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Search Results: 1 - 10 of 104284 matches for " Qibin Zhang "
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Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface
Qibin Zhao,Liqing Zhang
Computational Intelligence and Neuroscience , 2007, DOI: 10.1155/2007/37695
Abstract: Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.
INFLUENCE OF HIGH TEMPERATURE THERMOMECHANICAL TREATMENT ON STRUCTURE AND PROPERTIES OF AN IRON-NICKEL SUPERALLOY

Zhang Yongchang,Yang Qibin,

金属学报 , 1981,
Abstract:
EEG-based asynchronous BCI control of a car in 3D virtual reality environments
QiBin Zhao,LiQing Zhang,Andrzej Cichocki
Chinese Science Bulletin , 2009, DOI: 10.1007/s11434-008-0547-3
Abstract: Brain computer interface (BCI) aims at creating new communication channels without depending on brain’s normal output channels of peripheral nerves and muscles. However, natural and sophisticated interactions manner between brain and computer still remain challenging. In this paper, we investigate how the duration of event-related desynchronization/synchronization (ERD/ERS) caused by motor imagery (MI) can be modulated and used as an additional control parameter beyond simple binary decisions. Furthermore, using the non-time-locked properties of sustained (de)synchronization, we have developed an asynchronous BCI system for driving a car in 3D virtual reality environment (VRE) based on cumulative incremental control strategy. The extensive real time experiments confirmed that our new approach is able to drive smoothly a virtual car within challenging VRE only by the MI tasks without involving any muscular activities.
EEG-based asynchronous BCI control of a car in 3D virtual reality environments

