bluuebunny/crossref_metadata_2025_split huggingface dataset:
Review of Research on Task-Oriented Spoken Language Understanding
Authors: Hou Lixian , Li Yanling , Li Chengcheng , Lin Min 
Published: July 2019

Abstract
Spoken language understanding(SLU) is an important function module of the dialogue system. Slot filling and intent detection are two key sub-tasks of task-oriented spoken language understanding. In recent years, the methods of joint recognition have become the mainstream methods of spoken language understanding to solve slot filling and intent detection. Since deep neural network has advantages such as strong generalization and autonomous learning characteristics compared with traditional methods. So far, slot filling and intent detection have been developed from traditional methods to deep neural network methods, and the performance has also been significantly improved. This paper introduces the methods of two tasks from the independent model to the joint model. It focuses on the joint modeling methods based on deep neural network, analyzes current problems and future development trend of two sub-tasks.
De-Noising Algorithm for Flight Data Recording System Based on Modified Ensemble Empirical Mode Decomposition
Authors: Dai Shaowu , Chen Qiangqiang , Dai Hongde 
Published: July 2019

Abstract
The Flight Data Recording System (FDRS) records a lot of parameters of the aircraft during flight, which can be used for the test-flying, training mission of aircraft and so on. Effected by the working environment, information interference and its non-stability, the outliers and noise often exists in the FDRS data. These noises and outliers have a great impact on the use of FDRS. The aim of this paper is to remove outliers and de-noising of navigation data in FDRS. The causes of outliers and noise in FDRS data are analyzed firstly, with a reference suggestion proposed. Then the Letts criterion is used to remove outliers and the Modified Ensemble Empirical Mode Decomposition (MEEMD) is applied to achieve denoising for FDRS. Results demonstrate that outliers are removed and the navigation data are de-noised effectively.
Positioning and Tilt Correction of Milk Production Date Based on Mathematical Morphology
Authors: Lei Fei , Sun Kang , Wang Xueli 
Published: July 2019

Abstract
Milk is one of the most common drinks in people’s lives. Its quality is related to people’s health and safety. Therefore, food regulatory authorities and milk manufacturers are increasingly strict with milk. The production date on the packaging is an important part of milk testing. One. Aiming at the problems of long time and high cost of manual detection, this paper proposes a method based on mathematical morphology for milk production date location and tilt correction based on computer vision related technology. The method firstly preprocesses the collected milk image, then uses the mathematical morphology method to obtain the candidate region of the milk production date, then analyzes the candidate region, extracts the accurate milk production date region, and finally uses the least squares principle. The straight line is fitted to calculate the tilt angle of the milk production date, and the milk image is tilt corrected using the rotation formula. In order to verify the comparison with the Hough transform method, the experiment proves that the proposed method can accurately predict the milk production date and tilt correction.
A Target Detection Method Based on Statistical Mean Value Difference Model
Authors: Lei Fei , Long Kai , Wang Xueli 
Published: July 2019

Abstract
In the field of oil pipeline monitoring applications, the guarantee of high accuracy target detection is required. Aiming at the problem of moving target detection under static camera, a method of mean background model based on probability and statistics is proposed. The background model is quickly constructed. The background threshold is adaptively used to eliminate the influence of illumination changes. The target is detected quickly and accurately. Finally, the proposed target is proposed. The background update method updates the background model in real time. The simulation results show that the better detection results can be obtained under the influence of light changes. The background model has strong stability and adaptability, and can be applied to the real-time monitoring system of the field oil pipeline.
Multi-agent Consensus Problem Based on Observer and Considered Lur’e Nonlinearity in Directed Topology
Authors: Zhou Shaolei , Zhao Xueyuan , Yan Shi , Wang Shuailei 
Published: July 2019

