bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving LiDAR performance on complex terrain using CFD-based correction and direct-adjoint-loop optimization
Authors: Nabi S , Nishio N , Grover P , Matai R , Kajiyama Y , Kotake N , Kameyama S , Yoshiki W , Iida M
Published: March 2020

Naïve estimation of horizontal wind velocity over complex terrain using measurements from a single wind-LiDAR introduces a bias due to the assumption of uniform velocity in any horizontal plane. While Computational Fluid Dynamics (CFD)-based methods have been proposed for bias removal, there are several issues exist in the implantation. For instance, the upstream atmospheric boundary layer thickness or direction are unknown. Conventional CFD-based corrections use trial and error to estimate the bias. Such approaches not only become numerically intractable for complicated flows, e.g. when the number of unknowns is large, but they also suffer from the fact that there is no guarantee for optimality of the obtained results. We propose a direct-adjoint-loop (DAL) optimization based framework to estimate such unknown parameters in a systematic way. For the validation of the method, we performed an experimental study using DIABREZZA LiDAR on a complex terrain for two wind directions of northwesterly (NW) and southeasterly (SE). The slope error associated with linear regression improved from -0.09 to -0.02 for SE and from -0.09 to +0.04 for NW.
Modeling Uncertainties of Wind Field Reconstruction Using Lidar
Authors: Schlipf David , Koch Martin , Raach Steffen
Published: March 2020

The use of nacelle-based lidar systems for power performance testing of wind turbines is promising. Here, the knowledge of uncertainties is crucial. Wind field reconstruction is necessary to estimate wind field characteristics such as horizontal wind speed and wind direction from line-of-sight wind speed measurements using a wind flow model. In this work, two types of uncertainties in this process are investigated and confirmed in simulation: the uncertainty caused by measurement errors and by modeling errors. Based on this first investigation, the modeling uncertainty is exceeding the measurement uncertainty.
Multicast Routing Algorithms Based on Levy Flying Particle Swarm Optimization
Authors: Zhi Chun , Cui jianming , Zhu liangshuai
Published: March 2020

Aiming at the problems of bandwidth, network delay and cost in IPv6 multicast routing transmission, a particle swarm optimization multicast routing algorithm (LPSO) based on Levy flight mechanism is proposed to find the optimal path in the multicast tree. In the iteration process, the cost, bandwidth, delay and delay jitter of multicast tree are taken as indexes, and penalty function is combined to constrain the algorithm. Compared with existing multicast routing algorithms, this algorithm improves the global search and meets the requirements of QoS in multicast routing.
Improvement of Text Segmentation TextTiling Algorithm
Authors: He Xin , Wang Jian , Zhang Quan , Ju Xiaoming
Published: March 2020

Text segmentation has a wide range of applications in the fields of information extraction and abstract generation. The method uses local information to determine a specific segmentation, and cannot fully consider the information of the remaining paragraphs. In response to this problem, this paper improves the segmentation point selection of the algorithm. After the similarity values of the respective interval points are obtained by the similarity calculation, the sliding window is used to slide on the interval points, and the similarity value of the interval points at the center of the window is replaced with the average of all the similarity values in the window. The experimental results show that the text segmentation based on the improved method can suppress small local changes on the curve, thus highlighting the large similarity value changes, and effectively improving the performance of text segmentation.
An improved weighted fusion algorithm of multi-sensor
Authors: Liu Haibin , Fang Shengyu , Jianhua JI
Published: March 2020

Multi-sensor data fusion is to take full advantage of the complementary nature of multivariate data to improve the feasibility of the statistics. The weighted fusion algorithm is commonly used due to its easiness to achieve. Among the relative factors, the weight directly impacts the results of the data fusion, therefore, the selection of weight is particularly important, as choosing an inappropriate weight will lead to the instability of algorithm performance. To find the best weight, we develop an improved weighted fusion algorithm, introducing the concept of the optimal proportion weight and using secondary weighted approach ----- the single sensor will be weighted individually before the whole sensor system is weighted in order to achieve the optimal algorithm performance.
An Intrusion Detection Method based on Stacked Autoencoder and Support Vector Machine
Authors: Shuaixin Tan
Published: March 2020

To explore the feasibility of forming a combined classifier for intrusion detection by applying Support Vector Machine (SVM) and Stacked Autoencoder (SAE), an intrusion detection method based on Stacked Autoencoder and Support Vector Machine is proposed. Considering that the Piecewise Radial Basis Function (P-RBF) in the Support Vector Machine can improve the classification performance, the Radial Basis Function (RBF) and P-RBF are selected for the proposed method, and the detection performance of the above two methods are compared. Experiments show that the method based on Stack Autoencoder and Support Vector Machine using P-RBF kernel are superior to the one using RBF kernel in accuracy, detection rate and false alarm rate. The method based on Stack Autoencoder and Support Vector Machine provides an idea for the application in the field of intrusion detection.
Research on Improved Access Control Model Based on T-RBAC
Authors: Wang Yong , Yang Yueping , Wang Binxu , Ran Qingwen , Ju Xiaoming
Published: March 2020

