检索结果-内蒙古大学图书馆

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Jinrui Shen Jiahao Zheng Xiaoping Wang the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China the School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

With the development of deep learning, emotion recognition tasks are more inclined to use multimodal data and adequate supervised information to improve accuracy. In this work, MMTrans-MT (Multimodal Transformer-Multitask), the framework for multimodal emotion recognition using multitask learning is proposed. It has three modules: modalities representation module, multimodal fusion module, and multitask output module. Three modalities, i.e, words, audio and video, are comprehensively utilized to carry out emotion recognition by a simple but efficient fusion model based on Transformer. As for multitask learning, the two tasks are defined as categorical emotion classification and dimensional emotion regression. Considering a potential mapping relationship between two kinds of emotion model, multitask learning is adopted to make the two tasks promote each other and improve recognition accuracy. We conduct experiments on CMU-MOSEI and IEMOCAP datasets. Comprehensive experiments show that the accuracy of recognition using multimodal information is higher than that using unimodal information. Adopting multitask learning promotes the performance of emotion recognition.

关键词： Learning systems Deep learning Emotion recognition Correlation Computational modeling Task analysis Computational intelligence

来源：评论

学校读者我要写书评

暂无评论

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

引用

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2019年第11期27卷 2263-2273页

作者： Cui, Yuqi Xu, Yifan Wu, Dongrui Huazhong Univ Sci & Technol Minist Educ Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Key Lab Wuhan 430074 Hubei Peoples R China

Drowsy driving is pervasive, and also a major cause of traffic accidents. Estimating a driver's drowsiness level by monitoring the electroencephalogram (EEG) signal and taking preventative actions accordingly may improve driving safety. However, individual differences among different drivers make this task very challenging. A calibration session is usually required to collect some subject-specific data and tune the model parameters before applying it to a new subject, which is very inconvenient and not user-friendly. Many approaches have been proposed to reduce the calibration effort, but few can completely eliminate it. This paper proposes a novel approach, feature weighted episodic training (FWET), to completely eliminate the calibration requirement. It integrates two techniques: feature weighting to learn the importance of different features, and episodic training for domain generalization. Experiments on EEG-based driver drowsiness estimation demonstrated that both feature weighting and episodic training are effective, and their integration can further improve the generalization performance. FWET does not need any labelled or unlabelled calibration data from the new subject, and hence could be very useful in plug-and-play brain-computer interfaces.

关键词： Drowsy driving domain generalization EEG episodic training feature weighting

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Flow Model-Based Impact Assessment of Cyberattacks for Critical Infrastructures

引用

IEEE SYSTEMS JOURNAL 2019年第4期13卷 3944-3955页

作者： Zhu, Qianxiang Qin, Yuanqing Zhou, Chunjie Fei, Li Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China

Critical infrastructures (CIs) are essential for the national security, economy, and public safety, whose protection against cyberattacks has become the focus of significant attention. Impact assessment plays an important role in this protection, which provides real-time situations for security managers. The assessment of CIs not only need to analyze the cyberattack's impact on any specific station, but also to consider the spreading process of the negative impact. However, few research in this domain consider the above two aspects simultaneously due to the complexity of CIs and their operation. To tackle this problem, a hierarchical flow model (HFM)-based impact assessment is presented. In this approach, a novel HFM method is proposed to describe a system from a function system perspective, which combines a flow model and hierarchical knowledge. By using this method, a CI model which considers cyber-physical interaction within a station, dependence among stations, and the topological structure of the physical network is established. Next, based on the CI model, an impact assessment is proposed to quantify the loss which is caused by the impact spreading within the CI network. Finally, case studies on a gas supply system are conducted to verify the effectiveness of the proposed approach.

关键词： Critical infrastructures cybersecurity protection hierarchical flow model impact assessment system of systems

来源：评论

学校读者我要写书评

暂无评论

Multichannel Synchronous Hydrodynamic Gating Coupling with Concentration Gradient Generator for High-Throughput Probing Dynamic Signaling of Single Cells

引用

ANALYTICAL CHEMISTRY 2020年第17期92卷 12062-12070页

作者： Guo, Yiran Gao, Zhaolong Liu, Yinan Li, Shunji Zhu, Jinchi Chen, Peng Liu, Bi-Feng Huazhong Univ Sci & Technol Hubei Bioinformat & Mol Imaging Key Lab Syst Biol ThemeDept Biomed EngnColl Life Sci & Key Lab Biomed Photon MOEWuhan Natl Lab Optoelec Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Coll Life Sci & Technol Dept Genet Wuhan 430074 Peoples R China

