• Complex
  • Title
  • Author
  • Keyword
  • Abstract
  • Scholars
Search
High Impact Results & Cited Count Trend for Year Keyword Cloud and Partner Relationship

Query:

学者姓名:孟德宇

Refining:

Source

Submit Unfold

Co-Author

Submit Unfold

Language

Submit

Clean All

Export Sort by:
Default
  • Default
  • Title
  • Year
  • WOS Cited Count
  • Impact factor
  • Ascending
  • Descending
< Page ,Total 12 >
A Multiobjective Approach Based on Gaussian Mixture Clustering for Sparse Reconstruction SCIE
期刊论文 | 2019 , 7 , 22684-22697 | IEEE ACCESS
Abstract&Keyword Cite

Abstract :

The application of multiobjective approaches for sparse reconstruction is a relatively new research topic in the area of compressive sensing. Unlike conventional iterative thresholding methods, multiobjective approaches attempt to find a set of solutions called Pareto front (PF) with different sparsity levels. The major focus of the existing sparse multiobjective approaches is to find the knee region of PF, where the K-sparse solution should reside. However, the strategies in these approaches for finding the knee region of PF are not very reliable due to the sensitivities on the setting of control parameters or noise levels. In this paper, we propose a new strategy based on Gaussian mixture models (GMMs) within a decomposition-based multiobjective framework for sparse reconstruction. The basic idea is to cluster the population found by a chain-based search procedure into two subsets via GMM. One of them with the small values of loss function should include the knee region. Our proposed algorithm was tested on a set of six artificial instance sets at four different noise levels. The experimental results showed that our proposed algorithm is superior to two existing sparse multiobjective approaches and one iterative thresholding algorithm.

Keyword :

multiobjective evolutionary approach Sparse optimization Gaussian mixture clustering iterative thresholding

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Li, Hui , Sun, Jianyong , Meng, Deyu et al. A Multiobjective Approach Based on Gaussian Mixture Clustering for Sparse Reconstruction [J]. | IEEE ACCESS , 2019 , 7 : 22684-22697 .
MLA Li, Hui et al. "A Multiobjective Approach Based on Gaussian Mixture Clustering for Sparse Reconstruction" . | IEEE ACCESS 7 (2019) : 22684-22697 .
APA Li, Hui , Sun, Jianyong , Meng, Deyu , Zhang, Qingfu . A Multiobjective Approach Based on Gaussian Mixture Clustering for Sparse Reconstruction . | IEEE ACCESS , 2019 , 7 , 22684-22697 .
Export to NoteExpress RIS BibTex
An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization SCIE
期刊论文 | 2019 , 38 (2) , 360-370 | IEEE TRANSACTIONS ON MEDICAL IMAGING
WoS CC Cited Count: 2
Abstract&Keyword Cite

Abstract :

Cerebrovascular diseases, i.e., acute stroke, are a common cause of serious long-term disability. Cerebral perfusion computed tomography (CPCT) can provide rapid, high-resolution, quantitative hemodynamic maps to assess and stratify perfusion in patients with acute stroke symptoms. However, CPCT imaging typically involves a substantial radiation dose due to its repeated scanning protocol. Therefore, in this paper, we present a low-dose CPCT image reconstruction method to yield high-quality CPCT images and high-precision hemodynamic maps by utilizing the great similarity information among the repeated scanned CPCT images. Specifically, a newly developed low-rank tensor decomposition with spatial-temporal total variation (LRTD-STTV) regularization is incorporated into the reconstruction model. In the LRTD-STTV regularization, the tensor Tucker decomposition is used to describe global spatial-temporal correlations hidden in the sequential CPCT images, and it is superior to the matricization model (i.e., low-rank model) that fails to fully investigate the prior knowledge of the intrinsic structures of the CPCT images after vectorizing the CPCT images. Moreover, the spatial-temporal TV regularization is used to characterize the local piecewise smooth structure in the spatial domain and the pixels' similarity with the adjacent frames in the temporal domain, because the intensity at each pixel in CPCT images is similar to its neighbors. Therefore, the presented LRTD-STTV model can efficiently deliver faithful underlying information of the CPCT images and preserve the spatial structures. An efficient alternating direction method of multipliers algorithm is also developed to solve the presented LRTD-STTV model. Extensive experimental results on numerical phantom and patient data are clearly demonstrated that the presented model can significantly improve the quality of CPCT images and provide accurate diagnostic features in hemodynamic maps for low-dose cases compared with the existing popular algorithms.