QiBin Zhao,LiQing Zhang,Andrzej Cichocki,

科学通报(英文版) , 2009,
Abstract: Brain computer interface (BCI) aims at creating new communication channels without depending on brain’s normal output channels of peripheral nerves and muscles. However, natural and sophisticated interactions manner between brain and computer still remain challenging. In this paper, we investigate how the duration of event-related desynchronization/synchronization (ERD/ERS) caused by motor imagery (MI) can be modulated and used as an additional control parameter beyond simple binary decisions. Furthermore, using the non-time-locked properties of sustained (de)synchronization, we have developed an asynchronous BCI system for driving a car in 3D virtual reality environment (VRE) based on cumulative incremental control strategy. The extensive real time experiments confirmed that our new approach is able to drive smoothly a virtual car within challenging VRE only by the MI tasks without involving any muscular activities. Supported by the National High-Tech Research Program of China (Grant No. 2006AA01Z125) and the National Basic Research Program of China (Grant No. 2005CB724301)
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion
Qibin Zhao,Liqing Zhang,Andrzej Cichocki
Computer Science , 2015,
Abstract: Tucker decomposition is the cornerstone of modern machine learning on tensorial data analysis, which have attracted considerable attention for multiway feature extraction, compressive sensing, and tensor completion. The most challenging problem is related to determination of model complexity (i.e., multilinear rank), especially when noise and missing data are present. In addition, existing methods cannot take into account uncertainty information of latent factors, resulting in low generalization performance. To address these issues, we present a class of probabilistic generative Tucker models for tensor decomposition and completion with structural sparsity over multilinear latent space. To exploit structural sparse modeling, we introduce two group sparsity inducing priors by hierarchial representation of Laplace and Student-t distributions, which facilitates fully posterior inference. For model learning, we derived variational Bayesian inferences over all model (hyper)parameters, and developed efficient and scalable algorithms based on multilinear operations. Our methods can automatically adapt model complexity and infer an optimal multilinear rank by the principle of maximum lower bound of model evidence. Experimental results and comparisons on synthetic, chemometrics and neuroimaging data demonstrate remarkable performance of our models for recovering ground-truth of multilinear rank and missing entries.
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
Qibin Zhao,Liqing Zhang,Andrzej Cichocki
Computer Science , 2014, DOI: 10.1109/TPAMI.2015.2392756
Abstract: CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank. In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.
Recent progress on renewable energy in engineering thermophysics
JianZhong Xu,HongGuang Jin,Jun Sui,QiBin Liu,MingMing Zhang
Chinese Science Bulletin , 2012, DOI: 10.1007/s11434-012-5532-1
Abstract: This article portrays a concise review on the state-of-the-art advancements in methodologies and applications of engineering thermophysics for renewable energy, which includes wind energy, solar energy, geothermal energy, biomass energy, hydroelectric energy and CO2 capture, transportation and storage.
MicroRNA expression profiling during the life cycle of the silkworm (Bombyx mori)
Shiping Liu, Liang Zhang, Qibin Li, Ping Zhao, Jun Duan, Daojun Cheng, Zhonghuai Xiang, Qingyou Xia
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-284
Abstract: After publication of article [1], we became aware of the fact that Figures one C (let-7a and let-7a#), four B (bmo-let-7a and bmo-let-7a#) and five B (let-7a and let-7a#) were duplicated from another published article [2]. In light of these problems, the authors in consultation with the journal's Editors, have decided to retract article [1] from BMC Genomics. The authors are currently preparing a new manuscript clarifying the role of let-7a during the life cycle of the silkworm.
MicroRNA expression profiling during the life cycle of the silkworm (Bombyx mori)
Shiping Liu, Liang Zhang, Qibin Li, Ping Zhao, Jun Duan, Daojun Cheng, Zhonghuai Xiang, Qingyou Xia
BMC Genomics , 2009, DOI: 10.1186/1471-2164-10-455
Abstract: Our results show that miRNAs display a wide variety of expression profiles over the whole life of the silkworm, including continuous expression from embryo to adult (miR-184), up-regulation over the entire life cycle (let-7 and miR-100), down-regulation over the entire life cycle (miR-124), expression associated with embryogenesis (miR-29 and miR-92), up-regulation from early 3rd instar to pupa (miR-275), and complementary pulses in expression between miR-34b and miR-275. Stage-by-stage examinations revealed further expression patterns, such as emergence at specific time-points during embryogenesis and up-regulation of miRNA groups in late embryos (miR-1 and bantam), expression associated with stage transition between instar and molt larval stages (miR-34b), expression associated with silk gland growth and spinning activity (miR-274), continuous high expression from the spinning larval to pupal and adult stages (miR-252 and miR-31a), a coordinate expression trough in day 3 pupae of both sexes (miR-10b and miR-281), up-regulation in pupal metamorphosis of both sexes (miR-29b), and down-regulation in pupal metamorphosis of both sexes (miR-275).We present the full-scale expression profiles of miRNAs throughout the life cycle of Bombyx mori. The whole-life expression profile was further investigated via stage-by-stage analysis. Our data provide an important resource for more detailed functional analysis of miRNAs in this animal.MiRNAs are an abundant class of small (~22 nucleotides) noncoding RNAs expressed by a variety of eukaryotic organisms and viruses [1,2], which represent at least 1% of predicted genes within the genomes of individual species [3]. A mammalian genome may contain >500 genes encoding miRNAs [4,5]. Accumulating evidence shows that miRNAs function in a broad range of biological processes, including development, cellular differentiation, proliferation, metabolism and apoptosis [1,6-8]. Organisms devoid of miRNAs undergo arrest during development [9,10].
Identification and Comparative Analysis of Cadmium Tolerance-Associated miRNAs and Their Targets in Two Soybean Genotypes
Xiaolong Fang, Yunyun Zhao, Qibin Ma, Yian Huang, Peng Wang, Jie Zhang, Hai Nian, Cunyi Yang
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0081471
Abstract: MicroRNAs (miRNAs) play crucial roles in regulating the expression of various stress responses genes in plants. To investigate soybean (Glycine max) miRNAs involved in the response to cadmium (Cd), microarrays containing 953 unique miRNA probes were employed to identify differences in the expression patterns of the miRNAs between different genotypes, Huaxia3 (HX3, Cd-tolerant) and Zhonghuang24 (ZH24, Cd-sensitive). Twenty six Cd-responsive miRNAs were identified in total. Among them, nine were detected in both cultivars, while five were expressed only in HX3 and 12 were only in ZH24. The expression of 16 miRNAs was tested by qRT-PCR and most of the identified miRNAs were found to have similar expression patterns with microarray. Three hundred and seventy six target genes were identified for 204 miRNAs from a mixture degradome library, which was constructed from the root of HX3 and ZH24 with or without Cd treatment. Fifty five genes were identified to be cleaved by 14 Cd-responsive miRNAs. Gene ontology (GO) annotations showed that these target transcripts are implicated in a broad range of biological processes. In addition, the expression patterns of ten target genes were validated by qRT-PCR. The characterization of the miRNAs and the associated target genes in response to Cd exposure provides a framework for understanding the molecular mechanism of heavy metal tolerance in plants.
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