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intelligent Fault Diagnosis With Noisy labels via Semisupervised Learning on Industrial Time Series

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第6期19卷 7724-7732页

作者： Cheng, Cheng Liu, Xiaoyu Zhou, Beitong Yuan, Ye Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Deep neural networks (DNNs) excel at industrial fault diagnosis. Their performance heavily relies on the quality of human-annotated labels. Due to perception limitations of annotators, industrial time series samples (such as vibration and voltage signals) are frequently mislabeled in several conditions, such as samples with frequency domain feature differences and samples on class borders. Hence, an annotated industrial dataset will inevitably contain noisy labels at a certain level, leading to overfitting and poor generalization of DNNs. In this work, we introduce an industrial noisy label semisupervised learning (INL-SSL) fault diagnosis approach, addressing the problem that a certain number of samples in an industrial dataset are mislabeled. The proposed INL-SSL architecture simultaneously trains two DNNs, which cross-train on each other to filter noisy label errors. In particular, a fitted Gaussian mixture model divides time series samples of each DNN flow into an unlabeled set with samples likely to be noisy and a labeled set with samples likely to be clean. Given the labeled and unlabeled data, we proposed a time series MixMatch semisupervised learning strategy to train the diagnostic model. Ablation study verifies the benefit of the proposed time series augmentation techniques for semisupervised training. Extensive experiments on a benchmark industrial dataset of rolling element bearings (REB) reveal that the INL-SSL outperforms state-of-the-art approaches. On another self-collected REB dataset, the proposed approach also exceeds other comparison methods under noise ratios from 20% to 90%, validating the model's generalizability.

关键词： Fault diagnosis noisy labels rotating machinery semisupervised learning (SSL) time series data

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A Spatio-Temporal Scale Based Affective Neural Network for Emotion Detection in Conversation

A Spatio-Temporal Scale Based Affective Neural Network for E...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiang Wang, Xiaoping Lei, Shanglin Zeng, Zhigang Wuhan430074 China Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Key Laboratory of Brain-inspired Intelligent Systems Hubei Province Wuhan430074 China

ISBN: (纸本)9798350303759

Emotion Detection in Conversation (EDC) has been implemented in numerous domains and is under increasing attention. The EDC task is highly challenging due to the need to incorporate both the context of the utterance and the identity of the participant. Some existing EDC approaches employ redundant network structures and inadequately model contextual utterances, resulting in limited performance gains. In this work, we devise a Spatio-Temporal scale based Affective neural Network (STANet) in a simplistic and efficacious manner. STANet consists of two main components, i.e., spatial scale network and temporal scale network, which are applied to extract spatial scale and temporal scale information in the conversation, respectively. Prior to spatio-temporal scale modeling, we encode the identity information of participants to examine its impact on the performance of emotion detection. We conduct substantial comparative experiments and ablation studies on three conversational emotion datasets, and the results demonstrate that our STANet realizes superior performance without taking advantage of external knowledge. © 2023 IEEE.

关键词： Emotion Recognition

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GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 77-89页

作者： Li, Jiang Wang, Xiaoping Lv, Guoqing Zeng, Zhigang Huazhong Univ Sci & Technol Educ Minist China Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches.

关键词： Emotion recognition in conversation multimodal fusion graph neural networks cross-modal feature complementation

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Quasi-Synchronization of Fractional Multiweighted Coupled Neural Networks via Aperiodic Intermittent control

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第3期54卷 1671-1684页

作者： Wei, Chen Wang, Xiaoping Hui, Meng Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan Peoples R China Changan Univ Sch Elect & Control Xian 710064 Peoples R China

This article investigates the quasi-synchronization for fractional multiweighted coupled neural networks (FMCNNs) with discontinuous activation functions and mismatched parameters. First, under the generalized Caputo fractional-order derivative operator, a novel piecewise fractional differential inequality is established to study the convergence of fractional systems, which significantly extends some related published results. Subsequently, by exploiting the new inequality and Lyapunov stability theory, some sufficient quasi-synchronization conditions of FMCNNs are presented by aperiodic intermittent control. Meanwhile, the exponential convergence rate and synchronization error's bound are given explicitly. Finally, the validity of theoretical analysis is confirmed by numerical examples and simulations.