Abstract
This paper studies the multi-agent systems consensus problem considering Lur’e-type nonlinearity under directed network. Compared with the conclusions of the existing undirected communication topology, the conclusions obtained have greatly reduced the communication topology requirements and are more general. This paper designs a control protocol based on the observer, which solves the problem that the system state information is unknown. By decomposition of a specific form of the Laplacin matrix, consensus problem is converted into stability problem for low-dimensional systems. In this paper, the method of solving the control protocol is given and proved by the piecewise Lyapunov function. The simulation results show that the designed control protocol can solve the consistency problem.
Exploring Multiple Embedded Features on Event Extraction
Authors: Yi Shi-Xiang , Li Chun-Yan 
Published: July 2019

Abstract
In recent years, the neural network method can automatically learn effectively features. Unlike traditional discrete features, neural network features are mostly continuous features and can be automatically combined to build higher-level features. The efficiency of the features has been proven in numerous tasks in natural language processing and has led to breakthroughs. In this paper, we propose a event extraction system based on combination of multiple embedded features. Our work is mainly based on the three aspects: (1) traditional pipeline systems have serious error propagation problems; (2) there are several different event descriptions in the text; (3) representation learning can provide rich semantic and syntactic representation. As a result, we achieve competitive performance, specifically, F1-measure of 60.25 in event extraction. Meanwhile, evaluation results point out some shortcomings that need to be addressed in future work.
Common Sensors in Industrial Robots: A Review
Authors: Li Peng , Liu Xiangpeng 
Published: July 2019

Abstract
The application of industrial robots has greatly promoted the development of industry in the past decades. Now with the proposal and prevalence of Industry 4.0, industrial robots are required to be more independent and intelligent to accomplish more complex and flexible tasks. The advancement of industrial robots relies on the development and progress of multiple technologies, among which sensors are the indispensable part. They can acquire abundant information to help industrial robots implement their functions. This paper reviews the recent literatures and gives a summary and introduction of the commonly used sensors in industrial robots. Additionally, the applications of these sensors in diverse functions of industrial robots are also presented. Finally, the developing direction and challenges of industrial robots in the future are discussed in the last part of this article.
An Improved Negatively Correlated Search Inspired by Particle Swarm Optimization
Authors: Qun Niu , Bing Liu , Kecheng Jiang , Han Wang 
Published: July 2019

Abstract
Negatively correlated search (NCS) is a latest intelligence algorithm which maintains multiple individual search processes in parallel and models the behavior of individual search processes using probability distributions. In this paper, an improved NCS inspired by particle swarm optimization (PSO) is proposed, namely PSO-NCS. This algorithm introduces the global best solution (gbest) and personal best solution (pbest) from PSO to calculate the Bhattacharyya Distance (BD) of individuals in the population, which greatly reduces the computational time. Experimental results on six 100 dimensional functions and 10 CEC2005 benchmark functions show that the proposed PSO-NCS has outstanding capability in solving the optimization problems and outperforms the conventional NCS and other methods on most of the test functions. The computational time has been greatly shortened, which saves about 65% in average compared to NCS.
Research on a Fast Continuous Vialog Algorithm
Authors: Jinning Zhang , Yishu Zhou , Huayang He , Lishuo Ren 
Published: July 2019

Abstract
Surface evenness affects vehicle safety, service life, and passenger comfort. The road surface with poor flatness will produce resistance to the vehicle driving, making the vehicle vibrate and bump, hindering the safety of the vehicle driving, and will also cause local water accumulation, speed up the road damage and so on. In order to make the vehicle safe, passengers feel comfortable, road life growth, road flatness detection is particularly important.In order to make the vehicle safe to travel, passengers feel comfortable, the road service life growth, and the detection of road surface evenness is particularly important. In order to overcome the shortcomings of the existing algorithms for pavement evenness, this paper has put forward a new fast algorithm that can be applied to the continuous vialog, and according to the elevation value obtains the evenness standard deviation σ. The results show that the algorithm is high reliability, which provides a reference for the upgrade of instruments and equipment, and makes up for the shortcomings that the continuous vialog cannot be tested quickly.
Research and Optimization of Real-time Simultaneous Localization and Mapping of Indoor Robot Based on Binocular Vision
Authors: Zhang Qiwei , Zhou Guanglu , Wang Pengpeng , Wu Fengguang 
Published: July 2019