The T-RBAC model has a wide range of applications in various fields, but has certain disadvantages. Aiming at the shortcomings of T-RBAC model in task assignment and authority management, an improved model is proposed. The new model simplifies the task classification of T-RBAC model. Considering the task context, task time limit, and task status attributes, the role manager, task manager, and authority manager are introduced, and the grant of authority is closely linked with the three managers, which enhances the dynamic management of rights. The constraints are strengthened and expanded to ensure dynamic separation of duties during task execution. The model has finer-grained rights management, better meets the principle of separation of duties and least privilege, and has higher security.
The Application of GABP Neural Network Algorithm in the Aspect of Security Assessment of Computer Network
Authors: Zhang Yan-lin , Yu Jun
Published: March 2020

With the rapid development and wide application of computer and network technology, there appear increasing number of factors affecting the security of network system. Therefore, it is urgent for network security administrators to adopt efficient network security algorithm to guarantee the network security. On the basis of GABP neural network algorithm, and in combination with different sub-modules and supporting platforms, the small complex network can be evaluated in an all-round way, and the network security can be effectively managed.
Mining Important Functions in Software Network by Node Vulnerability
Authors: Zhang Bing , Sun Shengting , Hao Xiaobing
Published: March 2020

Given that analysis on the vulnerability of functions is helpful to the detection and improvement of software security, this paper aims to propose an efficient methods to identify the vulnerable nodes (ITVN) in different software by the interdependence of functions. First, the dynamic software execution process was constructed as Software Execution Dependency Network (SEDN) based on Complex network theory. Second, by analyzing the dependency relationship among functions, the algorithm calVulAndScoOfNodes (CVSN) was designed to compute the vulnerability and the affected scope of each node for further analysis. Third, in order to measure the functions vulnerability in the whole software network, the algorithm calVulDegreeOfNodes (CVDN) was put forward to calculate the vulnerable degree of each node. Finally, the Vulnerable Nodes in different software were obtained by ITVN. Experimental results show that the vulnerable nodes selected as important nodes are well-reasoned in software network by testing different software, and the measures are effective for evaluating nodes vulnerability.
The Construction and Application of Hybrid Teaching Model of Flipping Classroom and Divided Classroom Base on Project-driven in the Teaching of Java programming
Authors: Zhang Maohong , Yu Zhenmei , Sun Yuhong , Gong Xueyan , Liu Yuan
Published: March 2020

In view of the problems existing in the teaching of Java programming, in order to further improve the teaching quality of Java programming, a hybrid teaching model of flipping classroom and divided classroom based on project-driven was proposed. The problem-oriented, student-cantered teaching concept forms an effective teaching activity for teachers and students. With a complete project throughout the course, online learning is adopted to deeply explore students’ independent learning ability, assist the teaching of course content and group communication learning, combine the flipped classroom with the divided classroom to construct a hybrid teaching model. The experiment proves that under the project-driven teaching mode, the full use of the hybrid teaching of the flipped classroom and the divided classroom can greatly improve learning effect of students at different levels of knowledge, and students’ independent learning ability, innovation and creativity ability can also be improved accordingly.
Intelligent Fault Diagnosis of Engine Based on PCA-SOM
Authors: Zhang Yangyang , Jia Yunxian , Guo Chiming , Wu Weiyi
Published: March 2020

Self-Organizing Feature Map (SOM) is a kind of self-organizing and self-learning network without teacher. It is mainly used for pattern recognition and region classification of input vectors. A fault diagnosis method of engine fuel supply system based on SOM neural network is proposed. The sensor is used to monitor the fuel pressure waveform of a certain engine fuel supply system, time domain analysis and feature extraction are carried out on the waveform, and the feature dimension reduction is realized by PCA to form the input vector of SOM neural network. The SOM neural network is used to establish the diagnosis model and then recognize fault patterns for test samples. The results of pattern recognition show that SOM neural network can identify and classify faults accurately, and it has certain engineering application value.
RBF neural network sliding mode control on pitching motion of an underwater spherical vechicle
Authors: Liu Zhimin , Zhou Yiyang
Published: March 2020

