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A pricing game strategy with virtual prosumer guidance in community grid

IET RENEWABLE POWER GENERATION 2023年第11期17卷 2701-2710页

作者： Hu, Qihao Chi, Ming Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Huazhong Peoples R China

The peer-to-peer (P2P) energy trading model is considered to be one of the appropriate models for managing local energy trading in community grids. But free pricing mechanisms can lead to energy wastage and long response times. In order to facilitate the local consumption of renewable energy within the community, this paper proposes a pricing game strategy with virtual prosumer (VP) guidance on a P2P energy market model in community grid. To prevent market disruption caused by free pricing strategies in the P2P market and ensure the overall social benefit of the community, a VP controlled by the community microgrid manager is introduced to regulate the energy trading of the entire community. A novel and efficient prosumer trading model is proposed where the VP changes its pricing strategy to guide other prosumers to achieve Nash equilibrium and improve the internal consumption rate of new energy in the community. The proposed strategy's rationality and effectiveness are verified through case studies.

关键词： energy management systems market design for support and integration power distribution power markets

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Video stabilization based on image registration

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JOURNAL OF ELECTRONIC IMAGING 2022年第2期31卷 023007-023007页

作者： Zhu, Angfan Lu, Hao Cao, Zhiguo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

We address the problem of video stabilization via image registration. We propose a single convolutional neural network, which is simultaneously a dense feature descriptor and a keypoint detector, to find reliable keypoints and their features under each frames. To obtain accurate keypoint localization, the authors leverage the inherent feature hierarchy to restore spatial resolution and low-level details. Compared with traditional detectors, the obtained keypoints are more stable for the following image registration. Based on the correspondences collected from registration results, we propose a technique for video stabilization which is a spatial smooth sparse motion field with motion vectors only at the mesh vertexes. In practice, the authors assign each vertex an unique motion vector via their neighboring correspondences and a median filter. The video stabilization is performed on the vertex profiles, which are motion vectors collected at the same vertex location over time. The quantitative and qualitative evaluations show that the proposed registration-based video stabilization method can produce comparable results with the state-of-the-art methods and achieve more stable performance on challenging situations. (C) 2022 SPIE and IS&T

关键词： video stabilization feature description keypoint detection deep learning

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

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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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Reading is not believing: A multimodal adversarial attacker for Chinese-NLP model

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COMPUTERS & SECURITY 2023年 125卷

作者： Ge, Zhaocheng Hu, Hanping Zhao, Tengfei Shi, Dingmeng Huazhong Univ Sci & Technol 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Key Lab Image Proc & Intelligent Control 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

The research of adversarial examples has extended from image to text in the last few years. However, these attacks are typically limited to the English language and simple substitution strategies. To further expose the vulnerability of NLP models, we study the linguistic characteristics of Chinese, the quintessential ideogram with over 1.2 billion native speakers. Accordingly, a novel attack framework named ZHDeceiver is proposed to generate Chinese adversarial examples from the perspective of morphology, phonetics, semantics, and basic transformation. In particular, a CNN-based Siamese Network is integrated to ameliorate the quality of adversarial examples. To elaborate the validity of ZH-Deceiver, extensive experiments are conducted on two datasets. Compared with four benchmarks such as Genetic, PWWS, TextBugger, and SememePSO, our attack achieves impressive performance on effectiveness, efficiency, imperceptibility, and human evaluation by deceiving seven AI models including CNN and BERT. Furthermore, the transferability, as well as the robustness, is further analyzed and the former is successfully applied to attack three commercial APIs: Tencent, ALi, and Baidu. ZH-Deceiver acts as a wake-up call for multilingual processing models, and tangibly extends the application and methodology of adversarial textual attack. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Vulnerability Adversarial example Multimodal attack Natural language processing Deep neural networks

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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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