关键词： Synchronization Biological neural networks Behavioral sciences Fractional calculus Convergence Standards Power system stability Aperiodic intermittent control discontinuous activation functions fractional multiweighted neural networks mismatched parameters quasi-synchronization

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Discussion on application of FLIC-Fluent coupled simulation technology on medium-scale and large-scale municipal solid waste incinerators 6

Discussion on application of FLIC-Fluent coupled simulation ...

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6th International Conference on intelligent Computing, Communication, and Devices, ICCD 2023

作者： Zhang, Hongze Guangxi Key Laboratory of Machine Vision and Intelligent Control Guangxi Wuzhou China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Guangxi Wuzhou China School of Electronics and Information Engineering Wuzhou University Guangxi Wuzhou China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

Today, the world economy is in the stage of rapid development, followed by the rising quantity, the decreasing moisture content, and the increasing heat value of Municipal Solid Waste (MSW). Since waste incineration has the advantages of excellent volume reduction effect, high resource utilization efficiency, and less secondary pollution to the environment, waste incineration disposal technology has received extensive attention worldwide. With the continuous increase of the urban population, the amount of sewage sludge is growing year by year. The process of the sludge incineration system is highly complicated, and the cost of construction and operation is high. The waste incinerator can process not only MSW but also sewage sludge. Therefore, the co-incineration of sewage sludge with MSW in the existing waste incinerator is a safe and effective way to dispose of sewage sludge. In this paper, the simulation results of Computational Fluid Dynamics (CFD) are analyzed for medium-scale and large-scale waste incinerators, characterized by high moisture content, high ash content, low heat value of sewage sludge, and incomplete combustion and high pollutant emissions in waste incinerators. With FLIC-Fluent coupled simulation method, the critical information such as temperature field, velocity field, concentration field of principal components, and the pollutant emissions can be predicted. Based on the CFD simulation results, the performance and structure of waste incinerators can be optimized, and the new products can be designed and developed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Waste incineration

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A dual-stream recurrence-attention network with global-local awareness for emotion recognition in textual dialog

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Li, Jiang Wang, Xiaoping Zeng, Zhigang Huazhong Univ Sci & Technol HUST Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol HUST Inst Artificial Intelligence Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Hubei Key Lab Brain inspired Intelligent Syst Wuhan 430074 Peoples R China

In real-world dialog systems, the ability to understand the user's emotions and interact anthropomorphically is of great significance. Emotion Recognition in Conversation (ERC) is one of the key ways to accomplish this goal and has attracted growing attention. How to model the context in a conversation is a central aspect and a major challenge of ERC tasks. Most existing approaches struggle to adequately incorporate both global and local contextual information, and their network structures are overly sophisticated. For this reason, we propose a simple and effective Dual-stream Recurrence-Attention Network (DualRAN), which is based on Recurrent Neural Network (RNN) and Multi-head ATtention network (MAT). DualRAN eschews the complex components of current methods and focuses on combining recurrence-based methods with attention-based ones. DualRAN is a dual-stream structure mainly consisting of local-and global-aware modules, modeling a conversation simultaneously from distinct perspectives. In addition, we develop two single-stream network variants for DualRAN, i.e., SingleRANv1 and SingleRANv2. According to the experimental findings, DualRAN boosts the weighted F1 scores by 1.43% and 0.64% on the IEMOCAP and MELD datasets, respectively, in comparison to the strongest baseline. On two other datasets (i.e., EmoryNLP and DailyDialog), our method also attains competitive results.

关键词： Dialog emotion recognition Recurrent neural network Multi-head attention network Dialog system Dual-stream network

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A Robotic Tactile Excavation System for Excavating Objects Buried in Granular Materials

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2025年

作者： Deng, Linan Wang, Yasen Yue, Zuogong Li, Zhi Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Robotic excavation of objects buried in granular materials (GM) is challenging due to the lack of visual information. In this work, we present a robotic tactile excavation system (RTES) for exploring and excavating unknown objects buried in GMs. We first design an underactuated tactile finger with multimodal tactile sensing, including sensing contact pressure and fingertip motion. A contact detection model is established to identify GMs and buried objects in real-time based on the multimodal sensor data. Then, a 3-D occupancy map of GMs is constructed to guide the object grasping. Finally, a tactile excavation (TE) policy is proposed to explore and excavate buried objects, which mainly includes two rounds of rough excavation and one round of fine excavation. Experiments on contact detection, shape detection, and TE are conducted to evaluate the effectiveness of RTES, where RTES can successfully excavate various unknown objects from GMs.