The pendulum spherical underwater vehicle can realize the ascending and descending motion in water by using the internal pitch pendulum and the central propeller. It is the frequent swing of the pitch pendulum that causes the fluctuation of the speed of the robot and reduces the stability of the system. A two-stage sliding method is proposed for the pitch driving characteristics of the robot and it can control the pitch angle and the swing angle of the pitch pendulum of the robot at the same time. The neural network is introduced to compensate the interference items adaptively. The stability of the designed controller is proved in theory. The simulation and experimental results show that the proposed control method can realize the rapid control of the pitch angle of the robot and the frequency of the pitch pendulum. The complex swing is restrained effectively, and the stability of the underwater motion of the robot is improved.
A Requirement Engineering Approach to User-Centered Design
Authors: Xiao Bin , Su Kai , Su Xiaoguang
Published: March 2020

This paper introduces a user-centered design method in software engineering practice. This method can develop software more effectively based on the design results of UCD, and make these results be tracked and tested in the future. Another feature of this method is to make UCD as schedulable and manageable as other software development activities. This paper introduces a requirement analysis framework based on three types of usability requirements, which reflect the results of UCD activities in software development. These types of requirements come from the description of UCD in ISO 13407 model.
An Integrated Cell Region Reconstruction Method Based upon Mask R-CNN Model and Improved Voronoi Algorithm
Authors: Zou Shun , Wei Shengyun , Liu Han
Published: March 2020

Cell region segmentation in IHC staining images is the basis for quantitative analysis, which is very challenging due to the incomplete membrane staining in IHC images. In this paper, we propose a novel integrated cell region reconstruction method based upon Mask R-CNN model and improved Voronoi algorithm to reconstruct cell region in incomplete IHC staining images, in which Mask R-CNN model is introduced to perform nucleus segmentation, and then the improved Voronoi algorithm with the prior knowledge of nucleus-cytoplasm ratio is used to reconstruct the whole cell region. We also develop a framework for the integrated method to achieve automatic end-to-end cell region segmentation. Our method could better solve the problem of cell segmentation under incomplete staining than only deep-learning method or standard Voronoi algorithm, and the reconstructed cell region could better fit the real cell boundary.
A G2G Similarity Guided Pedestrian Re-identification Algorithm
Authors: Wang Guangcai , Gao Shang , Fan Di
Published: March 2020

Pedestrian re-identification aims to settle the matching problem for a given target pedestrian under the multi-camera with non-overlapping visual field, so as to achieve the goal of pedestrian retrieval. In this paper, the similarity between the gallery images (G2G similarity) will be used to guide and refine the similarity between query images and gallery images (P2G similarity). It is also introduced into the training process and playing a supervisory role. To fully utilize details of the images, the image features are horizontally overlapped into groups, and the similarities between each group of the query images and the gallery images are calculated respectively. By learning the weights of grouping features in the training process, the importance of key parts can be automatically perceived. The results on the Market-1501 and CUHK03 datasets prove the effectiveness of proposed algorithm.
Research on Extension Innovation Design Method of Product Family Based on Kano Demand Model
Authors: Chen Huihong , Liu Shiming
Published: March 2020

By the existing innovation design methods of product family, people can get new products design effectively. However, these methods can’t make their products to meet customers’ demand completely, because they can’t help people turn customers’ demands into products characteristics using model methods. This paper uses Kano requirement model to find essential demands, clarify customers’ demands, and utilizes extension innovation methods to establish models for the products, which have different characteristics, then uses extensive analysis and extension transformations to obtained new products designs, and finally get innovation designs of product family according to superiority evaluation methods. A design for mobile phone is taken as an example to demonstrate this method..
Method of Controlling Vehicles at Intersections Based on the Auction Algorithm
Authors: Liu Mingjian , Zhang Sijia , Teng Lin
Published: March 2020

In this study, a method of controlling vehicles at intersections based on the auction idea was proposed, which can be compatible with unmanned vehicles and human driving vehicles, ensuring driving safety and improving traffic efficiency at intersections. Collecting vehicle reservation requests through the bidding unit at intersections, this method provided reasonable vehicle traffic strategies according to information such as vehicle routing (Route), budget (M), hunger coefficient and credit coefficient. The simulation results show that the method of controlling vehicles at intersections based on the auction idea can effectively reduce the average delay of vehicles caused by waiting and improve the throughput of the intersection, verifying the effectiveness of this method.
An improved measurement method of size of mechanical parts based on monocular vision
Authors: Guo Qiang , Fu Wenyu
Published: March 2020

