检索结果-内蒙古大学图书馆

42nd Chinese Control Conference, CCC 2023

作者： Li, Zexin Liu, Guoqiang Zhao, Wei Li, Xi School of Artificial Intelligence and Automation Huazhong University of Science & Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China

ISBN: (纸本)9789887581543

The multi-stack fuel cell system (MFCS) is an important basis for the application of solid oxide fuel cell (SOFC) technology to large-scale power generation systems. MFCS can provide higher power, longer lifetime and better reliability. Different from the single-stack fuel cell system (SFCS), there may be some inconsistencies among the stacks in the MFCS. If the inconsistencies are ignored, they will significantly affect the lifetime of the MFCS. This study focuses on the lifetime extension of multi-stack SOFC power generation systems with inconsistent stacks. Since the multi-stack system of SOFC generally cannot control the flow rate of each stack respectively, it brings a difficulty to the control of the MFCS with inconsistencies among the stacks. In order to overcome this difficulty, this paper built a multi-stack SOFC system model, which can characterize the degradation and inconsistencies among SOFC stacks. And then designed a power distribution strategy based on fuzzy control to extend the lifetime of the MFCS. The strategy was verified by simulations. The simulation results show that the strategy can effectively control the inconsistencies among the stacks and significantly extend the lifetime of the MFCS. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Fuzzy control

来源：评论

学校读者我要写书评

暂无评论

引用

Computer Science and Blockchain (CCSB), International Conference on

作者： Guimin Huang Canjun Shi Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance.

关键词： Computational modeling Semantics Knowledge based systems Syntactics Predictive models Feature extraction Vectors Natural language processing Convolutional neural networks Mutual information

来源：评论

学校读者我要写书评

暂无评论

EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation

引用

Science China(Information Sciences) 2024年第8期67卷 130-146页

作者： Yingjian LIU Jiang LI Xiaoping WANG Zhigang ZENG School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Institute of Artificial Intelligence Huazhong University of Science and Technology

Emotion recognition in conversation(ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper,we propose an emotional inertia and contagion-driven dependency modeling approach(EmotionIC) for ERC tasks. Our EmotionIC consists of three main components, i.e., identity masked multi-head attention(IMMHA), dialogue-based gated recurrent unit(DiaGRU), and skip-chain conditional random field(SkipCRF).Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention-and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while Dia GRU is utilized to extract speaker-and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark *** ablation studies confirm that our modules can effectively model emotional inertia and contagion.

关键词： emotion recognition in conversation emotional inertia and contagion multi-head attention gated recurrent unit conditional random field

来源：评论

学校读者我要写书评

暂无评论

Cooperative Target-Surrounding Control of Unmanned Surface Vessels Based on MADDPG 49

Cooperative Target-Surrounding Control of Unmanned Surface V...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Li, Taoman Zhu, Fei Gan, Zihan Zhou, Zexing Wang, Xiaotao Lu, Renzhi China Ship Scientific Research Center Wuxi214082 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China Wuhan University of Science and Technology Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan430081 China

ISBN: (纸本)9798350331820

This article proposes a multi-agent deep reinforce-ment learning algorithm to control a fleet of unmanned surface vessels (USVs) that encircle and capture sea targets. First, a simulation environment for USVs is established based on a dynamic model;two-dimensional control variables are used to control movements in three directions. Second, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed to achieve intelligent control of the USVs, using a reward function based on certain prior knowledge. Finally, centralized training and decentralized execution are used to complete the offline learning of multiple agents, and continuous action decisions are made based on the observations of the USV sensors. Simulations demonstrate that the method can capture stationary moving sea targets with any number of multi-agents under disturbances, and exhibits strong robustness and practicality. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Chinese Sentiment Analysis Model Incorporating Knowledge Graph and Deep Learning 7

A Chinese Sentiment Analysis Model Incorporating Knowledge G...

引用

7th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023

作者： Zhao, Shengping Chen, Jinlong Qin, Xingguo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9798350382877

With the rapid growth of social media and e-commerce, users are generating large amounts of textual review data on these platforms. Analyzing and mining these comment data is important for governments, enterprises, and consumers to make decisions. Therefore, sentiment analysis, especially for Chinese comments, has become an area of wide interest in industry and academia. Aiming at the characteristics of Chinese online reviews, this paper applies the techniques of knowledge graph and deep learning around sentence-level sentiment analysis and designs a sentiment analysis model that integrates different information such as sentiment knowledge graph and serialized annotation in a deep learning model to improve the accuracy and effectiveness of sentiment analysis of Chinese online reviews. This is important for improving products and services, understanding user needs, and making more informed business and policy decisions. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Cross-user Gesture Recognition Based on Deep Domain Adaptation Networks

Cross-user Gesture Recognition Based on Deep Domain Adaptati...

引用

第43届中国控制会议

作者： Zhongzheng Fu Xinrun He Haoyuan Wang Xingjian Chen Yixuan Li Jian Huang Ministry of Education Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This study introduces an innovative approach for gesture recognition in smart wearable devices using a deep domain adaptation model, focusing on the challenges posed by heterogeneous user environments and the need for precise, adaptable, and personalized gesture recognition systems. Traditional methods based on machine learning and deep learning techniques, while effective, struggle with the variance in data distribution among different users, leading to accuracy challenges. To address this,the study proposes the Domain Kernel-Alignment Adaptation Network(DKAN) model, utilizing a new Kernel-Aligned Multi-kernel Maximum Mean Discrepancy(KAMMD) method within deep networks. By calculating kernel alignments between source and target domains and adjusting weights of Gaussian kernels accordingly, DKAN emphasizes features conducive to transfer learning, enabling more accurate cross-user gesture recognition. The model achieves a significant average accuracy of 97.3% across 21 gestures, highlighting its potential in practical applications. This advancement addresses challenges in traditional gesture recognition methods and sets a new direction for future research in smart wearable technology.