Abstract
For the problem of inaccuracy and cumulative error of visual odometer, The research and optimization of real-time Simultaneous Localization and Mapping of indoor robot based on binocular vision are studied. Based on ORB-SLAM2, key-frame map is created. First, the ORB feature is extracted from each frame of the input image and matched by fast approximation nearest neighbour(FLANN). Then, perform the preliminary pose estimation using EPnP, and optimize it with bundle adjustment and key-frame maps. When the tracking fails, apply key-frame maps and bag of words model to relocate. Finally, for the input binocular image, the SGBM is used to solve the parallax and then the depth, which will be converted to radar format data to create a map. In the research and optimization of real-time Simultaneous Localization and Mapping of indoor robot based on binocular vision, propose a method of assisted positioning with key frame map, and a method of feature matching optimization and relocation, which combines various pose optimization to achieve the accuracy of the robot indoors positioning and map construction.
Analysis of Competitive Situation in Basic Research of Global Intelligent Robots
Authors: Rong Li , Ru Liu , Zichao Luo , Jing Liu 
Published: July 2019

Abstract
Practically grasp the global development trend of the basic research on intelligent robots and provide support for the scientific development of intelligent robots in China. Relying on bibliometrics, this paper has proposed a four-dimensional competitive situation analysis system for basic research based on activeness and talent distribution, etc., which can effectively analyze the global situation of basic research on intelligent robots, able to reveal and describe the competition status quo and situation of the basic scientific research in specific fields. There is a big gap between China and the United States in basic research on intelligent robots, mainly including such aspects as follows: uneven distribution of high-level basic research talents; insufficient international cooperation in high-level scientific research; lower research activeness; layout of key research and development.
CNN-LSTM Combined Network for IoT Enabled Fall Detection Applications
Authors: Xu Jun , He Zunwen , Zhang Yan 
Published: July 2019

Abstract
An accidental fall could do a great damage to the health of elderly. Failure to provide timely assistance after a fall may cause injury or even death. In this paper, a fall detection algorithm based on Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) combined network is proposed, which makes full use of the powerful feature extraction ability of CNN and the excellent time series processing ability of LSTM. Data required by the algorithm is only the resultant acceleration from a low cost three-axis acceleration sensor. The experimental results show that compared with the algorithms based on Support Vector Machine (SVM) and CNN, the proposed algorithm has higher detection accuracy with a small data volume, which is very suitable for Internet of Things (IoT) enabled fall detection applications.
Parametric Design of Grasshopper Based on Moulding Characteristics of Longitudinal Profile of Shoe Last
Authors: Tian Yujing , Miao Yuxin , Yu Ying , Zhang Ziran 
Published: July 2019

Abstract
In order to achieve low-cost, high-efficiency customized footwear design and service to meet the individual needs of customers, quickly producing various types of shoe last based on different needs has become the key to customization. According to the moulding characteristics of the longitudinal profile of the shoe last, the key characteristic parameters and morphogenesis logic are found. Combined with the parameterized nonlinear thinking feature, the parameter logic is constructed. Rhino’s parametric design plug-in Grasshopper is used to develop the shoe last design process. The automatic moulding process of the sectional view, combined with the parametric design of the shoe last and the cross-section of the shoe last, can form a quick custom shoe last method. A professional and rapid design method is formed to enhance the design and production efficiency of customized shoe lasts.
A Novel Line Feature Description and Matching Method for Visual-Aided Inertial Navigation System
Authors: Guang Xingxing , Gao Yanbin , Liu Pan , Li Guangchun 
Published: July 2019