Keyword :

low-rank Computed tomography tensor regularization cerebral perfusion

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Li, Sui , Zeng, Dong , Peng, Jiangjun et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2019 , 38 (2) : 360-370 .
MLA Li, Sui et al. "An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 38 . 2 (2019) : 360-370 .
APA Li, Sui , Zeng, Dong , Peng, Jiangjun , Bian, Zhaoying , Zhang, Hao , Xie, Qi et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2019 , 38 (2) , 360-370 .
Export to NoteExpress RIS BibTex
Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under a Collaborative Self-Paced Curriculum Learning Framework EI Scopus SCIE
期刊论文 | 2019 , 127 (4) , 363-380 | International Journal of Computer Vision
WoS CC Cited Count: 8 SCOPUS Cited Count: 3
Abstract&Keyword Cite

Abstract :

Weakly supervised object detection is an interesting yet challenging research topic in computer vision community, which aims at learning object models to localize and detect the corresponding objects of interest only under the supervision of image-level annotation. For addressing this problem, this paper establishes a novel weakly supervised learning framework to leverage both the instance-level prior-knowledge and the image-level prior-knowledge based on a novel collaborative self-paced curriculum learning (C-SPCL) regime. Under the weak supervision, C-SPCL can leverage helpful prior-knowledge throughout the whole learning process and collaborate the instance-level confidence inference with the image-level confidence inference in a robust way. Comprehensive experiments on benchmark datasets demonstrate the superior capacity of the proposed C-SPCL regime and the proposed whole framework as compared with state-of-the-art methods along this research line. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Keyword :

Benchmark datasets Learning frameworks Learning object model Learning process Self-paced larning State-of-the-art methods Vision communities Weakly supervised learning

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Zhang, Dingwen , Han, Junwei , Zhao, Long et al. Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under a Collaborative Self-Paced Curriculum Learning Framework [J]. | International Journal of Computer Vision , 2019 , 127 (4) : 363-380 .
MLA Zhang, Dingwen et al. "Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under a Collaborative Self-Paced Curriculum Learning Framework" . | International Journal of Computer Vision 127 . 4 (2019) : 363-380 .
APA Zhang, Dingwen , Han, Junwei , Zhao, Long , Meng, Deyu . Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under a Collaborative Self-Paced Curriculum Learning Framework . | International Journal of Computer Vision , 2019 , 127 (4) , 363-380 .
Export to NoteExpress RIS BibTex
Hyperspectral image restoration under complex multi-band noises EI Scopus CSSCI-E SCIE
期刊论文 | 2018 , 10 (10) | Remote Sensing
Abstract&Keyword Cite

Abstract :

Hyperspectral images (HSIs) are always corrupted by complicated forms of noise during the acquisition process, such as Gaussian noise, impulse noise, stripes, deadlines and so on. Specifically, different bands of the practical HSIs generally contain different noises of evidently distinct type and extent. While current HSI restoration methods give less consideration to such band-noise-distinctness issues, this study elaborately constructs a new HSI restoration technique, aimed at more faithfully and comprehensively taking such noise characteristics into account. Particularly, through a two-level hierarchical Dirichlet process (HDP) to model the HSI noise structure, the noise of each band is depicted by a Dirichlet process Gaussian mixture model (DP-GMM), in which its complexity can be flexibly adapted in an automatic manner. Besides, the DP-GMM of each band comes from a higher level DP-GMM that relates the noise of different bands. The variational Bayes algorithm is also designed to solve this model, and closed-form updating equations for all involved parameters are deduced. The experiment indicates that, in terms of the mean peak signal-to-noise ratio (MPSNR), the proposed method is on average 1 dB higher compared with the existing state-of-the-art methods, as well as performing better in terms of the mean structural similarity index (MSSIM) and Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS). © 2018 by the authors.

Keyword :

Gaussian Mixture Model Hierarchical Dirichlet process Hierarchical dirichlet process (HDP) Mean structural similarity indices Peak signal to noise ratio Restoration techniques State-of-the-art methods Variational bayes

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Yue, Zongsheng , Meng, Deyu , Sun, Yongqing et al. Hyperspectral image restoration under complex multi-band noises [J]. | Remote Sensing , 2018 , 10 (10) .
MLA Yue, Zongsheng et al. "Hyperspectral image restoration under complex multi-band noises" . | Remote Sensing 10 . 10 (2018) .
APA Yue, Zongsheng , Meng, Deyu , Sun, Yongqing , Zhao, Qian . Hyperspectral image restoration under complex multi-band noises . | Remote Sensing , 2018 , 10 (10) .
Export to NoteExpress RIS BibTex
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation CPCI-S
会议论文 | 2018 , 5197-5206 | 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Abstract&Keyword Cite

