检索结果-内蒙古大学图书馆

2nd IEEE Advanced information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Kang, Guoqing Yuan, Jiantao Qin, Taojie Gillani, Ameer Ahmed Pei, Wenjiang Southeast University School of Information Science and Engineering Nanjing China Department of Information and Communication Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China Faculty of Information Science and Technology COMSATS Institute of Information Technology Sahiwal Pakistan

ISBN: (纸本)9781538618035

D295 long term evolution (LTE) in unlicensed spectrum is a promising method to cope with the radically increasing requirement of data traffic. However, whether the unlicensed LTE (LTE-U) can provide transmissions comparatively at higher data rates depends on its channel access delay characteristic. In this paper, we focus on the LTE-U system's channel access delay distribution to study the potential for it to run and support the delay-sensitive traffic. We first develop a unified analytical model to analyze the coexistence of LTE-U system and Wi-Fi system, and then derive the close-form expression of the channel access delay under the condition of LTE-U system coexistence with Wi-Fi system. By Lattice-Poisson inversion algorithm we obtain the numerical results of the distribution of LTE-U's channel access delay and the analytical results match the simulated results demonstrating the accurate prediction of the distribution of channel access delay for LTE-U system by the proposed analytical model. © 2018 IEEE.

关键词： Analytical models

来源：评论

学校读者我要写书评

暂无评论

A Survey on Machine Learning Algorithms in Little-Labeled Data for Motor Imagery-Based Brain-computer Interfaces

引用

Journal of information Hiding and Privacy Protection 2019年第1期1卷 11-21页

作者： Yuxi Jia Feng Li Fei Wang Yan Gui School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore639798Singapore

The Brain-computer Interfaces(BCIs)had been proposed and used in therapeutics for ***,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the *** classified these algorithms into two categories,namely,and enhancing the representation and expanding the training ***,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so *** methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG *** result of these techniques showed the resolution of the challenges to some *** a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.

关键词： Brain-computer interface electroencephalography(EEG) machine learning

来源：评论

学校读者我要写书评

暂无评论

Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Statistical approach to detection of attacks for stochastic cyber-physical systems

arXiv

引用

arXiv 2020年

作者： Marelli, Damián Sui, Tianju Fu, Minyue Lu, Renquan School of Automation Guangdong University of Technology Guangzhou China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Control Science and Engineering Dalian University of Technology Dalian China School of Electrical Engineering and Computer Science University of Newcastle CallaghanNSW2308 Australia School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guandong University of Technology Guangzhou China

We study the problem of detecting an attack on a stochastic cyber-physical system. We aim to treat the problem in its most general form. We start by introducing the notion of asymptotically detectable attacks, as those attacks introducing changes to the system’s output statistics which persist asymptotically. We then provide a necessary and sufficient condition for asymptotic detectability. This condition preserves generality as it holds under no restrictive assumption on the system and attacking scheme. To show the importance of this condition, we apply it to detect certain attacking schemes which are undetectable using simple statistics. Our necessary and sufficient condition naturally leads to an algorithm which gives a confidence level for attack detection. We present simulation results to illustrate the performance of this algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

An evolutionary algorithm with a new operator and an adaptive strategy for large-scale portfolio problems 18

An evolutionary algorithm with a new operator and an adaptiv...

引用

2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Chen, Yi Zhou, Aimin Dou, Liang Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University 3663 North Zhongshan Road Shanghai China

ISBN: (纸本)9781450357647

A portfolio optimization problem involves optimal allocation of finite capital to a series of assets to achieve an acceptable trade-off between profit and risk in a given investment period. In the paper, the extended Markowitz's mean-variance portfolio optimization model is studied with some practical constraints. We introduce a new operator and an adaptive strategy for improving the performance of the multi-dimensional mapping algorithm (MDM) proposed specially for the portfolio optimization. Experimental results show that the modification is efficient on tackling large-scale portfolio problems. © 2018 Copyright held by the owner/author(s).

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Representation matching for remote quantum computing

arXiv

引用

arXiv 2020年

作者： Yang, Yuxiang Hayashi, Masahito Institute for Theoretical Physics ETH Zürich QICI Quantum Information and Computation Initiative Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong Hong Kong Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan

Many quantum computational tasks have inherent symmetries, suggesting a path to enhancing their efficiency and performance. Exploiting this observation, we propose representation matching, a generic probabilistic protocol for reducing the cost of quantum computation in a quantum network. We show that the representation matching protocol is capable of reducing the communication or memory cost to almost minimum in various tasks, including remote execution of unitary gate arrays, permutation gates and unitary conjugation, as well as the storage and retrieval of unitary gates. Copyright © 2020, The Authors. All rights reserved.