During the monocular measurement of multiple mechanical parts with non-negligible thickness at the same time, the physical dimensions of the pixels on the upper and lower surface edges of the mechanical parts in the image taken by the camera are different, resulting in the deviation of the actual measurement results of the mechanical parts In this paper, a measurement method based on unit pixel size compensation is proposed. This method compensates the measurement data by the distance between the upper and lower surfaces of the mechanical parts and the camera, thus reducing or even eliminating the measurement error caused by the thickness of mechanical parts. The simulation results show that, under the same measuring platform, for the mechanical parts of different sizes and thicknesses, this method can achieve a significant improvement in measurement accuracy.
Research on “Internet +” Rural Precision Poverty Alleviation and Sharing Financial Model
Authors: Li Qiutong , Yang Chuansheng
Published: March 2020

Internet technology has many advantages and can provide a good tool for all aspects of development. In China’s economic and social development, “Internet +” has become an important strategy at present. The work to the “Internet +” has become a new development trend. The use of modern technologies such as computer networks has been put on the agenda. After continuous exploration and comparison, the shared service platform is a relatively good mode of operation, and a financial sharing model has been formed in the financial aspect. Financial sharing can significantly reduce the cost of precision poverty alleviation, thus accelerating the process of standardization of rural poverty alleviation. Based on the background of “Internet +”, this paper discusses the new model of rural poverty alleviation and analyzes the framework model of rural poverty alleviation. In the context of “Internet +”, poverty alleviation work combined with computer Internet technology, using computer networks and other modern technologies to carry out rural precision poverty alleviation work, has become the development direction of application of new technologies in the era of computer networks. This article explores financial sharing and social development to explore the advantages of financial sharing and how it affects financial management; It makes full use of the convenience brought by high-tech development to the government service work.
Analysis on Dietary Behavior and Dietary Intake of Children in Chongqing City, China by SPSS Statistical Software
Authors: Tan Fang , Zhao Xin , Huang Qianqian , Zhang Jiaqiong
Published: March 2020

In order to understand the current situation of children’s dietary behavior and its influencing factors in Chongqing City, China, this study investigated the dietary behavior of children in Chongqing City by random sampling of 2200 parents of children in the main urban area of Chongqing City by questionnaires. Through SPSS statistical software analysis, it was found that children’s dietary behavior problems were prominent, 83.5% of children had food concentration problems, 57% of children had selective eating behavior. In addition, children’s dietary preferences are also uneven. It can be seen from this that families, kindergartens and all sectors of society work together to carry out the education of infants’ dietary behavior, so as to cultivate infants’ scientific and healthy dietary behavior.
Prediction model of household appliance energy consumption based on machine learning
Authors: Xiang Lei , Xie Tao , Xie Wei
Published: March 2020

This paper presents and discusses the prediction model of household appliance energy consumption. The paper discusses data filtering to remove non-predictive parameters, and used for model training. Five prediction models are trained with repeated cross validation and evaluated in the testing set: support vector machine (SVM), k nearest neighbor (KNN), random forest (RF), extreme random forest (ERF), and long short-term memory network (LSTM). Among them, the LSTM, which is a deep learning method, behaves the best performance. LSTM has the highest R2 (0.97) and the lowest RMSE (21.36) in the testing set. The analysis results show that the deep learning method has advantages in the prediction of household appliance energy consumption.
Accounting Professional Orientation and Talent Training Based on Artificial Intelligence
Authors: Guo Wei-wei , Li Ting
Published: March 2020

with the emergence of artificial intelligence technology, it is possible for financial robots to replace traditional accounting work. The paper analyzes the influence of artificial intelligence technology on accounting practice, accounting personnel and professional orientation, and puts forward the training direction of accounting talents.
Compact Prediction Tree (CPT) Application Research on the Causes of Faults in the Use of Multimedia Classrooms in Colleges and Universities
Authors: Bai Haicheng , Yang Shuai
Published: March 2020

In addition to maintaining equipment, the management of multimedia classrooms in colleges and universities is mainly to provide timely assistance to teachers. When a failure occurs, if the cause can be automatically predicted and online real-time assistance is provided in time, this will greatly enhance the teacher teaching experience. Series prediction is one of the hotspots of deep learning research, for it has been widely used in many fields from recommendation systems to speech recognition to natural language processing. The Compact Prediction Tree (CPT) is faster and more accurate than other sequence prediction models. Based on this, this paper studies how to generate prediction trees for multimedia fault text events in colleges and universities, and uses CPT to predict the causes of multimedia classroom failures to improve management efficiency and user experience.
Blink Detection Based on Pixel Fluctuation Ratio of Eye Image
Authors: Dou Dou , Zhang Zhaogong
Published: March 2020