Abstract
With the rapid development of machine vision technology, more and more attention has been paid to the visual-aided inertial navigation system. It is important that to extract and track the line features at the dynamic situation in the visual-aided inertial navigation system which is based on visual line feature information to compensate attitude errors. A novel line feature description is proposed that use the SURF points to mark the LSD lines. Then, through coarse matching and fine matching, the function of continuously tracking the one line features in different images was realized. These line feature description and tracking method are applied in the visual-aided inertial navigation system, and its effectiveness is verified by the vehicle experiment.
Semantic Segmentation of Remote Sensing Images via Stepwise-Refined Large-Kernel Deconvolutional Networks
Authors: Xinwei Zhao , Haichang Li , Rui Wang , Changwen Zheng , Song Shi 
Published: July 2019

Abstract
Deep CNN based semantic segmentation has been developed for several years and many models are proposed. However, most of them are designed for natural scene images such as PASCAL VOC, and cannot perform very well on remote sensing images, in which objects are much smaller and more densely distributed than those in natural scene images. In this paper, we demonstrate the importance of high-resolution feature maps and the problem of large dilated convolutional kernels in semantic segmentation of remote sensing images. Furthermore, we propose a Stepwise-Refined Large-Kernel Deconvolutional Network with a focus on small and densely-distributed objects such as houses and buildings, or long and narrow ones such as roads and rivers. Experiments on a public available ISPRS Vaihingen Challenge Dataset and our self-compiled Fujian Dataset show that our model outperforms the state-of-the-art models in semantic segmentation of remote sensing images.
Design of a Synchronous Control System for Mobile Lift Based on Wireless Network
Authors: Zhuang Lu , Chen Haijin 
Published: July 2019

Abstract
The lift used in automobile maintenance industry is usually installed in a place permanently. It occupies the place, and also has poor adaptability to vehicles with different size. Furthermore, there exists physical connection between lifting columns, which affects maintenance operation. To solve these problems, a mobile hydraulic lift control system based on wireless network has been designed. It uses the STM32F103 microcontroller and nRF905 wireless communication module to form a control network for several mobile lifts. The height of each lift is measured with laser displacement sensors, and synchronously controlled through the wireless control network. The control system was verified by lifting the Jinlong bus with wirelessly connected four lifts. The lifting process is stable, which demonstrates a bright prospect for practical usages.
A Human-Robot Interaction for a Mecanum Wheeled Mobile Robot with Real-Time 3D Two-Hand Gesture Recognition
Authors: Luo Xueling , Amighetti Andrea , Zhang Dan 
Published: July 2019

Abstract
Human interaction with mobile robot becomes a popular research area and its applications are widely used in industrial, commercial and military fields. A two-hand gesture recognition method with depth camera is presented for real-time controlling the mecanum wheeled mobile robot. Seven different gestures could be recognized from one hand for mobile robot navigation and three gestures could be recognized from the other hand for controlling the gripper installed on the robot. Under the proposed control scheme, the mobile robot system can be navigated and can be operated at the same time for achieving missions by two different groups of hand gestures. The accuracy of the gesture recognition is about 94%. During mobile robot control experiment, the system works timely, accurately and stably for certain tasks such as directional movement, grasping and cleaning obstacles.
Mechanics Modeling and Kinetic Analysis of Sub Marine Tracked Vehicle-Cable Salvage during Steady-state Steering
Authors: Hao Zhang , Jianjun Su , Xiaoli Hu , Yubing Duan , Xin Shi , Jinxia Yao , Chao Gu 
Published: July 2019

