作者机构：
[Yu, Xin Guo; He, Bin; Zhuang, Jiao Jiao; Sun, Jia Yu; Dai, Zi Chun] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Yu, Xin Guo] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称：
3rd International Conference on Mechatronics and Intelligent Robotics (ICMIR)
会议时间：
MAY 25-26, 2019
会议地点：
Kunming, PEOPLES R CHINA
会议主办单位：
[Yu, Xin Guo;Sun, Jia Yu;He, Bin;Zhuang, Jiao Jiao;Dai, Zi Chun] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
摘要：
This paper designs and implements the automatic invigilation functions using the embedded technology. It proposes a framework for automatic invigilation, which conducts the invigilation functions of the entire examination process. In the examination preparation stage, the framework collects the registration details of examinees and verifies the details with the database through the remote server. During the examination ongoing stage, it keeps checking the consistence between each examinee and his examination materials by photographing the examinee and scanning the QR codes on examination papers, sketch papers, and answer sheets. In the examination ending stage, it checks the consistence of an examination bag and the materials being put into it by scanning and verifying the QR codes on them. The framework reduces the human workload by using automatic functions to replace the human work. The tests demonstrates that the framework can perform the designed functions. (C) 2020 The Authors. Published by Elsevier B.V.
作者：
Xinguo Yu（余新国）;Wu Song;Xiaopan Lyu;Bin He;Nan Ye
期刊：
International Journal of Digital Crime and Forensics,2020年12(2):21-39 ISSN：1941-6210
作者机构：
[Xinguo Yu] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;[Nan Ye] University of Queensland, Brisbane, Australia;[Wu Song; Xiaopan Lyu; Bin He] Central China Normal University, Wuhan, China
作者机构：
[Xinguo Yu; Weina Cheng] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Intelligent Classroom;Traditional Classroom;Teaching Design;Advantage Perspective;Cooperative Development
摘要：
The rise of the future classroom is accompanied by many obstacles, including limited funds, technical and theoretical immaturity. In order to better carry out the construction of educational informatization, this paper uses big data analysis methods to rank the importance of each link and equipment in teaching activities, namely the perspective advantages. Then we propose a new model, which focuses on the advantage perspective input and construction, trying to construct the cooperative development of future classrooms and traditional classrooms on the basis of existing classrooms and maximizes the role of the perspective of superiority, so as to improve the quality of teaching, and discusses this new model in this paper. The feasibility and necessity of the idea.
期刊：
Communications in Computer and Information Science,2019年1043:184-192 ISSN：1865-0929
通讯作者：
Li, T.
作者机构：
[Zeng Z.; Yu X.; Li T.] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China
通讯机构：
[Li, T.] N;National Engineering Research Center for E-Learning, China
会议名称：
14th Conference on Image and Graphics Technologies and Applications, IGTA 2019
会议时间：
19 April 2019 through 20 April 2019
会议论文集名称：
Image and Graphics Technologies and Applications
关键词：
SLAM;Visual-inertial odometry
摘要：
In a pure visual odometry, a pose transformation matrix between adjacent two frames is estimated by an algorithm based on pixel variation between images. However, pure monocular visual odometers cannot obtain absolute scales; in addition, relying solely on recursive calculations will inevitably lead to cumulative errors. For pure inertial solution calculations, the low-precision IMU will diverge very quickly in a short time. We derive the IMU prediction form based on Lie group and Lie algebra, and apply it to VIO. Based on the idea of graph optimization in pure visual SLAM, the IMU relative measurement information between frame and frame is converted into constraint node. The side of the pose is involved in the optimization framework. Using the IMU preintegration theory, these IMU’s are processed relative to the measurement so that it is decoupled from the absolute pose (or only requires linear operations to correct), which greatly increases the speed of optimization. In addition, this optimized architecture also makes the unacceptable gravity of the accelerometer measurement an advantageous condition - the presence of gravity will make the entire system observable to the absolute attitude.
摘要：
Automatically understanding natural language problems is a long-standing challenging research problem in automatic solving. This paper models the understanding of geometry problems as a problem of relation extraction, instead of as the problem of semantic understanding of natural language. Then it further proposes a supervised machine learning method to extract geometric relations, targeting to produce a group of relations to represent the given geometry problem. This method identifies the actual geometric relations from the relation candidates using a classifier trained from the labelled examples. The formalized geometric relations can then be transformed into the target system-native representations for manipulation in various tasks. Experiments conducted on the test problem dataset show that the proposed method can extract geometric relations at high F-1 scores. The comparisons also demonstrate that the proposed method can achieve good performance against the baseline methods. Integrating the automatic understanding method with different geometry systems will greatly enhance the efficiency and intelligence in geometry tutoring.