In this paper, an algorithm of real-time eye blink monitoring using ordinary camera is proposed. This algorithm is based on pixel fluctuation ratio (FRP) of eye region image to detect eye blink. Position the eye area according to the facial landmark algorithm and then convert the image to a grayscale image. According to the point detected by the eye region in the grayscale image, the eye region is divided into three segments from the corner of the eye to the end of the eye. Calculate the value of the pixels in each segment, and find the partial image pixel value fluctuation ratio (FRP), and train the selected training samples with the support vector machine (SVM). We tested it on two data sets, and the blink detection algorithm achieved good results on both data sets.
Application of Computer Information Technology in Modern Distance Education System
Authors: He Lifang
Published: March 2020

The key to the application of computer information technology in distance education system is how to effectively apply the advantages of information technology to better achieve the goal of curriculum learning, cultivate students’ information literacy, innovative spirit and time ability. Computer information technology does play a huge role in distance education. And good teaching resources have laid a solid foundation for teachers to implement distance education, attaching importance to the construction of teaching resources. This paper introduces the necessity of applying computer information technology in modern distance education system and its application in modern distance education.
Application of New Relay Protection Technology in Agricultural Power System
Authors: Li Aichuan
Published: March 2020

With the continuous development and progress of society, information technology and computer technology have also been highly developed and improved. With the support of technology, new technologies of relay protection system have emerged, which has great significance and role for the effective operation of power system. Especially for the rural power system, the emergence of new relay protection technology has brought many benefits, solved some problems in the rural power system, and guaranteed the effectiveness and security of the rural power system. This paper mainly analyses what is the new technology of relay protection, what specific application of the new technology of relay protection in agricultural power system and the future development trend of the new technology of relay protection.
A Comparison on Data Augmentation Methods Based on Deep Learning for Audio Classification
Authors: Wei Shengyun , Zou Shun , Liao Feifan , lang weimin
Published: March 2020

Deep learning focuses on the representation of the input data and generalization of the model. It is well known that data augmentation can combat overfitting and improve the generalization ability of deep neural network. In this paper, we summarize and compare multiple data augmentation methods for audio classification. These strategies include traditional methods on raw audio signal, as well as the current popular augmentation of linear interpolation and nonlinear mixing on the spectrum. We explore the generation of new samples, the transformation of labels, and the combination patterns of samples and labels of each data augmentation method. Finally, inspired by SpecAugment and Mixup, we propose an effective and easy to implement data augmentation method, which we call Mixed frequency Masking data augmentation. This method adopts nonlinear combination method to construct new samples and linear method to construct labels. All methods are verified on the Freesound Dataset Kaggle2018 dataset, and ResNet is adopted as the classifier. The baseline system uses the log-mel spectrogram feature as the input. We use mean Average Precision @3 (mAP@3) as the evaluation metric to evaluate the performance of all data augmentation methods.
Adaptive Robust of RBF Neural Network Control Based on Model Local Approximation Method for Upper Limb Rehabilitation Robotic Arm
Authors: Li Yafeng , An Aimin , Wang Jinlei , Zhang Haochen , Meng Fancheng
Published: March 2020

For the problem of patient spasm disturbance and random disturbance of external environment in rehabilitation process of upper limb rehabilitation arm, Considering the approximation ability of neural network for arbitrary functions, an adaptive robust of radial basis function (RBF) neural network control algorithm based on model local approximation is proposed. This control algorithm introduces robust term to reduce the approximation error of neural network and the robustness of the rehabilitation manipulator control system is improved. The system can also obtain good track tracking performance under the condition of patient spasm disturbance and random disturbance of external environment. The asymptotic stability of the control system is proved by the stability theory of Lyapunov. Simulation results show that the proposed control algorithm has good control performance.
Track detection system of UAV based on line structure light
Authors: Huang Ruiting , Jia Mengyao , Guo Simeng
Published: March 2020

Aiming at the present problems of strong subjectivity and low efficiency of manual push flaw detection vehicle, in order to meet the requirements of high-speed rail detection, this system uses UAV aircraft combined with line structured light technology to realize a track detection system based on multiple sensors. In the method of joint positioning, after coarse positioning of the UAV through satellite positioning, the camera on the UAV is used to identify the image of the track to complete accurate positioning and single-track tracking. The high precision and fast scanning speed of structural light are used to obtain the profile data of rail. The obtained measurement data were calibrated by combining the mathematical model established with the track CAD model to eliminate the influence of the UAV’s self-disturbance on the acquired data and achieve the acquisition of rail wear data. At the same time, the computer vision technology is used to extract the window in the track, and the track intrusion detection is carried out through the analysis of the gray map distribution in the window.
Machine Learning Based Epileptic Seizure Detection for Responsive Neurostimulator System Optimization
Authors: Shen Yangchicheng
Published: March 2020