Abstract
Based on the demand for deep-sea submarine cable salvage in Northeast Asia, a mathematical model of Sub Marine Tracked Vehicle-Cable Salvage (SMT-CS) was designed in this study. Considering the slip characteristics of the submarine track, the mechanical model of the SMT-CS during steady-state steering was simplified to obtain the kinetic equation of the vehicle body during steady-state steering. According to the steering geometrical relationship, the arbitrary shear rate and shear displacement of the ground plane during the steering process were solved. Combined with the empirical shear model of bottom sediments, the longitudinal driving force, lateral resistance and resistance moment of the larboard and starboard were derived during steady-state steering. The functional relationship between the lateral shift of instantaneous center of the conventional steering rate and the turning radius and angular velocity of the vehicle body as well as that between the slipping rate and the turning radius and angular velocity of the vehicle body was derived according to the geometrical relationship of steering motion. These are of great reference value to the design, performance prediction and maritime application of SMT-CS in the future.
Event-triggered Group Consensus for First-order Multi-agent Systems Based on Directed Topology
Authors: Zhou Shaolei , Wang Shuailei , Yan Shi , Zhao Xueyuan 
Published: July 2019

Abstract
Event-triggered group consensus problem is addressed in this paper. Based on the assumption named the in-degree balance, a directed first-order multi-agent system is constructed, which consists of two subgroups. To analyze the problem, the Laplacian matrix of the initial system is decomposed to be two matrices with less row and column dimension. Then the initial state vector can be substituted by a new one, each element in which is the error of two adjacent elements of the initial state vector. Then a new reduced-order system is constructed and the consensus problem is converted to a stability problem. With the designed Lyapunov function, the validity of the proposed algorithm is proved. The lower-bound of the inter-event time interval is also presented. Simulation results are presented to demonstrate the effectiveness of the protocol.
ABB Robotic Arm Offline Programming System
Authors: Xu Zhe , Liu Yuanjiang 
Published: July 2019

Abstract
The offline programming technology of industrial robots is more and more widely used because it can effectively improve work efficiency. A DXF drawing file is a file generated by CAD that can serve as a bridge for data exchange with other software. Through experiments, this paper combines CAD-generated DXF drawing files with ABB robotic arm to realize data extraction of CAD drawings, and automatic generation and storage of robotic arm running programs, which are imported into the robot arm, so that the robot arm can be made according to CAD. The trajectory on the drawing performs the welding function. The simulation of path planning was carried out under the Robot studio software developed by ABB Robotics to meet the experimental requirements. The realization of this off-line programming system achieves the following two purposes. One is to assist non-control professional teaching, to help understand the simulation and actual production and processing of industrial robot arm; the second is to improve the efficiency of industrial robot arm programming and realize full automation programming.
The Design and Application of FEU: Automation Monitoring Unit for 5G base stations
Authors: Li Hong , Liu Huazhao , Yan Baiyi 
Published: July 2019

Abstract
In order to realize the co-construction, sharing and intensive management of millions of stations across the network, supporting 5G go into operation efficiently, China Tower Group has developed a new generation of intensive and distributed operation maintenance monitoring platform over the inherited mobile base stations from the three major telecom operators. Based on the development and the application of this platform, FEU (an intelligent Power and Environment Monitoring Unit) is proposed, which uses the dynamic shared library method to embed the edge gateway to realize the edge computing capability of China Tower’s intelligent operation and maintenance platform. It effectively solves the normalized monitoring of heterogeneous FSU and massive sites, constructs the intensive operation maintenance capability for China Tower and lays a solid foundation for the tower-based IoT [1, 2] expansion business.
A Novel Network Traffic Prediction Method Based on RMPM
Authors: Wu Jianjun , Gong Weijun , Shang Zhen 
Published: July 2019

Abstract
The network traffic prediction plays the key role in congestion control and bandwidth allocation. A variety of traditional learning models such as artificial neural networks (ANN) have been applied in prediction. To avoid the drawbacks of traditional models for prediction, a novel robust minimax probability machine (RMPM)-based traffic prediction method is proposed in this paper. The prediction performance is tested on two different types of traffic data, Ethernet data flow and MPEG4 video flow, at the timescale 1. The experiments demonstrate that the proposed method attains satisfactory performance in prediction accuracy. Therefore, the proposed method can be used for congestion control or bandwidth allocation, to meet the user QOS requirements.