作者机构：
[Zheng, Lina] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China.;[Yu, Xinguo; Zhang, Ting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称：
15th IAPR International Conference on Document Analysis and Recognition (ICDAR) / 2nd Workshop of Machine Learning (WML)
会议时间：
SEP 21-22, 2019
会议地点：
Sydney, AUSTRALIA
会议主办单位：
[Zheng, Lina] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China.^[Zhang, Ting;Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议论文集名称：
Proceedings of the International Conference on Document Analysis and Recognition
关键词：
Handwritten symbol recognition;Chemical organic ring structure symbols;convolutional neural networks
摘要：
Many types of data exhibit characteristic of rotational symmetry. Chemical Organic Ring Structure(ORS) Symbol is such a case. In this paper, we focus on offline handwritten chemical ORS Symbols recognition using convolutional neural networks(CNNs), from application point of view, in order to relax the inconvenience and ineffectiveness of the traditional click-and-drag style of interaction when input chemical notations into electronic devices; from scientific point of view, to explore the capacity of rotation invariance of CNNs using data augmentation. We propose a VGGNet-based classifier for offline handwritten chemical ORS Symbols. To evaluate it, a new dataset of 3600 samples are collected of which 90% is for training while 10% is for test. The recognition accuracy is 84.3% with VGGNet-16 and 92.4% with VGGNet-19.
摘要：
This paper presents a double channel 3D convolution neural network to classify the exam scenes of invigilation videos. The first channel is based on the C3D convolution neural network, which is the status-of-arts method of the video scene classification. The structure of this channel is redesigned for classifying the exam-room scenes of invigilation videos. Another channel is based on the two-stream convolution neural network using the optical flow graph sequence as its input. This channel uses the data from the optical flow of video to improve the performance of the video scene classification. The formed double channel 3D convolution neural network has appropriate size of convolution kernel and pooling kernel design. Experiments show that the proposed neural network can classify the exam-room scenes of invigilation videos faster and more accurately than the existing methods.
期刊：
Lecture Notes in Computer Science,2018年10749:314-325 ISSN：0302-9743
通讯作者：
He, Bin
作者机构：
[Yu, Xinguo; He, Bin; Jian, Pengpeng; Xia, Meng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.
通讯机构：
[He, Bin] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称：
8th Pacific-Rim Symposium on Image and Video Technology (PSIVT)
会议时间：
NOV 20-24, 2017
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Yu, Xinguo;Jian, Pengpeng;He, Bin;Xia, Meng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.
摘要：
This paper presents an algorithm for understanding problems from circuit schematics in exercise problems in physics at secondary school. This paper models the problem understanding as a problem of extracting a set of relations that can be used to solve problems with enough information. The challenges lie in not only analyzing the circuit schematics but also extracting the proper relations for a given exercise problem. To face these challenges a novel approach is proposed to detect circuit nodes with their current flows to extract the current equations for nodes. And the other novel approach is proposed to extract voltage equations of independent loops. The proposed approach was tested with a dataset collected from the text books and the exam papers for the students at secondary schools. Experimental results show that the effect of recognition and analysis we designed delivers promising result, and our approach can be adapted to more complex electrical circuit analysis.
摘要：
Extracting algebraic relations from a given circuit image is still a challenge task due to the complex topology of considered circuit. This paper presents an approach for extracting algebraic relations from circuit images through producing a set of atomic topologies from the complex topology of a given circuit. In which, algebraic relations, in form of a set of equations involving voltage, current and resistance relations from atomic topologies that is obtained by an iteratively operation of transforming a complex series/parallel connection into a series of atomic connection topology breaking down and shrinking. The extracted algebraic relations can be used to solve the exercise problem described by the circuit. Experimental results on 20 exercise problems show that the proposed algorithm can obtain a complete set of algebraic relations that can be used to solve the given problem. Further experiments conducted on a dataset of 200 scanned circuit images from the text books and exam papers demonstrate the proposed algorithm is the robustness and effectiveness.
