作者机构：
[Wu, Liang; Zhang, Jianfeng; Wan, Beibei; Zhang, Zhaoli; Shu, Jiangbo; Liu, Hai] Cent China Normal Univ, Natl Engn Ctr E Learning, Wuhan, Peoples R China.
会议名称：
International Symposium on Educational Technology (ISET)
会议时间：
JUL 19-21, 2016
会议地点：
Peking Univ, Beijing, PEOPLES R CHINA
会议主办单位：
Peking Univ
关键词：
big data;blended learning;process evaluation
摘要：
With the development of information technology, the application of big data in the field of education has been deepened, and blended learning has been popularized in teaching process. In blended learning, teacher can't remember every student in the process of learning in all details, resulting in methods of emotional subjective evaluation can only be used on the evaluation process of teachers to students. Without doubt it cannot describe their real behavior performance objectively and fairly. In view of this problem, this paper studies personal big data of blended learning, through the establishment of a large data model of personal learning, and analysis of these data, so as to provide a basis for objective evaluation. According to the data of teaching in software engineering course as an example, through the analysis of classroom video of the teaching process, gets the expression and action of learners in class in usual, and sees it as a factor of reflection of seriousness degree of the students listening in class, and it can reflect the attitude of students in a sense. This experiment shows that learning process of big data have a relatively objective evaluation to students, it can also show students' behavior history, so as to spur students to improve these behaviors consciously.
摘要：
In view of the problem of the low efficiency in traditional classroom teaching due to the limitation in time and space, an exploration which combines real classroom with virtual classroom in hybrid learning was proposed. We chose the teaching of a software engineering course and used starC as the teaching support tool for analysis. In our study, the teaching process was divided into several teaching units, and each teaching unit was further divided into several activity units. The content was organized in the form of topicalities, where students are allowed to choose the learning content according to their study plans and preferences. Through the questionnaire survey which includes the indicators of participation and satisfaction among the students on both traditional learning and hybrid learning, it is found that the students on hybrid learning have higher participation and satisfaction than that on traditional learning. This indicated that hybrid learning could effectively improve teaching effectiveness.
作者机构：
[Liu, Tingting; Zhang, Zhaoli; Liu, Sanya; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Yan, Luxin; Zhang, Tianxu] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China.
通讯机构：
[Liu, Hai] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
关键词：
Raman spectroscopy;Optical data processing;Baseline correction;Denoising;Morphological operation;Regularization
摘要：
Laser instruments often suffer from the problem of baseline drift and random noise, which greatly degrade spectral quality. In this article, we propose a variation model that combines baseline correction and denoising. First, to guide the baseline estimation, morphological operations are adopted to extract the characteristics of the degraded spectrum. Second, to suppress noise in both the spectrum and baseline, Tikhonov regularization is introduced. Moreover, we describe an efficient optimization scheme that alternates between the latent spectrum estimation and the baseline correction until convergence. The major novel aspect of the proposed algorithms is the estimation of a smooth spectrum and removal of the baseline simultaneously. Results of a comparison with state-of-the-art methods demonstrate that the proposed method outperforms them in both qualitative and quantitative assessments.
作者机构：
[Liu, Tingting; Zhang, Zhaoli; Liu, Sanya; Shu, Jiangbo; Zhang, ZL; Liu, SY; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.
会议名称：
IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)
会议时间：
AUG 09-12, 2015
会议地点：
Salt Lake, UT
会议主办单位：
[Liu, Hai;Zhang, Zhaoli;Liu, Sanya;Shu, Jiangbo;Liu, Tingting] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.
关键词：
Optics data processing;Signal processing;Blind deconvolution;Infrared spectroscopy
摘要：
In this paper, we will propose a new framework which can estimate the desired signal and the instrument response function (IRF) simultaneously from the degraded spectral signal. Firstly, the spectral signal is considered as a distribution, thus, new entropy (called differential-entropy, DE) is defined to measure the distribution with a uniform distribution, which allows negative value existing. Moreover, the IRF is parametrically modeled as a Lorentzian function. Comparative results manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression. The deconvolution IR spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical mixtures.
作者：
Liu, Hai;Zhang, Zhaoli;Liu, Sanya*;Liu, Tingting;Chang, Yi
作者机构：
[Liu, Tingting; Zhang, Zhaoli; Liu, Sanya; Chang, Yi; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议名称：
IEEE International Conference on Image Processing (ICIP)
会议时间：
SEP 27-30, 2015
会议地点：
Quebec City, CANADA
会议主办单位：
[Liu, Hai;Zhang, Zhaoli;Liu, Sanya;Liu, Tingting;Chang, Yi] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议论文集名称：
IEEE International Conference on Image Processing ICIP
摘要：
Remote sensing image often suffers from the common problems of stripe noise and random noise. In this paper, we present a destriping method with unidirectional gradient L0 norm and L0 sparsity priori. The major novelty of the proposed method is that combining the unidirectional gradient L0 norm with the sparsity priori to address the destriping and denoising issues. Moreover, doubly augmented Lagrangian (DAL) method is adopted to solve the L0 regularized minimization problem. The proposed method is verified on heavily striped remote sensing images. Comparative results demonstrate that the proposed method outperforms the-state-of-art methods, which can suppress noise effectively as well as preserve image structures well.