Cell signaling greatly affected by complicated and temporally dynamic extracellular microenvironments controls most of the physiological functions in vivo. To reconstruct or simulate such microenvironments in vitro represents a fundamental approach for revealing the underlying mechanisms of those sophisticated processes. Recent advances in microfluidics have added a new dimension to cell signaling analysis, for example, concentration gradient generators (amplitude aspect) or hydrodynamic gating strategy (frequency aspect), but it is still challengeable to capture single-cell dynamic signaling in response to a mimicked extracellular microenvironment with varied stimuli waveforms of different amplitude and frequency in a high-throughput manner. In this article, we proposed a novel microfluidic strategy coupling multichannel synchronous hydrodynamic gating with microfluidic concentration gradient generators (mu MHG-CGG) to probe dynamic signaling of single cells with high throughput. The mu MHG-CGG allows rapid delivery of dynamic chemical signals in both high frequency (as high as 670 mHz) and multiple amplitude domains at the same time and simultaneously high-throughput probing cell dynamics at single-cell resolution in real time. By applying the proposed system, the mechanisms for encoding/decoding systems (termed "frequency coding" or "amplitude coding") via GPCRs-mediated signaling pathways responding to histamine (HA) and adenosine triphosphate (ATP) in single HeLa cells were investigated. The optimal drug concentrations of single cells responses to HA and ATP individually or in combination were also successfully discussed, allowing us to obtain both single-cell heterogeneity and statistics from the cell population.

关键词： Microfluidics Molecular diffusion High-throughput Single cell Cell signaling

来源：评论

学校读者我要写书评

暂无评论

A study on a special DNA nanotube assembled from two single-stranded tiles

引用

NANOTECHNOLOGY 2019年第11期30卷 115602-115602页

作者： Xu, Fei Wu, Tingfang Shi, Xiaolong Pan, Linqiang Huazhong Univ Sci & Technol Sch Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China

Single-stranded tile (SST) strategy offers precise control over the circumferences of nanotubes while the kinetic trap in the process of the self-assembly prevents the formation of wider tubes. Here, we report a simple and efficient method to build DNA nanotubes using only 2 SSTs via one-pot annealing. The diameters of the 2-SST nanotubes were much larger than what the kinetic trap theory would predict, indicating a new mechanism was at play in the formation of these nanotubes. Further investigation suggested that the 2-SST nanotubes were assembled through a hierarchical pathway that involved an intermediate formation of 2-SST nano-lines.

关键词： DNA nanotube self-assembly hierarchical assembly DNA nanotechnology drug delivery

来源：评论

学校读者我要写书评

暂无评论

V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results

arXiv

引用

arXiv 2024年

作者： Wang, Jiaqi Zang, Yuhang Zhang, Pan Chu, Tao Cao, Yuhang Sun, Zeyi Liu, Ziyu Dong, Xiaoyi Wu, Tong Lin, Dahua Chen, Zeming Wang, Zhi Meng, Lingchen Yao, Wenhao Yang, Jianwei Wu, Sihong Chen, Zhineng Wu, Zuxuan Jiang, Yu-Gang Wu, Peixi Chai, Bosong Nie, Xuan Yan, Longquan Wang, Zeyu Zhou, Qifan Wang, Boning Huang, Jiaqi Xu, Zunnan Li, Xiu Yuan, Kehong Zu, Yanyan Hao, Jiayao Gao, Qiong Jiao, Licheng Shanghai AI Laboratory China Chinese University of Hong Kong Hong Kong Shanghai Jiao Tong University China Wuhan University China Shenzhen International Graduate School Tsinghua University China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Microsoft Research Redmond United States University of Science and Technology of China China Zhejiang University China Northwestern Polytechnical University China Northwest University China Tsinghua Shenzhen International Graduate School Tsinghua University China Intelligent Perception and Image Understanding Lab Xidian University China

Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown or unseen objects. The challenges necessitate the development of public benchmarks and challenges to advance the field of object detection. Inspired by the success of previous COCO [15] and LVIS [11] Challenges, we organize the V3Det Challenge 2024 in conjunction with the 4th Open World Vision Workshop: Visual Perception via Learning in an Open World (VPLOW) at CVPR 2024, Seattle, US. This challenge aims to push the boundaries of object detection research and encourage innovation in this field. The V3Det Challenge 2024 consists of two tracks: 1) Vast Vocabulary Object Detection: This track focuses on detecting objects from a large set of 13204 categories, testing the detection algorithm's ability to recognize and locate diverse objects. 2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects. In the following sections, we will provide a comprehensive summary and analysis of the solutions submitted by participants. By analyzing the methods and solutions presented, we aim to inspire future research directions in vast vocabulary and open-vocabulary object detection, driving progress in this field. Challenge homepage: https://***/challenge. © 2024, CC BY-NC-SA.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Cascading and Enhanced Residual Networks for Accurate Single-image Super-Resolution

引用

IEEE TRANSACTIONS ON CYBERNETICS 2021年第1期51卷 115-125页

作者： Lan, Rushi Sun, Long Liu, Zhenbing Lu, Huimin Su, Zhixun Pang, Cheng Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu Fukuoka 8048550 Japan Dalian Univ Technol Sch Math Sci Dalian 116024 Peoples R China Guilin Univ Elect Technol Natl Local Joint Engn Res Ctr Satellite Nav & Loc Guilin 541004 Peoples R China

Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single-image super-resolution (SISR) field. However, the majority of existing CNN-based models maintain high performance with massive parameters and exceedingly deeper structures. Moreover, several algorithms essentially have underused the low-level features, thus causing relatively low performance. In this article, we address these problems by exploring two strategies based on novel local wider residual blocks (LWRBs) to effectively extract the image features for SISR. We propose a cascading residual network (CRN) that contains several locally sharing groups (LSGs), in which the cascading mechanism not only promotes the propagation of features and the gradient but also eases the model training. Besides, we present another enhanced residual network (ERN) for image resolution enhancement. ERN employs a dual global pathway structure that incorporates nonlocal operations to catch long-distance spatial features from the the original low-resolution (LR) input. To obtain the feature representation of the input at different scales, we further introduce a multiscale block (MSB) to directly detect low-level features from the LR image. The experimental results on four benchmark datasets have demonstrated that our models outperform most of the advanced methods while still retaining a reasonable number of parameters.

关键词： Feature extraction image reconstruction Spatial resolution Convolutional neural networks Training Graphics Convolutional neural network multiscale learning residual learning single-image super-resolution (SISR)

来源：评论

学校读者我要写书评

暂无评论

EEG-Based Driver Drowsiness Estimation Using an Online Multi-View and Transfer TSK Fuzzy System

引用

IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2021年第3期22卷 1752-1764页

作者： Jiang, Yizhang Zhang, Yuanpeng Lin, Chuang Wu, Dongrui Lin, Chin-Teng Jiangnan Univ Sch Digital Media Wuxi 214122 Jiangsu Peoples R China Nantong Univ Dept Med Informat Nantong 226001 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ctr Artificial Intelligence Ultimo NSW 2007 Australia

In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibration data. However, most of existing TL-based models are offline, non-transparent, and in which features are only represented from one view (usually only one algorithm is used to extract features). In this paper, we consider an online multi-view regression model with high interpretability. By taking the 1-order TSK fuzzy system as the basic regression component and injecting the nature of the multi-view settings into the existing transfer learning framework and enforcing the consistencies across different views, we propose an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation. In this novel model, features in both the source domain and the target domain are represented from multi-view perspectives such that more pattern information can be utilized during model training. Also, comparing with offline training, the proposed online fuzzy system meets the practical requirements more competently. An experiment on a driving dataset demonstrates that the proposed fuzzy system has smaller drowsiness estimation errors and higher interpretability than introduced benchmarking models.

关键词： Brain modeling Electroencephalography Estimation Vehicles Fuzzy systems Feature extraction Accidents Transfer learning multi-view learning TSK fuzzy systems EEG

来源：评论

学校读者我要写书评

暂无评论

GTNet: Generative Transfer Network for Zero-Shot Object Detection 34

GTNet: Generative Transfer Network for Zero-Shot Object Dete...

引用

34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence

作者： Zhao, Shizhen Gao, Changxin Shao, Yuanjie Li, Lerenhan Yu, Changqian Ji, Zhong Sang, Nang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan Hubei Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781577358350

We propose a Generative Transfer Network (GTNet) for zero-shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen class features, which are applied to train a new classification layer for the Object Detection Module. In order to synthesize features for each unseen class with both the intra-class variance and the IoU variance, we design an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN consists of three unit models: Class Feature Generating Unit (CFU), Foreground Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU). CFU generates unseen features with the intra-class variance conditioned on the class semantic embeddings. FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively. We evaluate our method on three public datasets and the results demonstrate that our method performs favorably against the state-of-the-art ZSD approaches.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

An image Encryption Method Based on Logistic Chaotic Mapping and DNA Coding 11

An Image Encryption Method Based on Logistic Chaotic Mapping...

引用

SPIE 11th International Symposium on Multispectral image processing and Pattern Recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Lin, Jiayu Zhao, Kangnan Cai, Xin Li, Danning Wang, Zuxi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Natl Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9781510636422

This paper proposes a new image encryption algorithm, which is based on Logistic chaotic mapping and DNA coding. A variable parameter logistic mapping coupled chaotic system is proposed to generate pseudo-random sequences with good pseudo-random properties. Before encrypting plain-text images, the distinct features of some special images are eliminated by superposing them with a pseudo-random image. Combining with DNA encoding operation, an encrypting algorithm of cross-operation of diffusion and peunutation is designed. The simulation results show that the proposed algorithm has large key space, high sensitivity to key and plain-text, and good robustness to resist exhaustive attack, statistic attack, differential attack, etc. It shows that the proposed method has good encryption effect and security.

关键词： Chaos DNA code Pseudo random number image encryption

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：