关键词： Excavation Buried object detection Sensors Robots Robot sensing systems Grippers Grasping Three-dimensional displays Sensor arrays Force Granular materials (GMs) robotic excavation tactile sensing underactuated gripper

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Neural Network-Based Fixed-Time Tracking and Containment control of Second-Order Heterogeneous Nonlinear Multiagent Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 11565-11579页

作者： Chen, Chongyang Han, Yiyan Zhu, Song Zeng, Zhigang Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent ControlMinst Edu Wuhan Peoples R China China Univ Min & Technol Sch Math Xuzhou 221116 Peoples R China

This study concentrates on the fixed-time tracking consensus and containment control of second-order heterogeneous nonlinear multiagent systems (MASs) with and without measurable velocity under directed topology. By defining a time-varying scaling function and approximating the unknown nonlinear dynamics with radial basis function neural networks (RBFNNs), a novel distributed protocol for solving the fixed-time tracking consensus and containment control problems of second-order heterogeneous nonlinear MASs with full states available is proposed based on a nonsingular sliding-mode control method constructed by designing a prescribed-time convergent sliding surface. For the scenario of immeasurable velocity, a fixed-time convergent states' observer is designed to reveal the velocity information when the unknown linearity is bounded. Subsequently, a distributed fixed-time consensus protocol based on observed velocity information is proposed for the extended results. Ultimately, the acquired results are verified by three simulation examples.

关键词： Convergence Nonlinear dynamical systems Sliding mode control Task analysis Observers Multi-agent systems intelligent control Directed topology fixed-time consensus multiagent systems (MASs) neural networks sliding-mode control

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Cascaded Generalized Extended State Observer-Based control for Servo Systems With Matched and Mismatched Disturbances 12

Cascaded Generalized Extended State Observer-Based Control f...

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IEEE 12th Data Driven control and Learning Systems Conference (DDCLS)

作者： Jiang, Fuxi Ye, Jie Cheng, Shanmei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9798350321050

This paper proposes a cascaded generalized extended state observer-based control (CGESOBC) implementation scheme for a class of nonlinear servo systems with nonintegral-chain form and multiple matched and mismatched disturbances. In this approach, the total disturbances in each channel are reconstructed by designing a GESO. A reference model is developed with the estimated disturbances and the reference input, together with a state tracking error model containing the multiple residual disturbances. Another GESO is then devised to estimate the primary estimation errors, based on which a state feedback control law incorporating a dynamic compensator is formulated for robust stabilization of the state tracking error system. Moreover, the Lyapunov stability theory is applied to prove the bounded stability of the closed-loop system. Finally, the efficacy of the proposed control method is verified by a numerical example.

关键词： Nonlinear servo system Mismatched disturbance Generalized extended state observer Robust stabilization

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Reconfigurable Nanobook Structure Driven by Polymerase-Triggered DNA Strand Displacement 1

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

作者： Chen, Kuiting Chen, Zhekun Xie, Chun 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

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Due to its high degree of customization, DNA origami provides a versatile platform with which to engineer nanoscale structures and devices. Reconfigurable nanodevices driven by DNA strand displacement accomplish the task of transition between different conformations, endowing DNA origami with application values. Herein, we propose a strategy to regulate the conformation of DNA origami using the polymerase-triggered DNA strand displacement (PTSD) reaction. We design a book-shaped DNA origami structure consisting of four pages connected into a cuboid shape. The PTSD reactions initiated by different primer strands selectively remove the connecting strand, transforming the nano book into a two-page or a four-page conformation. We utilize three primer strands to remove thirty-five connecting strands and construct three conformations of the identical DNA origami, illustrating that the PTSD reaction is an effective tool for the reconfiguration of DNA origami. The statistical results of TEM images prove the effectiveness of the proposed method. Our work on the development of PTSD-driven reconfigurable nanostructure will offer a new way to create intelligent materials for advanced nanotechnology applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： DNA

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