关键词： Gesture recognition Kernel-alignment Domain adaptation

来源：评论

学校读者我要写书评

暂无评论

Multiple-instance CNN Improved by S3TA for Colon Cancer Classification with Unannotated Histopathological images 11

Multiple-instance CNN Improved by S3TA for Colon Cancer Clas...

引用

11th International Conference on Intelligent Control and Information processing, ICICIP 2021

作者： Ye, Tiange Lan, Rushi Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9781665425155

In this paper, we propose a new method for colon cancer classification from histopathological images, which can automatically analyze a given whole slide image (WSI). We usually classify cancer classification by referring a WSI, which is typically 20000 × 20000 pixels. The cost of obtaining WSIs with annotating cancer regions is very high. Multiple-instance learning (MIL) is a variant of supervised learning in which the instances in a bag share a single class label. That is, MIL only needs unannotated WSI. In recent years, MIL has developed a hard attention mechanism which has achieved good performance. However, this hard attention mechanism cannot notice the interior of each patch, i.e., it lacks soft attention mechanism. In this paper, a soft, sequential, spatial, top-down attention mechanism (which we abbreviate as S3TA) is used to make up for the lack of MIL attention mechanism. Finally, our experiments show that by varying the number of attention steps in S3TA, we achieved a better accuracy of 93.6% than the old model. © 2021 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

AN ADVERSARIAL AND DEEP HASHING-BASED HIERARCHICAL SUPERVISED CROSS-MODAL image AND TEXT RETRIEVAL ALGORITHM

引用

International Journal of Robotics and Automation 2024年第1期39卷 77-86页

作者： Chen, Ruidong Qiang, Baohua Zhou, Mingliang Zhang, Shihao Zheng, Hong Tang, Chenghua Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science Chongqing University Chongqing400044 China

With the rapid development of robotics and sensor technology, vast amounts of valuable multimodal data are collected. It is extremely critical for a variety of robots performing automated tasks to find relevant multimodal information quickly and efficiently in large amounts of data. In this paper, we propose an adversarial and deep hashing-based hierarchical supervised cross-modal image and text retrieval algorithm to perform semantic analysis and association modelling on image and text by making full use of the rich semantic information of the label hierarchy. First, the modal adversarial block and the modal differentiation network both perform adversarial learning to keep different modalities with the same semantics closest to each other in a common subspace. Second, the intra-label layer similarity loss and inter-label layer correlation loss are used to fully exploit the intrinsic similarity existing in each label layer and the correlation existing between label layers. Finally, an objective function for different semantic data is redesigned to keep data with different semantics away from each other in a common subspace, thus avoiding interference of retrieval by data of different semantics. The experimental results on two cross-modal retrieval datasets with hierarchically supervised information show that the proposed method substantially enhances retrieval performance and consistently outperforms other state-of-the-art methods. © 2024 Acta Press. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Sensitive Nanothermometer Based on DNA Triplex Structure 1

引用

17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Chen, Zhekun Hu, Yingxin Xie, Chun Chen, Kuiting Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

DNA has been used to construct a variety of nanoscale thermometers due to its unique thermodynamic properties. It has proven to be one of the most promising, yet challenging aspects of DNA-based thermometers, to obtain significant signal changes when the temperature changes slightly. Here, we propose a strategy to construct nanoscale thermometers with sensitive temperature responses based on DNA triplex. The thermometers consist of a CT-rich DNA strand and stabilizing strands, whose conformations vary with temperature. By adjusting the sequence design or introducing mismatch bases in stabilizing strands, the temperature response interval of the thermometer can be reduced to 7 ∘C, which lead to higher sensitivity compared to the interval of 12–15 ∘C for conventional designs. The temperature responses of the thermometer are characterized by fluorescence experiments. The fluorescence kinetics experiments demonstrate the good repeatability. The design of the nanothermometer will be helpful for constructing advanced nanosystem with sensitive temperature responses as well as long service times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thermometers

来源：评论

学校读者我要写书评

暂无评论

A Parallel Read-Write Circuit Design for Driving Memristor Crossbar Array

A Parallel Read-Write Circuit Design for Driving Memristor C...

引用

2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Jiang, Ningye Jiang, Mingxuan Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9798350316889

Memristor crossbar array is considered as a promising circuit module for accelerating neural networks. Because the memristor is tunable and multi-state, it is important to design applicable Read-Write (RW) circuit for driving memristor crossbar array. In this paper, a novel RW circuit based on analog circuits is proposed for tuning memristors in high-dimensional array parallelly, which is scalable and low-overhead. The standalone RW circuit schematic is presented for reading/writing single memristor and verified in circuit simulation. Then, we extend and apply this circuit into a two-dimensional memristor crossbar array. The performance of memristor crossbar array controlled by designed RW circuits is verified via the simulations under various machine learning datasets. The comparison with related works is presented, which demonstrates the advantages of the proposed RW circuit regarding overhead and anti-error. © 2023 IEEE.

关键词： Memristors

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：