检索结果-内蒙古大学图书馆

19th International Conference on Mobility, Sensing and Networking (MSN)

作者： Yang, Hongyu Zhang, Tao Hu, Ze Zhang, Liang Cheng, Xiang Civil Aviat Univ China Sch Safety Sci & Engn Tianjin Peoples R China Univ Arizona Sch Informat Tucson AZ USA Yangzhou Univ Sch Informat Engn Yangzhou Peoples R China

ISBN: (纸本)9798350358261;9798350358278

The malicious domains generated by domain generation algorithm (DGA) are a threat to network security and the existing DGA domain detection methods commonly represent domain features by scalars, resulting in damage to the feature structure. To cope with the above issues, an improved capsule network for DGA domain detection was proposed. Firstly, the original samples were numerically processed and converted to the domain word vectors. Secondly, we built a n-grams feature extraction network based on residual network to extract domain features. Thirdly, we designed an improved capsule network to classify the domains according to the domain features. The domain features were converted to primary capsules. Finally, an improved dynamic routing algorithm was used to generate high-level capsules, whose lengths were used as auxiliary information for detecting domains. The experimental results show that compared with state-of-the-art methods, our method has remarkable detection performance.

关键词： DGA domain detection n-grams feature extraction capsule network dynamic routing algorithm

来源：评论

学校读者我要写书评

暂无评论

dynamic routing-based Multimodal Neural Network for Multi-sensory Fault Diagnosis of Induction Motor

引用

JOURNAL OF MANUFACTURING SYSTEMS 2020年 55卷 264-272页

作者： Fu, Peilun Wang, Jinjiang Zhang, Xing Zhang, Laibin Gao, Robert X. China Univ Petr Sch Safety & Ocean Engn Beijing 102249 Peoples R China PetroChina Pipeline Res & Dev Ctr Langfang 065099 Hebei Peoples R China Case Western Reserve Univ Dept Mech & Aerosp Engn Cleveland OH 44106 USA

Induction motor is the main drive power in modern manufacturing, and timely fault diagnosis of induction motor is of significance to production safety, part quality and maintenance cost control. Data fusion-based diagnosis is attractive for effective utilization of multi-source monitoring information of motors with the development of industrial internet of things. A new multi-sensory fusion model is proposed, named dynamic routing-based multimodal neural network (DRMNN), following the paradigm of multimodal deep learning (MDL). Specifically, the fusion of vibration and stator current signals are investigated. A multimodal feature extraction scheme is designed for dimensionality reduction and invariant features capturing based on multisource information. Since it is necessary to determine the importance of each modality, a dynamic routing algorithm is introduced in the decision layer to adaptively assign proper weights to different modalities. The effectiveness and robustness of developed DRMNN is demonstrated in the experimental studies performed on a motor test rig. In comparison with similar neural networks without data fusion and other state-of-art fusion techniques, the proposed DRMNN yields better performance.

关键词： Induction motor fault diagnosis data fusion multimodal deep learning dynamic routing algorithm

来源：评论

学校读者我要写书评

暂无评论

Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018-2020

引用

REMOTE SENSING 2022年第3期14卷

作者： Zeng, Qiaolin Xie, Tianshou Zhu, Songyan Fan, Meng Chen, Liangfu Tian, Yu Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Inst Meteorol Sci Chongqing 401147 Peoples R China Univ Exeter Dept Geog Rennes Dr Exeter EX4 4RJ Devon England Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China

Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM2.5 measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R-2) increased by 0.05 +/- 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 +/- 1.0 mu g/m(3), 8 +/- 3%, and 1.40 +/- 0.2 mu g/m(3), respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R-2 values of the validation dataset using CapsNet improved by 4 +/- 2%, and RMSE, MRE, and MAE decreased by 1.50 +/- 0.4 mu g/m(3), 5%, and 0.60 +/- 0.2 mu g/m(3), respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R-2 values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study propose

关键词： aerosol optical depth PM2.5 concentration dynamic routing algorithm CapsNet DNN

来源：评论

学校读者我要写书评

暂无评论

BG-SAC: Entity relationship classification model based on Self-Attention supported Capsule Networks

引用

APPLIED SOFT COMPUTING 2020年 91卷

作者： Peng, Dunlu Zhang, Dongdong Liu, Cong Lu, Jing Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Key Lab Modern Opt Syst Shanghai 20093 Peoples R China