This paper proposes a novel method of identifying the time of epileptic seizure happening on patients by employing feature extraction and machine learning-based classification on Electroencephalogram (EEG) signal collected from a closed-loop interface implanted in the brain of patients. The closed-loop device was served as a neurostimulator which introduced stimuli to epilepsy patients when detecting the occurrence of seizure. A set of multiple time- and frequency-domain features are extracted from intracranial electroencephalography recordings of 7 subjects with epilepsy. Trained and tested on the extracted features, an ensemble of machine learning models with parameter tuning achieves an area under the curve (AUC) score of 0.99.
Study on Influence of Opening Roads in Community on Urban Traffic Planning and Scheduling
Authors: Sun Ziqiang
Published: March 2020

With the development of modern economy and society, the urban population grows and the number of motor vehicles keeps increasing. The traffic jam follows up, and has become a common problem in many cities, especially in the large cities. How to plan and schedule in advance is an intelligent planning problem. Reasonable planning and scheduling can effectively alleviate the urban traffic jams. In this paper, AHP method is used to establish a hierarchical structure, and demonstrate the effect of opening roads in community in different time by the qualitative to quantitative principle. In view of this, this paper gives rational suggestion of the opening roads in the community, and provides reference for the construction of the new intelligent living community in the future.
Research on the application of VR technology in the digital inheritance of ancient papermaking technology
Authors: Zhong Lishi , Zhong Zhou , Peng Xiaopeng
Published: March 2020

The traditional papermaking technique of ancient method is confronted with the problem of difficult inheritance. In order to enable the public to experience or learn the ancient papermaking technology with the help of virtual platform and make the ancient papermaking technology have more possibilities for future development, the VR experience project of ancient papermaking technology is designed and planned through data collection, virtual scene production, digital interactive design and other steps by means of integrating disciplines. It can retain the integrity of the information of ancient papermaking technology to the greatest extent, provide a safe, efficient and green simulation learning platform, rapidly promote the traditional ancient papermaking culture in a large range, enrich the presentation of ancient papermaking materials and predict the future development direction.
Construction Research on Index System of Quality Evaluation of Labour Education in China
Authors: Sun Xi
Published: March 2020

With the development of modern economy and society, labour education has been gradually weakened. In recent years, the declining status of labour education position in schools and colleges has weakened the function of labour education, resulting in the quality decline of labour education. The contemporary students have many problems in the aspects of labour quality and labour ability. Therefore, improving the quality of labour education in China has important historical and practical significance. This paper gives an explanation of the importance of labour education to personnel training, and analyses the innovation embodiment of labour education. On this basis, this paper constructs the evaluation index system of labour education quality in China, which consists of six criteria levels and twenty-four program levels. According to the Analytic Hierarchy Process, the paper evaluates the weight of each scheme layer, constructs a complete evaluation index system of labour education quality in China, and provides some references for relevant researchers.
Safety Assessment System of Launching Tower Structure with Multi-information Fusion and Data-driven
Authors: Gu Yufei , Chen Shaojiang , Zhang Lijun , Jia Hongliang , Xu Feiran
Published: March 2020

In order to monitor and evaluate the impact of rocket launching shock, strain, vibration, typhoon, earthquake and other loads on the launch tower, an online monitoring and evaluation system was established based on multi-sensor information fusion technology in a space launch site. Firstly, the system integrates the information of vibration, pressure, deformation, wind load, temperature and other sensors to comprehensively analyze and evaluate the safety of tower structure, and realizes health diagnosis. Then use 3dmax to draw the 3D simulation model, and realize the real-time 3D display of various monitoring and analysis results based on the 3D visualization and data-driven technologies.
Access control mechanism based on image recognition and user level
Authors: Qi Haojin , Wang Yong , Yang Yueping , Yu Xiaodie , Ju Xiaoming
Published: March 2020

The rapid development of big data and the Internet has brought enormous challenges to data security. Aiming at protecting the sensitive image data protection and secure access of information, an access control mechanism based on image recognition and user level is proposed. On the one hand, the mechanism is based on deep learning, mining sensitive information in images, realizing hierarchical classification and precise positioning of sensitive areas in images; on the other hand, combining identity authentication to achieve user management based on identity level; The image sensitivity level is mapped with the user identity level, which realizes the user’s access authority control, prevents unauthorized or illegal access of sensitive image information, and effectively protects sensitive image information.
A Social Mobilized Inspection System against External Damage of Power Grid Based on Block Chain Technology
Authors: Bing HE , Ren-de LI , Wang Shan-shan , Jian-guo LIU
Published: March 2020

