bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biometrics-based cryptography scheme for E-Health systems
Authors: Chen Hanlin , Ding Ding , Su Shuchun , Yin Jingyi 
Published: June 2020

Abstract
Thanks to all-weather, effective and convenient services, E-Health Systems have been widely applied. However, with the increase of network risks and security threats, the privacy of personal medical information must be assured. The biometrics have become an effective and secure means of identification and authentication due to good distinctiveness and exclusiveness. To meet security and privacy requirements of E-Health systems, a biometrics-based cryptography scheme is proposed. First, Biometrics-based Fuzzy Authentication and Key Negotiation (BFAKN) strictly identifies all components and operators in the whole system, and safely negotiate the cryptographic keys. Based on the biometrics of biological signals and the fuzzy vault algorithm, mutual authentications between the sensors and the terminal and between the terminal and the server can be achieved, and the keys for local and public communications are negotiated safely, ensuring the security of all communications. Second, Fingerprint-based Authority Access Mechanism (FAAM) is proposed to fulfil aothority management, realizing secure storage and access of sensitive biomedical information in the system. Experimental results show that local communications within Wireless Body Area Networks (WBAN) and public communications have very high security, and personal medical information can be accessed safely.
Calculation and Analysis of DC Surge Arrester Temperature Field
Authors: Geng Lina , Cui Juyong , Hao Shuang , Li Jing , Liu ShuXin 
Published: June 2020

Abstract
Zinc oxide arresters have been widely used in high-voltage, ultra-high-voltage and ultra-high-voltage power transmission systems due to their excellent non-linear characteristics. However, because there is no gap between the arrester itself, there is a slight leakage current when the zinc oxide arrester is subjected to normal line voltage, which causes the aging of the zinc oxide resistance valve. The degree of aging and the rate of temperature rise are related to the temperature distribution, so it is of great significance to study the temperature field distribution of the DC arrester. This paper takes multi-column zinc oxide arrester as an example. According to the differential equation of heat transfer and the auxiliary equation, the simulation of time-varying temperature field is carried out. The simulation results are helpful to optimize the structure of multi-column parallel arrester and analyze the aging characteristics of valve.
An experimental study of vehicle detection on aerial imagery using deep learning-based detection approaches
Authors: Liao Xuanzhi , Sahran Shahnorbanun , Abdul Shukor Syaimak 
Published: June 2020

Abstract
Deep convolutional neural network technology is widely used to deal with general object detection in computer vision, and it achieved remarkable progress. Unmanned aerial vehicles provide large numbers of aerial imagery that significantly facilitate several applications including traffic monitoring, surveillance, tracking, rescue, and safe military tasks. This paper presents an experimental study to evaluate the performances of several state-of-the-art deep learning-based detection approaches on vehicle detection from aerial imagery. The pre-trained models, including Faster R-CNN, R-FCN, and SSD, are adopted from the TensorFlow model zoo, and the VEDAI dataset is used as the benchmark. The results show that Faster R-CNN combined with Resnet101 backbone achieved the highest mAP, which is 39.73% on the COCO metric. This experimental study expects to be a guideline to choose suitable approaches for particular applications.
Static Calculation and Analysis of Certain Gravity Dam
Authors: Wei Liuchuang , Wei Ping , Yang Yabiao 
Published: June 2020

Abstract
At present, the main work of gravity dam research tends to adopt modern design theory and analysis methods to solve engineering problems. The most commonly used method is finite element method numerical simulation technology. This method discretizes the elastic continuum into a combination of a finite number of elements, and considers the continuous conditions of displacement between the elements. It can comprehensively consider the effects of various influencing factors. It has obtained good application results in the structural analysis of the dam. In this paper, a concrete gravity dam is taken as an engineering example. Based on the force of the dam, ANSYS finite element software is used to perform static analysis on the hydrostatic pressure, lifting pressure, dead weight and other loads.
Recognition of garlic clove orientation based on machine vision
Authors: Li Y H , Tian L , Li T H , Wu Y Q , Niu Z R , Hou J L 
Published: June 2020

Abstract
To address the problem of low garlic clove-head-turning rates in mechanized sowing of garlic, this study proposes an algorithm for identifying garlic clove orientation using machine vision. First, the algorithm performs preprocessing operations such as image enhancement and color space conversion; then, it identifies and labels the garlic clove regions by determining the connected regions. Each garlic clove region is derived for independent analysis, and three garlic clove features of each clove are calculated. Then, based on the distinctiveness of each feature parameter with clove recognition, comprehensive feature parameters are obtained by fusing the information of the three feature parameters. Finally, garlic clove head is identified on the basis of these comprehensive feature parameters. Furthermore, two garlic varieties, ‘Cangshan’ and ‘Jinxiang’ garlic, are tested by capturing images of both single and multiple garlic cloves. The experimental results indicate that the average recognition rate of the algorithm is 97.44%, and the average running time is 0.61 s. The correct recognition rate of ‘Jinxiang’ garlic is 94.51%, while that of ‘Cangshan’ garlic is 100%. In summary, the algorithm has strong adaptability and high accuracy, and it can provide a useful reference for an intelligent garlic clove-head-turning mechanism for garlic planters.
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.