To date, deep learning techniques, especially the combination of convolutional neural networks and recurrent neural networks with the attention mechanism, have been the state-of-the-art solutions for processing relation extraction and classification tasks. However, the neural network model constructed by this method cannot make full use of the labeled entities and their positional information in the relation classification, or even performs poorly on the small sample dataset. To address these issues, this paper proposes an entity relationship classification model BG-SAC, which combines BiGRU, Self-Attention mechanism and Capsule Networks. BG-SAC primarily uses BiGRU to obtain sentence sequential information and context-based semantic information, and then is coupled with the Self-Attention mechanism to get the correlation between words. Capsule Networks are used to acquire the positional information of entities. Eventually the probability that entities belong to a certain relationship category is calculated through the length of a capsule, so as to determine the relationship between entities and realize the classification of entity relationship. The experimental results show that the proposed model can effectively capture the word positional information and improve the classification effect with small sample datasets. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Entity relationship classification Self-Attention mechanism Capsule Networks dynamic routing algorithm

来源：评论

学校读者我要写书评

暂无评论

A dynamic and Mixed routing algorithm for 2D Mesh NoC

A Dynamic and Mixed Routing Algorithm for 2D Mesh NoC

引用

IEEE International Conference on Anti-Counterfeiting, Security and Identification (ASID)

作者： Du, Gaoming Liang, Dayi Song, Yukun Zhang, Duoli Hefei Univ Technol Inst VLSI Design Hefei Peoples R China

ISBN: (纸本)9781479911097

To solve the congestion of NoC caused by overload such as injection rate increase, this paper proposes a novel routing algorithm, that is dynamic and Mixed routing(MIXROUT) which is based on XY routing(XY) and Multiple and Load-Balance Path routing(MULTI). Although MULTI is an adaptive routing that can relieve traffic congestion state, it has a higher operating power and temperature than other routing mechanism. So a dynamic routing strategy is proposed on the status of the network. It works as follows: MULTI is selected as the routing algorithm when NoC is in heavy load state, otherwise XY is chosen for a lower operating power and temperature. Experimental results show that under the condition of 4*4 topology sizes, MIXROUT is better than XY and MULTI used singly in term of thermal gradient, with a value 1.882 degrees C, 4.116 degrees C and 4.542 degrees C respectively.

关键词： dynamic routing algorithm Power Temperature

来源：评论

学校读者我要写书评

暂无评论

SC-BiCapsNet: A Sentiment Classification Model Based on Bi-Channel Capsule Network

引用

IEEE ACCESS 2019年 7卷 171801-171813页

作者： Zhang, Kejun Jiao, Meng Chen, Xiaoming Wang, Zhiqiang Liu, Biao Liu, Lei Beijing Inst Elect Technol Dept Cyberspace Secur Beijing 100071 Peoples R China

Most text sentiment classification methods have the shortcomings of insufficient semantic understanding and low classification accuracy. To solve these problems, a sentiment classification model based on capsule network (SC-BiCapsNet) was proposed in this paper. In the stage of word vectorization, the G-Word model is presented to improve the ability of text information representation. In the stage of text semantic representation, the COL-Att model is presented to optimize the semantic coding structure and enhance the effect of text semantic expression. In the stage of text classification, the capsule network is devised, and its core dynamic routing algorithm can increase the weight value of important features, thus improving the accuracy of classification results. This paper evaluates the performance of SC-BiCapsNet model on two text datasets, IMDB and NLPCC 2014 dataset. The experimental results demonstrate that our method outperforms the results of other related models on IMDB dataset and gets the highest accuracy of 0.9238. Meanwhile, comparing with the best system in NLPCC2014-Task II, the F1-scsore of SC-BiCapsNet has increased by at least 1.5%, which proves the validity of SC-BiCapsNet. In the future work, we will try to reduce the time consumption and improve the generalization ability of the model.

