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Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Pengju Tang Yanxia Gao Yujun Yang Meixiu Tang College of Computer and Artificial Intelligence (Software College) Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China Huaihua University Library Huaihua University Huaihua China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

University computer rooms generally prefer batch purchasing strategy to simplify the management process such as system deployment and software update. However, with the growth of the life of the equipment, the failure of electronic products is inevitable, especially once the motherboard problem occurs, it is often difficult to find a replacement that exactly matches the original motherboard. Even if the replacement is found, its price is far beyond the market average level. After several years of operation, even the motherboard purchased in the same batch may also lead to the coexistence of a variety of models due to various reasons. In this situation, it becomes impractical to carry out unified network cloning. To solve this problem, we propose an innovative solution: deploying a local backup system combined with a server-controlled startup mode. The core of this solution is that through the local backup system, we can effectively deal with the challenge of different motherboard models, and ensure that each machine can have its own system snapshot. At the same time, the server-controlled boot mode further enhances the flexibility and efficiency of management, so that even in the hardware diversity of the environment, it can also achieve batch system recovery. The introduction of local backup system not only solves the problem of differentiated computer room management, but also significantly improves the convenience and stability of computer room operation and maintenance.

关键词： Biological system modeling Ecosystems Cloning System recovery Stability analysis Software Hardware Maintenance Information technology Consumer electronics

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Research and application of Transformer based anomaly detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Transformer, as one of the most advanced neural network models in Natural Language processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medical Information

STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medi...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Wentong Xu Chao Fu Jing Yu Caifeng Si Zuoyong Li Chaochao Li Haoyi Fan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Ultrasound First Affiliated Hospital of Zhengzhou University Zhengzhou Henan China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Anomaly detection has been widely explored by training an out-of-distribution detector with only normal data. Medical image analysis, as a common tool, faces the challenge of how to further enhance the detection performance for thyroid cancer, especially with the application of various deep learning methods in the thyroid-related field. In this paper, we validate the effectiveness of sonographic feature descriptions (textual information) for thyroid cancer diagnosis. Building on this, we propose a simple multimodal self-supervised framework that leverages both textual and image modalities for thyroid cancer anomaly detection. Results on a private dataset demonstrate the effectiveness and superiority of our proposed method compared to advanced methods.

关键词： Deep learning Training Image analysis Thyroid cancer Signal processing Feature extraction Informatics Anomaly detection Medical diagnostic imaging Thyroid

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Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation

arXiv

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arXiv 2023年

作者： Qi, Zipeng Chen, Hao Liu, Chenyang Shi, Zhenwei Zou, Zhengxia The Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views due to the lack of considering 3D information within the scene. In this paper, we propose "Implicit Ray-Transformer (IRT)" based on Implicit Neural Representation (INR), for RS scene semantic segmentation with sparse labels (such as 4-6 labels per 100 images). We explore a new way of introducing multi-view 3D structure priors to the task for accurate and view-consistent semantic segmentation. The proposed method includes a two-stage learning process. In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images. In the second stage, we design a Ray Transformer to leverage the relations between the neural field 3D features and 2D texture features for learning better semantic representations. Different from previous methods that only consider 3D prior or 2D features, we incorporate additional 2D texture information and 3D prior by broadcasting CNN features to different point features along the sampled ray. To verify the effectiveness of the proposed method, we construct a challenging dataset containing six synthetic sub-datasets collected from the Carla platform and three real sub-datasets from Google Maps. Experiments show that the proposed method outperforms the CNN-based methods and the state-of-the-art INR-based segmentation methods in quantitative and qualitative metrics. Ablation study shows that under limited label conditions, the combination of the 3D structure prior and 2D texture can significantly improve the performance and effectively complete missing semantic information in novel views. Experiments also demonstrate the proposed method could yield geometry-consistent segmentation results against illumination changes and viewpoint changes. Our dat

关键词： Remote sensing

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Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement

Watch the Speakers: A Hybrid Continuous Attribution Network ...

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International Conference on Tools for Artificial intelligence (ICTAI)

作者： Shanglin Lei Xiaoping Wang Guanting Dong Jiang Li Yingjian Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Institute of Artificial Intelligence Huazhong University of Science and Technology (HUST) Wuhan China

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss $\mathcal{L}_{EC}$ is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug-and-play nature of the EAE module in our method.

