-sheng , Luo Lin 
Published: July 2019

Abstract
In order to detect the MOSFET junction temperature, the paper measures the MOSFET junction temperature by electrical method without affecting the normal operation of the devices and the circuits. In addition, the method of optimizing the temperature calibration curve proposed is helpful for engineers to efficiently monitor the junction temperature and prevent the risk of failure caused by high temperature.
Design of reconnaissance system based on STM32 small multi-rotor real-time image transmission
Authors: Liu Jiawei , Zhang Lu , Shi Daosheng 
Published: July 2019

Abstract
This project analyses the main technical points of aircraft design, makes a preliminary discussion on rotor dynamics and mechanical analysis of four-axis aircraft, analyses the particularity of aircraft structure, control flexibility and control principle, establishes three-dimensional rigid body coordinate axis for aircraft. Describes the relationship between flight attitude and coordinate axis. Chooses a multi-channel attitude fusion algorithm, anti-jamming data transmission mode, adjusts the relationship between frequency and rate of wireless data transmission in detail. At the same time, the principle and arithmetic of the PID controller are analyzed in detail, and it is applied to the speed control of DC motor of aircraft. Through experimental verification and test, a good stable control effect is obtained. The design and implementation of the flight control system and remote control software and hardware are completed, and the test and flight test of the whole aircraft are completed. The flight control stability is good. It has the characteristics of small size, fast response and long communication distance. It can fully meet the reconnaissance requirements of obtaining real-time high-definition images in various complex situations. The upper computer software can quickly decode image data, convert it to video output to ground personnel, and realize FPV reconnaissance.
Encryption Cipher Text Retrieval Scheme Based on Fully Homomorphic Encryption Enterprise Cloud Storage
Authors: Wang Lijuan , Ge Lina , Geng Bo , Wang Qiuyue 
Published: July 2019

Abstract
With the development of computer networks, cloud storage has become the mainstream way for people to store information. However, some essential information is disclosed during the storage process and the security of information has become the most concerned issue at present. In order to use the powerful computing of cloud storage to realize resource sharing between relevant departments without revealing information of other unrelated departments, this paper proposes an encryption cipher text retrieval scheme based on fully homomorphic encryption enterprise cloud storage (ECRS), and designs an Enterprise-side security model. The model uses full homomorphic encryption, decryption and spatial vector cipher text retrieval to achieve the security of information and resource sharing among enterprise departments. ECRS determines the employee who can encrypt or decrypt the files, and who has access to the files by modifying the number of attributes intersected between attribute sets. The solution can provide the security for cloud storage, resources for different departments, and improve the accuracy for cipher text retrieval.
Improved Chaotic Sequence Generation Method Based on Direct Spread Spectrum
Authors: Liu Fang , Jia Shiyao , Xu Xiaodan , Tian Minghao 
Published: July 2019

Abstract
Aimed at the shortcomings of one-dimensional chaotic sequences as spread spectrum address codes in DS/SS(Direct Sequence Spread Spectrum) communication system, such as low complexity, simplicity of generation and easy to be estimated backwards, etc, an improved chaotic sequence is proposed, which is composed of two one-dimensional chaotic maps and the fractal parameter of one map is controlled to control the fractal parameter of another map in a certain mathematical form. Then the performance analyses of the sequence are carried out from the angles of complexity, correlation, randomness and anti-interference ability. The results of simulation show that the improved chaotic sequence not only improves the complexity obviously, but also has good correlation and randomness, and the anti-interference ability in DS/SS communication is more prominent.
Research on Preview-Fuzzy Control Strategy Based on MR Damper in Railway Vehicle
Authors: Xinna Ma , Wenrui Shi , Jingwei Liu 
Published: July 2019

Abstract
With the increasing speed, the vibration control of high-speed railway vehicle suspension system becomes more and more important. In view of the nonlinearity of high-speed vehicle and the time-delay problem of control system, a new intelligent material magnetorheological damper (MRD) is introduced. On the basis of analyzing the dynamic characteristics of MR damper, a vertical dynamic model of high-speed railway vehicle system based on MR damper is established. In order to suppress the vertical vibration of suspension system, a preview-fuzzy control strategy based on MR damper is proposed. In order to verify the availability of the control strategy, a lot of simulations are carried out. The results show that the preview-fuzzy control strategy based on MR damper can effectively reduce the vibration amplitude of the suspension system and solve the problem of time delay to a certain extent.
Uncertainty of Geomagnetically Induced Current in Xinjiang 750kV Planned Power Grid Based on Polynomial Chaos Expansion
Authors: Cui Xikai , Liu Qing , Wang Yinglan 
Published: July 2019