关键词： Text sentiment classification capsule network dynamic routing algorithm deep learning

来源：评论

学校读者我要写书评

暂无评论

Vehicle Type Recognition using Capsule Network 31

Vehicle Type Recognition using Capsule Network

引用

31st Chinese Control And Decision Conference (CCDC)

作者： Zhang, Zhiyong Zhang, Duanjin Wei, Hongbin Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

ISBN: (纸本)9781728101057

Vehicle type recognition plays an increasingly important role in the problem of dealing with traffic safety. In order to address this problem, a novel vehicle type recognition model based on capsule network is proposed, which dynamic routing algorithm of the capsule network is redesigned to save training time and to accelerate convergence rate. To compare with the original model, we conduct experiments on the dataset including five different vehicle types from different angles and lighting conditions. It is proved that the proposed model costs only half time of original model while maintaining a high recognition rate, and the reconstruction from the output of capsule network keeps important details of vehicle types.

关键词： Vehicle type recognition Capsule network dynamic routing algorithm Reconstruction

来源：评论

学校读者我要写书评

暂无评论

Spectral-spatial classification of hyperspectral remote sensing image based on capsule network

引用

JOURNAL OF ENGINEERING-JOE 2019年第21期2019卷 7352-7355页

作者： Jia, Sen Zhao, Baojun Tang, Linbo Feng, Fan Wang, WenZheng Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China

Hyperspectral image (HSI) classification is a hot topic in remote sensing community;many researchers have made a great deal of effort in this domain. Recently, deep learning-based manner paves a new way to better classification accuracy. However, the flow of information between layers and layers (e.g. max-pooling) in traditional deep architecture turns out to be ineffective. In this study, a novel spectral-spatial classification framework for HSI based on Capsule Network (CapsNet) and dynamic routing algorithm is introduced. The proposed architecture is composed of a hybrid of 1D and 2D convolutional layers and two capsule layers for better and effective mining and combining features. Consequently, experiments on two popular dataset indicate that CapsNet-based framework outperforms traditional CNN-based counterparts. Moreover, this study reveals great potential for CapsNet model in the field of HSI classification.

关键词： hyperspectral imaging learning (artificial intelligence) feature extraction image classification remote sensing geophysical image processing convolutional neural nets hyperspectral remote sensing image capsule network hyperspectral image classification deep learning-based manner dynamic routing algorithm 2D convolutional layers capsule layers CapsNet-based framework HSI classification deep architecture spectral-spatial classification framework CNN-based counterparts 1D convolutional layers

来源：评论

学校读者我要写书评

暂无评论

Comparing Demand Responsive and Conventional Public Transport in a Low Demand Context 13

Comparing Demand Responsive and Conventional Public Transpor...

引用

14th IEEE International Conference on Pervasive Computing and Communication (PerCom)

作者： Kashani, Zahra Navidi Ronald, Nicole Winter, Stephan Univ Melbourne Dept Infrastruct Engn Melbourne Vic Australia Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic Australia

ISBN: (纸本)9781509019410

Considering the sprawl of the cities, the conventional public transport (CPT) with fixed route and fixed schedule becomes less efficient and desirable every day. However, the emerging technologies in computation and communication are facilitating more adaptive types of public transport, such as Demand Responsive Transport (DRT) systems, which operate according to the demand. It is crucial to study the feasibility and advantages of these systems before implementation to prevent failure and financial loss. In this work, a realistic model is provided by incorporating a dynamic routing algorithm into an agent-based traffic simulation to compare DRT and CPT. This model provides a high spatial and temporal granularity, which makes it possible to analyze the results on an individual level. The results showed that replacing CPT with DRT will improve the mobility by decreasing the perceived travel time by passengers and without any extra cost under certain circumstances.

关键词： demand responsive transport modelling MATSim dynamic routing algorithm

来源：评论

学校读者我要写书评

暂无评论

A New Clustering Based routing algorithm for NoC

A New Clustering Based Routing Algorithm for NoC

引用

International Conference on Machine Learning, Electrical and Mechanical Engineering (ICMLEME’2014)

作者： Abdelmalek Bouguettaya Mohamed Taher Kimour Salah Toumi LERICA Laboratory Department of electronic LASE Laboratory Department of computer science University of Badji Mokhtar-Annaba

With the increasing number of the integrated cores on a single chip,research into Network on Chip(No C) becomes *** C has been proposed as a solution to replace the classic interconnections of Systems on Chip(So Cs) *** and routing algorithm are important keys of the No C *** this paper,we propose a new fault-tolerant routing algorithm for Networks on Chip(No Cs).The proposed algorithm is based on a combination between a dynamic routing algorithm and a communication load clustering technique in a 2D mesh topology.

关键词： Networks on chip dynamic routing algorithm Fault tolerance Clustering Mesh topology

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：