Abstract :

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Liu, Jiang , Gao, Chenqiang , Meng, Deyu et al. DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation [C] . 2018 : 5197-5206 .
MLA Liu, Jiang et al. "DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation" . (2018) : 5197-5206 .
APA Liu, Jiang , Gao, Chenqiang , Meng, Deyu , Hauptmann, Alexander G. . DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation . (2018) : 5197-5206 .
Export to NoteExpress RIS BibTex
CPCT-LRTDTV: Cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization EI CPCI-S Scopus
会议论文 | 2018 , 10573 | Conference on Medical Imaging - Physics of Medical Imaging
SCOPUS Cited Count: 2
Abstract&Keyword Cite

Abstract :

Ischemic stroke is the leading cause of serious and long-term disability worldwide. The cerebral perfusion computed tomography (CPCT) is an important imaging modality for diagnosis in case of an ischemic stroke event by providing cerebral hemodynamic information. However, due to the dynamic sequential scans in CPCT, the associative radiation dose unavoidably increases compared with conventional CT. In this work, we present a robust CPCT image restoration algorithm with a spatial total variation (TV) regularization and a low rank tensor decomposition (LRTD) to estimate high-quality CPCT images and corresponding hemodynamic map in the case of low-dose, which is termed "CPCT-LRTDTV". Specifically, in the LRTDTV regularization, the spatial TV is introduced to describe local smoothness of the CPCT images, and the LRTD is adopted to fully characterize spatial and time dimensional correlation existing in the CPCT images. Subsequently, an alternating optimization algorithm was adopted to minimize the associative objective function. To evaluate the presented CPCT-LRTDTV algorithm, both qualitative and quantitative experiments are conducted on digital perfusion brain phantom and clinical patient. Experimental results demonstrate that the present CPCT-LRTDTV algorithm is superior to other existing algorithms with better noise-induced artifacts reduction, resolution preservation and accurate hemodynamic map estimation.

Keyword :

low-dose cerebral perfusion computed tomography restoration Low rank tensor decomposition total variation

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Peng, Jianjun , Zeng, Dong , Ma, Jianhua et al. CPCT-LRTDTV: Cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization [C] . 2018 .
MLA Peng, Jianjun et al. "CPCT-LRTDTV: Cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization" . (2018) .
APA Peng, Jianjun , Zeng, Dong , Ma, Jianhua , Wang, Yao , Meng, Deyu . CPCT-LRTDTV: Cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization . (2018) .
Export to NoteExpress RIS BibTex
Denoising Hyperspectral Image With Non-i.i.d. Noise Structure EI SCIE Scopus
期刊论文 | 2018 , 48 (3) , 1054-1066 | IEEE TRANSACTIONS ON CYBERNETICS
WoS CC Cited Count: 12 SCOPUS Cited Count: 14
Abstract&Keyword Cite

Abstract :

Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoGs) noise assumption, which finely accords with the noise characteristics possessed by a natural HSI and thus is capable of adapting various practical noise shapes. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose an NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is then designed to infer the posterior of the proposed model. As substantiated by our experiments implemented on synthetic and real noisy HSIs, the proposed method performs more robust beyond the state-of-the-arts.

Keyword :

low-rank matrix factorization (LRMF) Hyperspectral image (HSI) denoising non independent and identically distributed (i.i.d.) noise modeling

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Chen, Yang , Cao, Xiangyong , Zhao, Qian et al. Denoising Hyperspectral Image With Non-i.i.d. Noise Structure [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) : 1054-1066 .
MLA Chen, Yang et al. "Denoising Hyperspectral Image With Non-i.i.d. Noise Structure" . | IEEE TRANSACTIONS ON CYBERNETICS 48 . 3 (2018) : 1054-1066 .
APA Chen, Yang , Cao, Xiangyong , Zhao, Qian , Meng, Deyu , Xu, Zongben . Denoising Hyperspectral Image With Non-i.i.d. Noise Structure . | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) , 1054-1066 .
Export to NoteExpress RIS BibTex
A generalized model for robust tensor factorization with noise modeling by mixture of gaussians EI Scopus SCIE
期刊论文 | 2018 , 29 (11) , 5380-5393 | IEEE Transactions on Neural Networks and Learning Systems
WoS CC Cited Count: 3 SCOPUS Cited Count: 2
Abstract&Keyword Cite

