检索结果-内蒙古大学图书馆

Natural Grasp Intention Recognition Based on Gaze in Human-Robot Interaction

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第4期27卷 2059-2070页

作者： Yang, Bo Huang, Jian Chen, Xinxing Li, Xiaolong Hasegawa, Yasuhisa Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Shenzhen Key Lab Biomimet Robot & Intelligent Syst Shenzhen 518055 Peoples R China Rehabil Robot Univ Southern Univ Sci & Technol Guangdong Prov Key Lab Human Augmentat Shenzhen 518055 Peoples R China Nagoya Univ Dept Micronano Mech Sci & Engn Furo cho Chikusa ku Nagoya 4648603 Japan

Objective: While neuroscience research has established a link between vision and intention, studies on gaze data features for intention recognition are absent. The majority of existing gaze-based intention recognition approaches are based on deliberate long-term fixation and suffer from insufficient accuracy. In order to address the lack of features and insufficient accuracy in previous studies, the primary objective of this study is to suppress noise from human gaze data and extract useful features for recognizing grasp intention. Methods: We conduct gaze movement evaluation experiments to investigate the characteristics of gaze motion. The target-attracted gaze movement model (TAGMM) is proposed as a quantitative description of gaze movement based on the findings. A Kalman filter (KF) is used to reduce the noise in the gaze data based on TAGMM. We conduct gaze-based natural grasp intention recognition evaluation experiments to collect the subject's gaze data. Four types of features describing gaze point dispersion (f(var)), gaze point movement (f(gm)), head movement (f(hm)), and distance from the gaze points to objects (f(dj)) are then proposed to recognize the subject's grasp intentions. With the proposed features, we perform intention recognition experiments, employing various classifiers, and the results are compared with different methods. Results: The statistical analysis reveals that the proposed features differ significantly across intentions, offering the possibility of employing these features to recognize grasp intentions. We demonstrated the intention recognition performance utilizing the TAGMM and the proposed features in within-subject and cross-subject experiments. The results indicate that the proposed method can recognize the intention with accuracy improvements of 44.26% (within-subject) and 30.67% (cross-subject) over the fixation-based method. The proposed method also consumes less time (34.87 ms) to recognize the intention than the fixation-bas

关键词： Grasp intention recognition gaze movement modeling human-robot interaction feature extraction

来源：评论

学校读者我要写书评

暂无评论

Predefined-Time Synchronization of Multiple Fuzzy Recurrent Neural Networks via a New Scaling Function

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第3期32卷 1527-1538页

作者： Liu, Peng Liu, Ting Sun, Junwei Zeng, Zhigang Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This article investigates the predefined-time synchronization of a group of fuzzy recurrent neural networks (FRNNs) under a leaderless communication topology. An effective control strategy is proposed based on a time-dependent exponential function as the scaling function. Sufficient criteria for guaranteeing the predefined-time synchronization of multiple FRNNs are derived under the digraph with strong connectivity and the digraph containing spanning trees, respectively. Unlike commonly used state-dependent sign function or time-dependent power function in existing works, the scaling function in this article is new and selected as the time-dependent exponential function. Moreover, the communication topology in this article is assumed to be leaderless, which is distinct from the master-slave or leader-following topologies previously investigated for predefined-time synchronization. Numerical examples are provided to illustrate the correctness of results.

关键词： Synchronization Topology Recurrent neural networks Manganese Behavioral sciences Delays Convergence Fuzzy recurrent neural networks (FRNNs) leaderless topology predefined-time synchronization

来源：评论

学校读者我要写书评

暂无评论

Fog images Generation About Unmanned Surface Vessels with Improved Generative Adversarial Network 42

Fog Images Generation About Unmanned Surface Vessels with Im...

引用

42nd Chinese control Conference, CCC 2023

作者： Tang, Yifeng Huang, Zhihui Tang, Chuancong Luo, Chuanshang Xu, Yifan Liu, Bin Zhang, Hai-Tao School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

This paper proposes a fog weather data augmentation method for the unmanned surface vessels (USVs) via improved Generative Adversarial Network(GAN) model. First, a generator scheme for GAN is proposed with the guided generation of the atmospheric scattering model in this paper. A Laplacian Pyramid Based Depth Residuals model is added to the generator which reduces the difficulty of generating fog images caused by the degradation of water surface image and improves the quality of generated images. Finally, fog images are generated from sunny weather images collected with HUST-12C by LPBDR-GAN model and experiments show that generated images are very close to real fog images. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Research on emergence mechanism of collective intelligence from the complexity perspective

引用

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2023年第1期22卷 28-+页

作者： Xiao, Renbin Feng, Zhenhui Wu, Bowen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

