bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection of Spike-type Stall of Axial Compressors Based on Dilated Causal Convolutional Neural Networks
Authors: Zhao Hongyang , Quan Fuxiang , Xia Weiguo , Sun Ximing 
Published: December 2020

Abstract
An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as spike-type stall inception in axial compressors is observed in aero-engine. In this paper, a deep dilated causal convolutional neural network(CNN) named as WaveNet is applied to spike-type stall inception detection and prediction in time-series data of axial compressors. WaveNet can implement fast anomaly detection and spike-type stall prediction in long-time term series data. Furthermore, a single WaveNet can be trained to capture and learn the time-domain statistical characteristics of different spike-type stall inception training data with equal fidelity. The trained WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of spike-type stall detection approach based on WaveNet.
Economical CMM Universal Minimal I++DME System Design
Authors: Liu Kai , Mu Hua , Pang Changtao , Bai Yuewei 
Published: December 2020

Abstract
With the development of motion control technology in recent years, more and more low-priced motion control cards have been adopted by small and medium-sized CMM manufacturers due to their cost-effective quality. However, most of these cards are not packaged as the standard I++DME. As a result, the CMM equipment has a disadvantage of compatibility. In addition, I++ is a tremendous superset instruction, that can support from basic measurement to scanning measurement and rotary table measurement and many other complex commands. So packaging the economical CMM to support all I++ standard instructions is entirely unnecessary for a cost-effective reason. So this article wants to pick out the necessary I++ instructions to build a minimal I++DME that can support manual or automatic measurement.
A topology-based optimization method for software-defined satellite network control
Authors: Qiao Wenxin , Lu Hao , Li Zhiwei , Chen Xingkai , Shi Runze 
Published: December 2020

Abstract
Satellite network is gaining significant attention, and software-defined networking architecture is more and more popular as applied in many traditional networking fields. In order to meet the diversified user service requirements and the continuously expanding application scenario requirements, the research of software defined satellite network combining satellite network and software definition has gradually become a research hotspot. Aiming at the problem of how to optimize the control function of software-defined satellite network, this paper proposes a control optimization method based on topological structure. By quantifying the controllability of the network structure and eliminating the special characteristic structure, the controllability of the network is improved, and then the control function of software-defined satellite network is optimized. The simulation results show that the controllability of the network is improved by the method.
Energy-Efficient Joint Computation Offloading and Resource Allocation in Multi-User MEC Systems
Authors: Meng Yao , Dai Janxin 
Published: December 2020

Abstract
Mobile edge computing (MEC) is envisioned as an emerging paradigm to enable energy-constrained and computation-limited user equipments (UEs) to offload various computation tasks to the edge of mobile networks in order to save energy consumption and prolong the battery life of UEs. In this paper, we consider a multi-user MEC system where each UE has a computation task to be processed locally or offloaded for remote processing. Specifically, we investigate the task offloading problem with the aim of minimizing the energy consumption of UEs via jointly optimizing the task admission decision, the transmission power, local computing and edge computing capacities. To solve this nonconvex problem, we transform it into four subproblems and propose a low-complexity algorithm by solving subproblems in an iterative manner. Simulation results demonstrate that the proposed algorithm requires lower energy consumption than the existing benchmark schemes.
An effective method for computer network anomaly detection
Authors: Wang Dongpo , Zhao Chengli , Zhang Xue , Liu Boyu , Li Xiang , Yi Dongyun 
Published: December 2020

Abstract
With the increasing network threat, network anomaly detection has become a very challenging and indispensable task. In this article, we propose an anomaly detection algorithm through modeling computer network as temporal network. The active subnetwork is extracted from the original computer communication network and then projected to an undirected weighted network series, finally the abnormal behavior patterns of network series are detected based on eigenvectors. Data test experiment shows that the proposed algorithm has achieved very good results.
Research on the Application of Deep Learning in Text Generation
Authors: Zhou Shuohua 
Published: December 2020

Abstract
In recent years, deep learning technology has made great progress, and text generation based on deep learning has received extensive attention. In the form of complex and diverse information, text information is a mainstream form of data, and its number is growing very fast. How to quickly and accurately locate and use effective information from massive text data has become an urgent research problem in the field of text information extraction. The deepening of deep learning technology has broken through the dependence of traditional natural language generation technology on templates. It can automatically learn the input to output mapping from the data to form an end-to-end solution and reduce the degree of human participation. It enables the generation system to generalize in a wider field, and can generate more free text under the given conditions according to the needs. This paper explores the text generation method based on deep neural network, conducts research work on the text summary task, designs a generative summary generation method based on improved cluster search, and conducts experiments. Experimental results show that this method is effective.
A Distilled BERT with Hidden State and Soft Label Learning for Sentiment Classification
Authors: Wei Shuyong , Yu Defa , Lv Chenguo 
Published: December 2020