Abstract
Geomagnetically inducted current (GIC) can cause DC bias of the transformer. The derivative effect of DC bias may threaten the safety of power equipment and power grid. To study the GIC uncertainty caused by the grid parameters variation is of great significance for the assessment and defense of GIC during magnetic storm. Based on the polynomial chaos expansion (PCE) method, the chaos polynomial expansion of GIC is constructed assuming the line impedance, transformer impedance and grounding impedance in the power grid as input variables. For planned Xinjiang power grid in 2020, the uncertainty analysis for the GIC of substations flowing into the earth is carried out by using the derived polynomial chaos expansions. The 95% confidence interval, mean and variance statistics of the GIC are obtained. Comparison between PCE method and Monte Carlo (MC) method shows the validity and the efficiency of the PCE method.
Research on the Role of Reader Behavior Development in Reading Promotion Promoted by New Media Communication
Authors: Hu Jianfei , Meng Luyang , Zhang Bin 
Published: July 2019

AbstractReading promotion is an important means to create a strong reading atmosphere and cultivate good reading habits. With the continuous popularity of new media, the form of reading promotion also needs to keep pace with the times and adjust strategies according to the reader’s behavior. Based on this phenomenon, this paper analyses the positive and negative effects of new media on reading behavior, summarizes the law of the development of reading behavior, and then puts forward some reading promotion strategies.
LMS Adaptive Filtering of Drilling Tool Vibration Signal
Authors: Simin Kang , Yuelong Wang , Tao Sun , Yi Gao , Yan He 
Published: July 2019

Abstract
As the dynamic measurement of attitude parameter of the steerable drilling tool under vibration may be not accurate, the Least Mean Square (LMS) adaptive filtering algorithm is adopted to filter the influence of drilling tool vibration on the attitude measurement in this paper. The simulation results show that the measurement error of inclination angle after LMS adaptive filtering can be less than 0.1°, and the measurement error of tool face angle is less than 6°, which could effectively improve the attitude measurement accuracy of vertical steerable drilling tools; the results of inverse analysis on actual drilling data show that the actual measurement error of inclination angle after LMS adaptive filtering is about 3°, which is much smaller than that before filtering. It shows that LMS adaptive filtering can effectively filter the vibration signal of drilling tools and greatly improve the dynamic measurement accuracy of the tool attitude.
Energy Balanced Clustering Protocol for Wireless Sensor Networks
Authors: Wang Lihong , Liu Ping 
Published: July 2019

Abstract
Wireless sensor network is composed of a large number of sensor nodes by self-organization in the monitoring area. The energy, computing and communication capabilities of sensor nodes are very limited, and therefore a good routing protocol is very important to prolong the lifetime of the network. An energy-balanced clustering protocol is proposed to solve the inequality problem of consumption in routing protocol. Clustering protocol considers the residual energy of nodes and the distance between sink nodes in the selection of cluster head node, which can be more evenly distributed cluster head. On the basis of estimating the optimal cluster number, the clustering method is improved, the number of clustering is reduced, and the network energy consumption is saved. The simulation experiment shows that the Clustering protocol makes the whole network energy consumption more balanced and increases the life cycle of the wireless sensor network.
Application of LSTM Neural Network in Forecasting Foreign Exchange Price
Authors: Qu Yaxin , Zhao Xue 
Published: July 2019

Abstract
LSTM neural network and RNN neural network models in deep learning are used to forecast the price of foreign exchange financial time series. The existing foreign exchange price and technical analysis indexes are taken as input parameters. By comparing the evaluation indexes of two deep learning models, the optimal neural network model is selected. The experimental results show that the LSTM neural network model has smaller root mean square error (RMSE) and mean absolute error (MAE) than the RNN network model, and the predicted price is more accurate.
Equipment State Assessment Based on Convolutional Neural Network
Authors: Yanglong Chen , Yanheng Ma , Yuanwei Yin 
Published: July 2019

Abstract
Aiming at the problem of the defect of less-information and poor reliability of assessment results when assessing equipment state, this paper proposes a method of equipment status assessment based on convolution neural network by utilizing the ability of equipment under cognitive testability design to easily obtain multi-source information. The assessment system is divided into feature layer and information layer. In the feature layer assessment, the analytic hierarchy process is used to determine the constant weight, then the variable weight theory is used to realize the real-time adjustment of the weight. And the convolutional neural network model is used in the assessment of the information layer. Finally, through the example verification and comparison with BP algorithm, it can be seen that this assessment method has fast recognition speed, high accuracy, more reliable assessment results, and has certain versatility and application value in engineering.