This paper explores the emergence mechanism of collective intelligence (CI) from the complexity perspective. It begins with a comparison of the main features based on the two basic stages of CI, i.e., CI 1.0 (swarm intelligence) and CI 2.0 (crowd intelligence). Considering the connection mechanism between the two stages is still unclear, we would regard higher organism group behaviours as the transition between lower organism group behaviours to crowd behaviours. Accordingly, the bionic prototypes of CI can be classified into three categories: lower organisms, higher organisms and humans. This paper first refined the emergence mechanisms of CI in lower organisms represented by labour division, i.e., stimulus-response mechanism and activation-inhibition mechanism. Subsequently, the higher organism emergence mechanism was revealed, which is the attraction-repulsion mechanism based on roles division and perception driven. Furthermore, the emergence mechanism of crowd intelligence at the perceptual level and cognitive level are presented respectively, by means of process evolutionary description based on the attraction-repulsion mechanism. Finally, the research gives a holistic illustration of the emergence mechanism of CI.

关键词： collective intelligence emergence complexity stimulus-response activation-inhibition attraction-repulsion perceptual level cognitive level

来源：评论

学校读者我要写书评

暂无评论

Exploring and Exploiting Multi-Modality Uncertainty for Tumor Segmentation on PET/CT

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第9期28卷 5435-5446页

作者： Kang, Susu Kang, Yixiong Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Innovat Inst Wuhan 430074 Peoples R China Qianjiang Peoples Hosp Qianjiang Peoples R China

Despite the success of deep learning methods in multi-modality segmentation tasks, they typically produce a deterministic output, neglecting the underlying uncertainty. The absence of uncertainty could lead to over-confident predictions with catastrophic consequences, particularly in safety-critical clinical applications. Recently, uncertainty estimation has attracted increasing attention, offering a measure of confidence associated with machine decisions. Nonetheless, existing uncertainty estimation approaches primarily focus on single-modality networks, leaving the uncertainty of multi-modality networks a largely under-explored domain. In this study, we present the first exploration of multi-modality uncertainties in the context of tumor segmentation on PET/CT. Concretely, we assessed four well-established uncertainty estimation approaches across various dimensions, including segmentation performance, uncertainty quality, comparison to single-modality uncertainties, and correlation to the contradictory information between modalities. Through qualitative and quantitative analyses, we gained valuable insights into what benefits multi-modality uncertainties derive, what information multi-modality uncertainties capture, and how multi-modality uncertainties correlate to information from single modalities. Drawing from these insights, we introduced a novel uncertainty-driven loss, which incentivized the network to effectively utilize the complementary information between modalities. The proposed approach outperformed the backbone network by 4.53 and 2.92 Dices in percentages on two PET/CT datasets while achieving lower uncertainties. This study not only advanced the comprehension of multi-modality uncertainties but also revealed the potential benefit of incorporating them into the segmentation network.

关键词： Tumor segmentation multi-modality uncertainty estimation PET/CT deep learning Tumor segmentation multi-modality uncertainty estimation PET/CT deep learning

来源：评论

学校读者我要写书评

暂无评论

Regulating the Polymerization of DNA Structures via Allosteric control of Monomers

引用

ACS NANO 2023年第2期17卷 1505-1510页

作者： Xie, Chun Chen, Zhekun Chen, Kuiting Hu, Yingxin Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Shijiazhuang Tiedao Univ Coll Informat Sci & Technol Shijiazhuang 050043 Hebei Peoples R China

Regulation of self-assembly is crucial in constructing structural biomaterials, such as tunable DNA nanostructures. Traditional tuning of self-assembled DNA nanostructures was mainly conducted by introducing external stimuli after the assembly process. Here, we explored the allosteric assembly of DNA structures via introducing external stimuli during the assembly process to produce structurally heterogeneous polymerization products. We demonstrated that ethidium bromide (EB), a DNA intercalator, could increase the left-handed out-of-plane chirality of curved DNA structures. Then, EB and double strands were introduced as competing stimuli to transform monomers into allosteric conformations, leading to three different polymerization products. The steric trap between different polymerization products promoted the polymerized structures to keep their geometric properties, like chirality, under varying intensity of external stimuli. Our strategy harnesses allosteric effects for assembly of DNA-based materials and is expected to expand the design space for advanced control in synthetic materials.