Abstract
BERT is a pre-trained language model. Although the model is proven to be highly performant in a variety of natural language understanding tasks, its large size makes it hard to implement in practical situation where computing resource is limited. In order to improve the model efficiency of BERT for sentiment analysis task, we propose a novel distilled version of BERT. It distills knowledge from the full-size BERT model, which serves as the teacher model. The distilled model efficiently learns the last hidden state and soft label of the teacher model, which are different from previous models. We use distillation learning objective that is able to effectively transfer knowledge from the original big model to the compact model. Our model reduces BERT model size by ∼40%, but retains ∼98.2% of performance in sentiment classification task. Our model achieves promising results in SST-2 sentiment analysis, and outperforms previous distilled model.
AFCGDroid: Deep Learning Based Android Malware Detection Using Attributed Function Call Graphs
Authors: Lu Tong , Liu Xiaoyuan , Chen Jingwei , Hu Naitian , Liu Bo 
Published: December 2020

Abstract
With the rapid development of the mobile network, large numbers of Android malware emerge and pose a serious security threat to Android users. The function call graphs (FCG) extracted from Android application involves permissions, API calls, and structure semantics. Leveraging FCGs has great potential for Android application detection. In this paper, we propose AFCGDroid, an approach based on attributed function call graph (AFCG), to detect Android malware. The nodes of FCGs are divided into internal nodes and external nodes in our work. AFCGDroid extracts FCGs through static analysis and then generates AFCGs by labeling its internal nodes with the external nodes. To handle the graph data, graph embedding based on deep learning is proposed to embed the AFCGs into low dimension vectors. We evaluate AFCGDroid in malware detection achieving a 99.6% accuracy in a publicly available dataset, Drebin.
Improved SVM classification algorithm based on KFCM and LDA
Authors: Zhang Xiaoyan , Wang Mengjuan 
Published: December 2020

Abstract
To address the problem that SVM is sensitive to outliers and noise points, in order to improve the classification accuracy of SVM, this paper introduces fuzzy theory and intraclass dispersion theory, proposes an improved SVM classification algorithm, uses KFCM and LDA to filter the data set, and selects reasonable training samples, thereby reducing the number of wild points and noise points in the training sample, and thus reducing its impact on the classification effect of the classification model. Compared with the traditional SVM, the algorithm in this paper considers the impact of training samples on the classification effect, introduces fuzzy theory and intra-class dispersion, and eliminates the wild points and noise points in the training samples that affect the classification accuracy of the classification model. Experimental verification shows that the classification accuracy of the SVM classification model trained by the filtered training samples is higher than that of the SVM classification model without the trained training samples.
The Impact of Artificial Intelligence on Vocational Education and Countermeasures
Authors: Hui Feng 
Published: December 2020

Abstract
Under the influence of manual functions, vocational education is facing unprecedented challenges and opportunities, and it also increases the employment pressure of vocational education graduates. Due to the emergence of artificial intelligence, the labor market has stricter requirements for employees, and graduates with high professional quality and high comprehensive level will be selected. Therefore, as a vocational education college, it should correctly understand the trend of artificial intelligence, attach great importance to the influence and role on the development of vocational education, connect artificial intelligence with the reform and innovation of vocational education, and scientifically and rationally use artificial intelligence teaching methods. It is used in daily teaching to cultivate high-quality talents for the country and society in line with the development of the times. This article will analyze the impact of artificial intelligence on vocational education and countermeasures, hoping to improve the teaching quality and level of vocational education.
Research on Medical Image Registration Based on Graphic Neural Network Reinforcement Learning
Authors: Dong Zhejia 
Published: December 2020

Abstract
On the basis of traditional medical image registration methods, aiming at the shortcomings of rigid body images, sequential rigid body images and non-rigid body images registration methods, this paper proposes a new method of medical image registration application based on graphic neural network reinforcement learning. Reinforcement learning adjusts the strategy from the actual system learning experience, and it is a process of gradually approaching the optimal strategy, mapping the image from high-dimensional gray space to low-dimensional feature category space; Then, the elastic registration of mutual information images is carried out in the feature class space. The advantage of this method is that it does not need extra supervision information, which effectively saves manpower and material resources. So as to make an accurate diagnosis or make a proper treatment plan.
The Role of Emotion in Designing Self-tracking Visualization: Where Avatars are Better Choice than Dashboard
Authors: Wu Xue , Liao Han-Teng , Wang Zijia 
Published: December 2020