关键词：

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Optimizing Binary Sequence Excitation Signals for Rapid Impedance Spectrum Measurement in Proton Exchange Membrane Fuel Cells

Optimizing Binary Sequence Excitation Signals for Rapid Impe...

引用

第43届中国控制会议

作者： Jingzhi Lei Chuang Sheng Jingjing Wang Liansong Yu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology State Grid Electric Power Research Institute Wuhan Nanrui Limited Liability Company Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

With the increasing demand for efficient and reliable renewable energy sources,Proton Exchange Membrane Fuel Cells(PEMFCs) have emerged as a promising *** experiments demonstrate that Electrochemical Impedance Spectroscopy(EIS) effectively differentiates various states of *** broadband signals as testing excitations significantly reduces EIS measurement time,facilitating real-time monitoring of PEMFCs' health under dynamic *** signal design requires maintaining adequate amplitude across frequency components for noise resistance while minimizing signal peak value to reduce operational *** paper introduces an EIS simulation environment,leveraging PEMFCs' impedance and electrochemical noise *** focuses on optimizing the excitation signal's amplitude-frequency distribution using discrete interval binary *** results indicate that our method achieves minimal perturbation amplitude while preserving measurement accuracy in amplitude-frequency signal design.

关键词： Proton exchange membrane fuel cell Electrochemical impedance spectroscopy Electrochemical noise Excitation signal optimization

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Wind Speed Prediction Based on Combined Deep Learning Model Woa-Vmd-Gru-Tcn

SSRN

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SSRN 2024年

作者： Zeng, Chuxi Xia, Zhipnig Wang, Zhuo Zhou, Yihuan Zeng, Linghong Yin, Zishan Li, Xi Huang, Haizhou Qi, Xiaolan Deng, Zhonghua School of Artificial Intelligence and Automation Key Laboratory of Image Processing Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China Huazhong University of Science and Technology China

Accurate wind speed prediction is essential for the integration of wind turbines into microgrids. This paper proposes a combined prediction model, firstly, the key parameters of the variational modal decomposition (VMD) is optimized by whale optimization algorithm (WOA), and the original wind speed sequence is decomposed by using the WOA-VMD to obtain the intrinsic mode functions (IMFs) with different frequencies;secondly, the Pearson correlation coefficient is performed between IMFs and the original wind speed sequence, classifying the IMFs into two groups according to their correlation coefficients, the IMFs with strong correlation are predicted using the temporal convolutional network (TCN), and the IMFs with weak correlation are predicted using the gate recurrent unit (GRU);finally, the prediction results of each component are superimposed to obtain the final prediction results. The combined model is applied to real data from six wind farms in China with different climatic conditions and geographic locations, and different comparison models are established for validation. The experimental results show that the combined WOA-VMD-GRU-TCN prediction model can significantly improve the prediction accuracy, reduce the wind speed sequence error, and have better robustness than other methods. © 2024, The Authors. All rights reserved.

关键词： Intrinsic mode functions

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Toward Understanding How the Saturation Effect Impacts the Spread of Computer Viruses

Toward Understanding How the Saturation Effect Impacts the S...

引用

International Conference on Electronic Engineering and Informatics (EEI)

作者： Xianxiu Zhang Yuming Feng Yi Yang Baojie Zhang Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

It is well known that people who know the presence of network viruses will defend against them, and the more computer viruses are detected and the more measures are taken. However, most previous computer virus propagation models have completely ignored the saturation effect. This paper investigates how the saturation effect impacts the transmission of electronic virus. For this purpose, a new transmission model of network virus is proposed, in which the virus is not very prevalent. Under certain conditions, there is a global asymptotical stable equilibrium point with no virus in this model. Finally, we give several numerical examples to exhibit that sufficient saturation effect is greatly conducive to containing network viruses.

关键词： Asymptotic stability Computer viruses Computational modeling Stability analysis Numerical models Informatics Viruses (medical) Numerical stability

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InferEM: Inferring the Speaker’s Intention for Empathetic Dialogue Generation

arXiv

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arXiv 2022年

作者： Lv, Guoqing Li, Jiang Wang, Xiaoping Zeng, Zhigang The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China The Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker’s intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human’s psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multitask learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression. Copyright © 2022, The Authors. All rights reserved.

关键词： Encoding (symbols)

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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