关键词： allosteric assembly DNA structures intercalations chirality steric hindrance

来源：评论

学校读者我要写书评

暂无评论

Distributed algorithm for mixed equilibrium problems with event-triggered strategy

引用

NEURAL COMPUTING & APPLICATIONS 2022年第19期34卷 16463-16472页

作者： Zhou, Hongtao Xia, Liang Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China

A new iterative method based on the event-triggered strategy for finding a solution to a mixed equilibrium problem (MEP) is introduced in this paper. The target of the MEP is to find a point in a closed convex set, guaranteeing that the sum of bifunctions about this point is non-negative. To decrease the cost of communication, the MEP is investigated with an event-triggered protocol. Furthermore, it is the first attempt to combine the MEP with an event-triggered strategy. Although there exist difficulties caused by the asymmetry of the network structure associated with directed graphs, nonlinearity and strong coupling of the MEP, the novel algorithm for directed graphs, two event-triggered conditions and the range of the solution associated with the MEP are obtained to handle these challenges. Under the directed time-varying graph, the designed algorithm can converge to a solution to the MEP and reach average consensus. Finally, a numerical example is presented to illustrate the effectiveness of the above algorithm.

关键词： Mixed equilibrium problem Event-triggered protocol Directed graph Average consensus

来源：评论

学校读者我要写书评

暂无评论

Distributed Event-Triggered Synchronization of Interconnected Linear Two-Time-Scale Systems With Switching Topology

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13714-13726页

作者： Lei, Yan Wang, Yan-Wu Liu, Xiao-Kang Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

This article investigates the synchronization problem of interconnected linear two-time-scale systems (TTSSs) with switching topology. By utilizing the Chang transformation, a distributed synchronization protocol is proposed with event-triggered communication. Static and dynamic event-triggered mechanisms are proposed successively, which both contain two separated event-triggering conditions corresponding to the slow and the fast subsystems. The existence of a strictly positive time period between any two successive transmissions is ensured regardless of the initial states. The main difficulty of this study lies in that the state jump and parametric uncertainty appear because of the system transformation. To overcome the difficulty, the system is first modeled as an uncertain hybrid system. Then, the control gain is properly designed by solving Riccati-like equations dependent on the rough bounds of the eigenvalues of communication graph Laplacians, and a piecewise quadratic Lyapunov function is proposed with which the jump caused by the switching topology is subtly evaluated. Sufficient conditions are thus established to achieve the event-triggered synchronization. Furthermore, the results are also extended to solve the synchronization problem of the interconnected impulsive linear TTSSs. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

关键词： Switches Topology Synchronization Analytical models control systems Symmetric matrices Lyapunov methods Event-triggered communication switching topology two-time-scale system (TTSS)

来源：评论

学校读者我要写书评

暂无评论

Neuroadaptive Impulsive control on Consensus of Uncertain Multiagent Systems Using Continuous and Sampled Information

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 5086-5098页

作者： Han, Yiyan Xiao, Qiang Zeng, Zhigang Huazhong Univ Sci & Technol Educ Minist China Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China

This article considers the consensus problem of uncertain multiagent systems, which is addressed by neuroadaptive impulsive control schemes. The proposed control schemes indicate that the communication among agents only occurs impulsively, while the dynamics uncertainty is addressed by adaptive schemes using neural networks. Based on such approaches, two specific control schemes are designed. One is that with impulsive feedback, the control scheme uses continuous-time information, which implies that the adaptive process is continuous over time. Another is that by adopting sampled information, the update of all systems, including the feedbacks on agents, the update of neural networks, and the estimation for uncertainty, can be executed only at impulsive instants. The latter case can reduce the energy cost for communication and control, but extra assistant systems are required. The estimation and consensus prove to be achieved with errors if some conditions are fulfilled. Numerical simulations, including a practical system example, are presented.

关键词： Multi-agent systems Neural networks Estimation Costs Uncertainty Protocols Directed graphs Consensus neuroadaptive impulsive control sampled information uncertain system

来源：评论

学校读者我要写书评

暂无评论

Sparse representation and modified density peak clustering-based state identification for multimode processes

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第2期34卷 025016-025016页

作者： Wang, Zhaojing Chen, Bin Zheng, Ying Wang, Yang Yan, Xiaoyun Wuhan Text Univ Hubei Prov Engn Res Ctr Intelligent Text & Fash Sch Comp Sci & Artificial Intelligence Wuhan 430200 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Industrial processes with high-dimensional data are generally operated with mixed normal/faulty states in different modes, making it difficult to automatically and accurately identify the faults. In this paper, a state identification framework is proposed for multimode processes. First, a key variable selection approach is presented based on sparse representation to eliminate redundant variables. Then, modified density peak clustering is proposed to identify different states, in which a distance measurement with a time factor is constructed to select all the possible cluster centers. Then, the sum of squared errors-based approach is developed to determine the optimal cluster centers automatically. Further, considering that the mode attributes may be mixed with the fault attributes, a two-step 'coarse-to-fine identification' strategy is designed to precisely identify the modes and the faults in each mode. Finally, three cases including a numerical simulation, Tennessee Eastman benchmark process and an actual semiconductor manufacturing process are presented to show the feasibility of the proposed method.

关键词： multimode process state identification modified density peak clustering sparse representation

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：