Abstract
The interface design of self-tracking technology still has a lot of room for improvement. At present, self-tracking products with the dashboard as the main interface face challenges in negative user experience and high abandon. In this paper, we try to improve the user experience of such technologies by paying more attention to the role of the user’s emotions on the visualization and enhance their willingness to continue to use. Based on our previous study on people-centered self-tracking system design, we think the avatars should be a better choice than dashboards of self-tracking technologies. In order to detect whether avatars can bring more positive emotions than the dashboards, and to reduce abandon, we designed and implemented three phases of usability testing: a questionnaire of popular self-tracking applications in the market, a self-tracking product emotional research based on avatar and dashboards, and a questionnaire based on self-tracking APP prototype with avatar template. Through these usability testing, we obtained results that users are more tend to adopt the avatar one. This paper then proposed results that 9 emotions are significantly different when using these two different interface designs of self-tracking visualization. With such results of research, two implication for principles of sustainable interaction design of self-tracking applications had been proposed in this paper as 1) Understandability; 2) Performance optimizations. We hope to provide a new form of visualization for the interface design of self-tracking technology and complement usability research results of such design.
Fast 3D reconstruction based on UAV image
Authors: Pang Qiaoyu , Gui Jianjun , Li Jing , Deng Baosong 
Published: December 2020

Abstract
3D reconstruction for outdoor large environment, especially with high efficiency and robustness, has triggered increasing attention in both civilian and military scenario. This paper proposes a rapid method to perform 3D reconstruction using openMP multithreading strategy. The GPS information will be adopted to determine the adjacent relationship between images. The map will be processed through mesh blocks to save the computational time, and will be finally optimized by the mesh dissecting algorithm to remove holes. The experimental results show that the processing speed has been increased as five times faster than the methods based on openMVG and openMVS with better reconstruction outputs.
An Island Remote Sensing Image Segmentation Algorithm Based on A Fusion Network with Attention Mechanism
Authors: Chen Tianyuan , Wang Hongfei , Liu Hao , Wu Peng 
Published: December 2020

AbstractWith the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, in view of the problems that remote sensing images contain richer ratio information and complex background interference, we propose an island remote sensing image segmentation algorithm based on a fusion network with attention mechanism, called AFU-Net. The network is built on the basis of FC_U-Net[1]. An attention mechanism is added to pre-weight the shallow features before deep-shallow layer feature fusion, in order to enhance the response capability of the target features, suppress the background interference and improve the segmentation accuracy of the network. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of AFU-Net are greatly improved compared to FC_U-Net, and are also superior to other three state-of-the-art methods, U-Net, FCN, SegNet, which indicates the effectiveness of our method.
Synthesis of 3D pronunciation trajectories of lips in Mandarin Based on HMM model
Authors: Ye Yuanzhi , He Xiangzhen , Zhang Yihao , Hu Yerong , Zeng Jia 
Published: December 2020

Abstract
In the era of information technology, human-computer interaction tendings to be multi-modal, and lip animation simulation driven by pronunciation actions has attracted more and more attention. Starting from Mandarin Chinese, this paper establishins a model of the trajectory of the lips during the pronunciation process. The model is based on a motion capture technology, proposes a database collection a processing method, extracts acoustic parameters and text information to establish an HMM prediction model of pronunciation action parameters, and synthesizes the pronunciation movement trajectory, and the average percentage error with the real trajectory is less than 3.42%. The results show that the predicted pronunciation action parameters can effectively synthesize the lip pronunciation movement trajectory, and the use of lip animation can enhance language understanding.
Knowledge aided expert system for failure analysis of electronic components
Authors: Yaru Cui , Kui Zhang , Lijing Yin , Shengbiao An , Jie Huang 
Published: December 2020