Abstract :

The low-rank tensor factorization (LRTF) technique has received increasing attention in many computer vision applications. Compared with the traditional matrix factorization technique, it can better preserve the intrinsic structure information and thus has a better low-dimensional subspace recovery performance. Basically, the desired low-rank tensor is recovered by minimizing the least square loss between the input data and its factorized representation. Since the least square loss is most optimal when the noise follows a Gaussian distribution, L1-norm-based methods are designed to deal with outliers. Unfortunately, they may lose their effectiveness when dealing with real data, which are often contaminated by complex noise. In this paper, we consider integrating the noise modeling technique into a generalized weighted LRTF (GWLRTF) procedure. This procedure treats the original issue as an LRTF problem and models the noise using a mixture of Gaussians (MoG), a procedure called MoG GWLRTF. To extend the applicability of the model, two typical tensor factorization operations, i.e., CANDECOMP/PARAFAC factorization and Tucker factorization, are incorporated into the LRTF procedure. Its parameters are updated under the expectation-maximization framework. Extensive experiments indicate the respective advantages of these two versions of MoG GWLRTF in various applications and also demonstrate their effectiveness compared with other competing methods. © 2018 IEEE.

Keyword :

Computational model Expectation-maximization algorithms Indexes Mixture of gaussians (MOG) Tensor factorization

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Chen, Xi'Ai , Han, Zhi , Wang, Yao et al. A generalized model for robust tensor factorization with noise modeling by mixture of gaussians [J]. | IEEE Transactions on Neural Networks and Learning Systems , 2018 , 29 (11) : 5380-5393 .
MLA Chen, Xi'Ai et al. "A generalized model for robust tensor factorization with noise modeling by mixture of gaussians" . | IEEE Transactions on Neural Networks and Learning Systems 29 . 11 (2018) : 5380-5393 .
APA Chen, Xi'Ai , Han, Zhi , Wang, Yao , Zhao, Qian , Meng, Deyu , Lin, Lin et al. A generalized model for robust tensor factorization with noise modeling by mixture of gaussians . | IEEE Transactions on Neural Networks and Learning Systems , 2018 , 29 (11) , 5380-5393 .
Export to NoteExpress RIS BibTex
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification SCIE
期刊论文 | 2018 , 40 (1) , 7-19 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
WoS CC Cited Count: 21
Abstract&Keyword Cite

Abstract :

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/

Keyword :

Cost-effective model face identification self-paced learning incremental processing active learning

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Lin, Liang , Wang, Keze , Meng, Deyu et al. Active Self-Paced Learning for Cost-Effective and Progressive Face Identification [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (1) : 7-19 .
MLA Lin, Liang et al. "Active Self-Paced Learning for Cost-Effective and Progressive Face Identification" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40 . 1 (2018) : 7-19 .
APA Lin, Liang , Wang, Keze , Meng, Deyu , Zuo, Wangmeng , Zhang, Lei . Active Self-Paced Learning for Cost-Effective and Progressive Face Identification . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (1) , 7-19 .
Export to NoteExpress RIS BibTex
Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network EI SCIE Scopus
期刊论文 | 2018 , 27 (5) , 2354-2367 | IEEE TRANSACTIONS ON IMAGE PROCESSING
WoS CC Cited Count: 19 SCOPUS Cited Count: 23
Abstract&Keyword Cite

Abstract :

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent and update the class labels of all pixel vectors using alpha-expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.

Keyword :

Hyperspectral image classification Markov random fields convolutional neural networks

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Cao, Xiangyong , Zhou, Feng , Xu, Lin et al. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2018 , 27 (5) : 2354-2367 .
MLA Cao, Xiangyong et al. "Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 27 . 5 (2018) : 2354-2367 .
APA Cao, Xiangyong , Zhou, Feng , Xu, Lin , Meng, Deyu , Xu, Zongben , Paisley, John . Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2018 , 27 (5) , 2354-2367 .
Export to NoteExpress RIS BibTex
10| 20| 50 per page
< Page ,Total 12 >

Export

Results:

Selected

to

Format:
FAQ| About| Online/Total:2639/65150133
Address:XI'AN JIAOTONG UNIVERSITY LIBRARY(No.28, Xianning West Road, Xi'an, Shaanxi Post Code:710049) Contact Us:029-82667865
Copyright:XI'AN JIAOTONG UNIVERSITY LIBRARY Technical Support:Beijing Aegean Software Co., Ltd.