The operation and inspection of external damage of power grid is becoming more and more difficult, mainly reflected in the uncertainty of risk and insufficient manpower. At present, most of the on-site inspection rely on human inspection since new technology cannot respond to unexpected situations. In this paper, we proposed a social mobilized inspection system. By mobilized the social forces around power facilities, we propose an inspection framework to encourage people upload photos of external damage. To ensure the incentive system transparency, we use block chain technology as the decentralized database. This method provides a new insight against external damage of power grid and is hopefully to be used in inspection system.
A Simple Dual-Frequency Circularly Polarized Slot Antenna With a Modified Crossed Patch
Authors: Hua Lu , Chen Tingting , Liu Hengxiang
Published: March 2020

A simple single-layer single-fed dual-frequency circularly polarized(cp) antenna with a crossed patch is proposed in this paper. The modified crossed patch with dual-frequency cp provides a new method for the design of other multi-frequency cp antennas. A horizontal stub is introduced at the upper end of the long arm and sharp angles protrude from both ends of the short arm to achieve dual circular polarization at low frequency (1.64 GHz) and high frequency (3.45 GHz), respectively. The antenna proposed in this paper can be applied to WIMAX with a compact size of 53 × 60mm2. The experimental results show that the impedance bandwidth of 250 MHz (1.55 GHz-1.8 GHz) and 1440 MHz (2.2 GHz-3.64 GHz), and the 3 dB axis-ratio bandwidth of 250 MHz (1.52 GHz-1.78 GHz) and 520 MHz (3.08GHz-3.6 GHz) are achieved in these two bands respectively. The proposed antenna has been printed and fabricated, and the measured results are in good agreement with the simulated results.
Defect diagnosis technology of typical components on transmission line based on Fully Convolutional Network
Authors: Zhenyu LI , Wanguo Wang , Tao LI , Guangxiu LIU , Zengwei LI , Yuan Tian
Published: March 2020

This paper presents an intelligent defect diagnosis algorithm based on fully convolutional neural network for typical transmission line components. Based on the region-based fully convolutional neural network algorithm and combined with deformable convolution, feature context fusion, clustering, the feature expression ability of neural network is improved. Through the improvement, the algorithm adapts to the object deformation and scale difference. In addition, the training effect is improved by improving the sample labeling strategy. In this paper, the defect diagnosis of four kinds of transmission line components(insulator, vibration damper, grading ring, wire clamp) is realized.
Influencers Identification in Weighted and Directed Networks Based on k-layer Decomposition
Authors: Cai Yuecheng , Zhou Wen
Published: March 2020

In recent years, complex network research has received wide attention from scientific and industrial circles in different academic fields owing to its high application value. In the field of complex network research, identifying influencers has far-reaching implications for various real-world propagation dynamics, such as epidemic outbreaks, and cascading failures. In this paper, we analyse the node-specific attributes of weighted and directed networks and propose two new influencers identification indices for weighted and directed networks. For weighted networks, in k-layer decomposition, weighted degree is used in the algorithm instead of degree as the basis for decomposition. For directed networks, we separately consider the effect of in-degree and out-degree on network decomposition and propose directed k-layer (KLD) indicators that are more suitable for directed networks. Thereafter, a Susceptible-Infected-Recovered (SIR) model and network robustness evaluation methods are used to compare and evaluate the proposed indices on multiple real-world complex network datasets. We found that using the proposed indices resulted in improved node ranking ability for weighted or directed networks.
Application Research of Transmission Technology in Information Communication Engineering
Authors: Jiang Meng
Published: March 2020

In the planning, design and concrete construction of information communication engineering, transmission technology has an irreplaceable role in the transmission system because it has very important influence and effect. The overall application effect of the transmission technology will directly affect the basic situation of the entire information communication project during construction and utilization.Therefore, this paper analyzes the specific application of transmission technology in information communication engineering, and lays a good foundation for improving the efficiency of information communication engineering.
Research on Winding Deformation Detection and Diagnosis Technology of Power Transformer
Authors: Lu Wei , Ma Jun , Ma Bo
Published: March 2020

With the continuous increase of power grid capacity, transformer damage accidents caused by short-circuit faults are on the rise. Timely and effective measurement of transformer winding deformation can effectively prevent further deterioration of transformer deformation and ensure that transformer does not occur accidents. This paper summarizes the methods, principles, influencing factors and judging criteria of winding deformation detection, and summarizes the important research results in the field of transformer winding deformation in recent years. Finally, this paper compares different detection methods from the aspects of detection methods, influencing factors of test results, detection information, detection sensitivity, deformation judgment methods and detection standards.
Multilayer WFRFT and chaotic encryption wireless communication system based on MIMO
Authors: Zhuang Lei , Hu Jing , Cheng Jian
Published: March 2020