Abstract
Aiming at the importance of failure analysis of electronic components, the complexity of the knowledge system and the high dependence of experts, this paper conducts in-depth research on the application of expert systems. According to the characteristics of component failure analysis, the reasoning fault tree analysis engine is innovatively designed, and the active knowledge assistant technology is introduced to assist analysts to obtain failure mechanism information in real time. The low threshold and high output of failure analysis of electronic components are realized, which fills the gap at home and abroad.
A Robust Facial Landmark Detection in Uncontrolled Natural Condition
Authors: Wang Ping , Zhou Qianyu , Zhao Yucong , Zhao Shumin , Ma Jiquan 
Published: December 2020

Abstract
Facial landmark detection in uncontrolled natural condition is a challenging task due to illumination, occlusion, expression, posture, blur and other factors. In order to make a stable and accurate prediction, a novel data augmentation strategy based on random mask is used to obtain an efficient training data set. Our work is motivated by the ability of artist’s spatio imagination from part to whole. The artist can find the corresponding key points according to the incomplete image, so as to realize the whole from the part. In the experiment, the corresponding incomplete image is first generated based on the random mask and the complete image, and then the incomplete image is fed into the neural network to fit the mapping between the incomplete image and the facial landmarks. In order to evaluate the performance, a simple regression convolutional neural network based on the improved VGG16 [1] was trained, and the latest technical results were obtained. Furthermore, our method is tested over both complete and incomplete face images. Extensive experiments reveal that our model can work well on different human face data set.
Multi-modal human-computer interaction system in cockpit
Authors: Ren Jie , Cui Yanyan , Chen Jing , Qiao Yuanyuan , Wang Luhui 
Published: December 2020

Abstract
In order to explore new human-machine interaction methods, a set of multi-modal human-machine interaction coordinated control system is proposed, which realizes the basic flight control based on the change of the pilot’s field of view, touch control, voice control and other information obtained from multi-mode coordinated control. This system introduces a new type of human-computer interaction into the cockpit application on the basis of the human-computer interaction interface for flight driving, and has carried out research on multi-mode collaborative control system, mainly including eye movement interaction, touch interaction gesture Interaction and voice interaction. Finally, this project formed a multi-mode cooperative control software and hardware system.
Automatic high-precision measurement technology of special-shaped surface parts based on robot arms
Authors: Jiang Zhiguang , Zhang Lijian , Qiu Tiecheng 
Published: December 2020

Abstract
In order to meet the requirements of large-scale, special-shaped surface, high-precision surface measurement of product parts, an automated, high-precision robot arm measurement system based on industrial robots is proposed. According to the D-H method, the relative posture conversion relationship between the robot arm and the elastic probe is discussed. In addition, on the premise of considering the contact deformation between the elastic probe and the measuring surface, the coordinate data of the measuring point is obtained based on the probe deformation model. At the same time, the accuracy calibration is performed by means of visual photogrammetry, and the validity of the coordinate measurement data of the profiled surface is guaranteed.
Intelligent obstacle avoidance algorithms for autonomous underwater vehicle
Authors: Liu Hui , Guo Yinjing 
Published: December 2020

Abstract
Real-time obstacle avoidance is the basis to ensure the safe operation of the autonomous underwater vehicle (AUV), and it also represents the intelligent level of AUV. The main factors that affect AUV obstacle avoidance are environment perception ability, obstacle avoidance decision-making ability, and trajectory tracking control ability. This paper starts from the perspective of obstacle avoidance decision-making ability, that is, obstacle avoidance algorithm. Firstly, we summarized the structure and influencing factors of AUV real-time obstacle avoidance. Then, we introduced in detail the research progress of AUV intelligent obstacle avoidance algorithms, including fuzzy logic algorithms, neural network algorithms, and reinforcement learning algorithms. And we analysed the improvement methods of each algorithm from three-dimensional underwater environment, ocean current, AUV motion characteristics, and dynamic obstacles. Finally, we prospected the development of the AUV intelligent obstacle avoidance algorithm.
Research on Load Balancing Control of Internet of Things (IoT) Link Based on Sparse Random Clustering
Authors: Wenxuan Gao 
Published: December 2020

Abstract
Aiming at the problems of poor channel balancing control ability and high output bit error rate in traditional IoT link load balancing control methods, an improved ant colony algorithm based load balancing control method for IoT link is proposed. This paper constructs the channel transmission model of IoT, uses sparse random clustering analysis method to detect the load characteristics of Internet of things links, and extracts the statistical characteristics of IoT link. The output balance control and interference suppression of IoT link are carried out by matching filter detection method, and the load balance of IoT link is fuzzy control combined with fuzzy PID control method. The simulation results show that the proposed method has good self-adaptability, strong channel equalization control ability and lower output bit error rate.