作者机构：
[Liu, Sanyan; Liu, Zhi; Zhang, Zhaoli; Shu, Jiangbo; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议名称：
Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)
会议时间：
DEC 16-19, 2015
会议地点：
Hong Kong, PEOPLES R CHINA
会议主办单位：
[Liu, Hai;Zhang, Zhaoli;Liu, Sanyan;Shu, Jiangbo;Liu, Zhi] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议论文集名称：
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
关键词：
Spectral restoration;Multiscales;Blind deconvolution;Infrared spectroscopic data
摘要：
Band overlap and random noise exist widely when the spectra are captured using an infrared spectrometer, especially when the problems of instrument aging has become more and more serious recently. In this paper, via introducing the similarity of multiscales, a blind spectral deconvolution method is proposed. Considering similarity of the latent spectrum between different scales, it is used as a prior to constrain the estimated latent spectrum similar to pre-scale to reduce artifacts which is produced from deconvolution. Experiments indicate that the proposed method is able to obtain better performance than the state-of-the-art methods, and obtain satisfying deconvolution results with fewer artifacts. The recovered infrared spectra can easily extract the spectral features and recognize the unknown objects.
作者机构：
[Liu, Tingting; Zhang, Zhaoli; Liu, Sanya; Yan, Zhonghua; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议名称：
5th International Conference on Information Science and Technology (ICIST)
会议时间：
APR 24-26, 2015
会议地点：
Changsha, PEOPLES R CHINA
会议主办单位：
[Liu, Hai;Zhang, Zhaoli;Liu, Sanya;Yan, Zhonghua;Liu, Tingting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议论文集名称：
International Conference on Information Science and Technology
关键词：
Optical data processing;Blind deconvolution;Spectral analysis;high-resolution;Regularization;Spectroscopic data
摘要：
Spectroscopic data often suffers from common problems of bands overlap and random Gaussian noise. Spectral resolution can be improved by mathematically removing the effect of the instrument response function (IRF). In this paper, a novelty model is proposed to deconvolute the measured spectrum with the sparsity regularization. The proposed model is solved by iteratively reweighted least square method. The major novelty of the proposed method is that it can estimate the IRF and latent spectrum simultaneously. Experimental results with actual Raman spectra manifest that this algorithm can recover the overlap peaks as well as suppress the noise effectively.
摘要：
A blind deconvolution algorithm with modified Tikhonov regularization is introduced. To improve the spectral resolution, spectral structure information is incorporated into regularization by using the adaptive term to distinguish the spectral structure from other regions. The proposed algorithm can effectively suppress Poisson noise as well as preserve the spectral structure and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Comparative results on simulated and real degraded Raman spectra are reported. The recovered Raman spectra can easily extract the spectral features and interpret the unknown chemical mixture.
期刊：
International Conference on Communication Technology Proceedings, ICCT,2012年:861-865
通讯作者：
Zhu, Hong
作者机构：
[Zhu, Hong] Cent China Normal Univ, Acad Comp Sci, Wuhan, Peoples R China.;[Liu, Zhi; Zhang, Zhaoli] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Zhu, Hong] C;Cent China Normal Univ, Acad Comp Sci, Wuhan, Peoples R China.
会议名称：
2012 IEEE 14th International Conference on Communication Technology(2012年第十四届通信技术国际会议(ICCT 2012))
会议时间：
2012-11-09
会议地点：
成都
会议主办单位：
[Zhu, Hong] Cent China Normal Univ, Acad Comp Sci, Wuhan, Peoples R China.^[Zhang, Zhaoli;Liu, Zhi] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议论文集名称：
2012 IEEE 14th International Conference on Communication Technology(2012年第十四届通信技术国际会议(ICCT 2012))论文集
摘要：
To deal with the high dimensionality and redundancy of the online writeprint, this paper proposed an ensemble learning approach based on Multiple Probabilistic Reasoning Model. An inverse method of pseudo-random number generator is employed to construct multiple random subspaces, and then the base classifier is trained in each subspace. Finally, each classifier is aggregated to construct a strong ensemble through a combination strategy. The experiment is conducted on a real dataset, focusing on the approach's parameters, sampling rate and granularity of space dividing. The results show that the proposed method is effective and appropriate values of parameters can effectively improve the identification performance of online writeprint.