In order to improve the concealment and anti interception performance of satellite and other wireless communication.Based on the characteristics of multilayer weighted fractional-order Fourier transform and chaotic mapping, a multilayer weighted fractional-order Fourier transform and chaotic mapping wireless communication system is designed. The number of modulation layers of multilayer WFRFT is consistent with the number of transmitting antennas, and different modulation orders are adopted in each layer, which makes it more difficult to crack the information by chaotic mapping scrambling codes on the modulated signals. The simulation results show that the designed system has better concealment performance by comparing the bit error rate of the receiver with that of the eavesdropper. The bit error rate of the eavesdropper is about 0.5 when the eavesdropper does not know the specific parameters.
Design of an Intelligent Leakage Prevention System for High pressure Injector
Authors: Zhengdong LI , Xiuling LI , Haimei XU , Juan CAI , Xiaoling XU , Yan HE
Published: March 2020

In order to solve the problem of liquid leakage due to high injection pressure and fast injection speed in the current medical high pressure injector, a set of intelligent anti-leakage diversion alarm system for high pressure injector was designed by using TI microcontroller MSP430F4619, precision electronic FS2050 pressure sensor and Nordic company’s NRF24L01+ wireless transmission chip. The system can monitor the process of high pressure injection in real time. Once the leakage of drug solution is detected, wireless acousto-optic alarm will be given immediately and diversion switch will be made quickly. This system not only overcomes the disadvantage of the previous mechanical design scheme which is easy to be loosened, but also increases the function of diversion switch which can effectively reduce the pain of patients. The results of animal experiments show that the intelligent diversion alarm system is stable, reliable and highly sensitive, which can effectively reduce the leakage of drug solution in high pressure injector.
Methods for High Voltage Transmission Line Detection Based on Image Recognition
Authors: Qiang Liu , Fangbo Gong , Zhiming Jiao , Juning Li , Xin Feng , Lichuan Wang
Published: March 2020

With the development of national economy, the demand for electric power from all walks of life is expanding. How to prevent and discover the natural disaster and accident that harm the transmission line in time has become an important basic work to guarantee the power supply. Image identification and automatic alarm system of transmission lines adopts image technology to monitor and analyze and process transmission lines, which can automatically identify and alarm the safe operation of transmission lines, provide basis for scientific decision-making of power enterprises, and improve response efficiency of natural disasters and accidents. Based on the research direction of identification and detection of transmission lines, this paper summarizes the commonly used methods of detection of transmission lines.
Weighted network community division based on co-neighbor nodes and similarity
Authors: Xiaoxia LUO , Xin SUN , Xiangyu LUO , Yunzhong LUO
Published: March 2020

In order to realize the accuracy of weighted network community division, based on the analysis of existing weighted network community dividing algorithm, a community dividing algorithm based on co-neighbor nodes and similarity is proposed. Firstly, the similarity between nodes is defined, and the weight of the edge is added to the local modularity function. Secondly, the Burt structural hole principle is used to select the important nodes. Then, the local clustering method is used to continuously expand the community. Finally, the judgment is made. The change of the Local modularity determines whether to continue the clustering, so as to obtain a more accurate result. Experiments on the simulation dataset and Zachary’s Karate club dataset show that compared with the CRMA algorithm, the algorithm is effectively improved in weighted network division time and accuracy.
A distortion image correction method based on machine vision
Authors: Tan Guangxing , Ding Ying , Fu Dandan , Wang Yuchen
Published: March 2020

As autopilot becomes a hot spot in the field of automobile, machine vision is widely used in autopilot technology, and calibration of camera parameters is the basic work of obtaining driving environment by using vision. In this paper, based on camera parameters and machine vision technology, the monocular camera image distortion correction algorithm is studied. Based on the perspective transformation principle, the method of controlling transformation point is used to correct the image. The black-and-white checkerboard is taken as the object of image acquisition, and the results obtained in this paper are compared with those obtained by the calibration toolbox camera calibrator of matlab camera. The algorithm proposed in this paper is used to correct the images collected in traffic environment, which lays a foundation for the subsequent recognition of traffic signs. The experimental results show that the distorted image correction results are good, the accuracy is higher, and it is practical and effective.
Edge detection methods and key parameters study
Authors: Li Qingyu
Published: March 2020

Edge is essential for diverse vision processing tasks and it constitutes a crucial part in the image of objects. There has already been a huge amount of researches about edge detection algorithms. The Canny method and the Sobel method are the most widely used techniques because of their relative advantages. Some parameters can drastically affect the performance and results of the two methods, which will be discussed in this paper. In addition, noise is also a factor affecting the number of the extracted edges. Experiment results have demonstrated that the noise filter will achieve better edge detection performance. Further improvements with suitable parameters of different applications for the two methods are possible.