关键词： Cost reduction

来源：评论

学校读者我要写书评

暂无评论

Adversarial Learning for Distant Supervised Relation Extraction

引用

computers, Materials & Continua 2018年第4期55卷 121-136页

作者： Daojian Zeng Yuan Dai Feng Li R.Simon Sherratt Jin Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114P.R.China Department of Biomedical Engineering University of ReadingUK. School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114P.R.China.

Recently,many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction(DSRE).These approaches generally use a softmax classifier with cross-entropy loss,which inevitably brings the noise of artificial class NA into classification *** address the shortcoming,the classifier with ranking loss is employed to *** randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss ***,the majority of the generated negative class can be easily discriminated from positive class and will contribute little towards the *** by Generative Adversarial Networks(GANs),we use a neural network as the negative class generator to assist the training of our desired model,which acts as the discriminator in *** the alternating optimization of generator and discriminator,the generator is learning to produce more and more discriminable negative classes and the discriminator has to become better as *** framework is independent of the concrete form of generator and *** this paper,we use a two layers fully-connected neural network as the generator and the Piecewise Convolutional Neural Networks(PCNNs)as the *** results show that our proposed GAN-based method is effective and performs better than state-of-the-art methods.

关键词： Relation extraction generative adversarial networks distant supervision piecewise convolutional neural networks pair-wise ranking loss

来源：评论

学校读者我要写书评

暂无评论

A Novel Method for Multiple Biomedical Events Extraction with Reinforcement Learning and Knowledge Bases

A Novel Method for Multiple Biomedical Events Extraction wit...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weizhong Zhao Yao Zhao Xingpeng Jiang Tingting He Fan Liu Ning Li Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan Hubei PR China School of Computer Central China Normal University Wuhan Hubei PR China Central China Normal University Wuhan Hubei PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Comcast Silicon Valley Innovation Center Sunnyvale CA USA Chinese Academy of Sciences Institute of Information Engineering Beijing China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical event extraction is usually modeled as two sub-tasks: trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially but ignore the interaction between them. This paper proposes a novel method for multiple biomedical events extraction, in which the task of event extraction is modeled under a framework of reinforcement learning (RL). We treat the trigger identification and argument detection as main-task and subsidiary-task, respectively. And the result of argument detection is modeled as environmental information. In this way, the proposed method can capture the interaction between two sub-tasks, and the semantic associations among multiple biomedical events are also utilized effectively. Moreover, external biomedical knowledge bases are employed for representation learning of biomedical text. Comprehensive experiments are conducted on two widely used biomedical corpora, and results demonstrate that our method gains better performance compared to existing methods, especially in multiple biomedical events extraction.

关键词： Task analysis Biological system modeling Ontologies Semantics Data mining Feature extraction Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Deep Multi-Model Fusion for Single-Image Dehazing

Deep Multi-Model Fusion for Single-Image Dehazing

引用

International Conference on computer Vision (ICCV)

作者： Zijun Deng Lei Zhu Xiaowei Hu Chi-Wing Fu Xuemiao Xu Qing Zhang Jing Qin Pheng-Ann Heng South China University of Technology Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology CAS The Chinese University of Hong Kong State Key Laboratory of Subtropical Building Science Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Sun Yat-sen University The Hong Kong Polytechnic University CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology CAS

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

This paper presents a deep multi-model fusion network to attentively integrate multiple models to separate layers and boost the performance in single-image dehazing. To do so, we first formulate the attentional feature integration module to maximize the integration of the convolutional neural network (CNN) features at different CNN layers and generate the attentional multi-level integrated features (AMLIF). Then, from the AMLIF, we further predict a haze-free result for an atmospheric scattering model, as well as for four haze-layer separation models, and then fuse the results together to produce the final haze-free image. To evaluate the effectiveness of our method, we compare our network with several state-of-the-art methods on two widely-used dehazing benchmark datasets, as well as on two sets of real-world hazy images. Experimental results demonstrate clear quantitative and qualitative improvements of our method over the state-of-the-arts.

关键词： Atmospheric modeling Predictive models Scattering Computational modeling Neural networks computer vision Solid modeling

来源：评论

学校读者我要写书评

暂无评论

Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhirui Liao Xiaodi Huang Hiroshi Mamitsuka Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University Albury NSW Australia Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781665429825

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI).

关键词： Drugs Proteins Deep learning Solid modeling Three-dimensional displays Conferences Predictive models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：