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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： He, Shiming Xiao, Jinpan Chen, Bowen Lei, Ying Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Web services have been integrated with all walks of life in society. Abnormalities in the network and services seriously affect user experience and company revenue. The system log records various information of the system, and the information-rich log messages usually show different anomalies. It is particularly important to accurately and timely identify the anomalies of the log messages and classify the anomalies. In addition, most of the current existing work only detects anomalies and does not classify them, and a few existing works require anomaly labels to classify anomalies. Based on these potential challenges, we propose an unsupervised hypersphere-based log message anomaly detection and classification method, LogADCH. We use the hypersphere loss function to let the BERT model learn compact representations of log messages and perform anomaly detection. Finally, K-Means clustering is performed on the semantic feature vectors detected as anomalies to form clusters of different types of anomalies. Compared with other works, LogADCH further classifies the abnormal types after detecting the abnormal logs, helping the operation and maintenance personnel to quickly locate the fault. We compare our method with other methods on two public datasets, and the experimental results show that LogADCH has superior performance in anomaly detection as well as anomaly classification. © 2023 IEEE.

关键词： Classification (of information)

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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Lightweight Human Pose Estimation Using Loss Weighted by Target Heatmap 26th

Lightweight Human Pose Estimation Using Loss Weighted by Ta...

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Li, Shiqi Xiang, Xiang Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031376597

Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computation cost and parameters of the deconvolution head network in SimpleBaseline and introduce an attention mechanism that utilizes original, inter-level, and intra-level information to intensify the accuracy. Additionally, we propose a novel loss function called heatmap weighting loss, which generates weights for each pixel on the heatmap that makes the model more focused on keypoints. Experiments demonstrate our method achieves a balance between performance, resource volume, and inference speed. Specifically, our method can achieve 65.3 AP score on COCO test-dev, while the inference speed is 55 FPS and 18 FPS on the mobile GPU and CPU, respectively. © 2023, Springer Nature Switzerland AG.

关键词： Benchmarking

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Enhancing Resilience of Microgrid-Integrated Power Systems in Disaster Events Using Reinforcement Learning 1

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Chinese intelligent Automation Conference, CIAC 2023

作者： Zha, Zhongyi Wang, Bo Liu, Lei Fan, Huijin Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819961870

ISBN: (纸本)9789819961863

Enhancing the resilience of power systems has become increasingly important to mitigate the negative impact of risk attacks. Integrating microgrids, which are small-scale power grids that can operate independently or in conjunction with the main grid, has emerged as an effective energy source for emergency situations and can avoid power outages. Typhoons, which are inevitable and relatively easy to predict risk attacks, have become one of the contexts to examine power system resilience. In this paper, we propose a reinforcement learning algorithm that combines graph theory knowledge to make decisions for optimizing power system resilience with microgrids under the Typhoon event. The algorithm effectively utilizes graph theory knowledge and reduces the number of decisions required in the optimization process. Numerical experimental results show that compared to traditional methods, the reinforcement learning algorithm can more effectively respond to typhoon changes, achieve flexible economic control, and enhance the resilience of power systems with microgrids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Numerical methods

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Fast Multivariate Time Series Anomaly Detection Based on Matrix Completion

Fast Multivariate Time Series Anomaly Detection Based on Mat...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： He, Shiming Yi, Tongzhijian Yang, Bo Guo, Chenxi Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Increasingly complex systems contain large numbers of devices that generate great number of multivariate time series that are monitored and recorded. For anomaly detection of these complex time series, deep learning technology is often used to build a learning model for anomaly detection. The method based on deep learning usually takes a long time to train data in practice, and the online service system is deployed or changed very frequently, which makes the online service system need to frequently spend a lot of time updating the anomaly detection model. To overcome this challenge, this paper proposes a fast multivariate time series anomaly detection (FMAD) approach based on matrix completion. After sampling the primitive low-rank matrix, FMAD uses matrix completion to reconstruct the original low-rank matrix to detect anomalies in multivariate time series. We conducted related experimental analysis and discussion. The experimental results on three real data sets show that FMAD has improved the problem of too long model training time and improved the detection efficiency. © 2023 IEEE.

关键词： Anomaly detection

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One-step solvothermal preparation of Fe-doped Ni_(0.85)Se/NF:An efficient catalyst for the oxygen evolution reaction

ChemPhysMater

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ChemPhysMater 2025年第1期4卷 78-85页

作者： Longqi Zhu Runze Wang Chen Wang Shuhan Yang Haizhen Liu Bo Xing Honghui Cheng Kuikui Wang Institute of Materials for Energy and Environment Laboratory of New Fiber Materials and Modern TextileGrowing Basis for State Key LaboratoryCollege of Materials Science and EngineeringQingdao UniversityQingdao 266071China MOE Key Laboratory of New Processing Technology for Non-ferrous Metals and Materials Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured MaterialsGuangxi UniversityNanning 530004China National Engineering Laboratory of Circular Economy Sichuan University of Science and EngineeringZigong 643000China School of Mechanical Engineering Yangzhou UniversityYangzhou 225127China Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology Changzhou UniversityChangzhou 213164China

Metal-cation doping is a fundamental strategy for enhancing catalyst ***-doped Ni_(0.85)Se/NF(Fe-Ni_(0.85)Se/NF)nanoparticles were prepared at 80°C via Fe^(2+)etching *** addition of Fe altered the coordination environment of the Ni species along with the catalyst's morphology,creating additional active ***,the synergistic interaction between the bimetallic components augmented the built-in activity and accelerated reaction *** Fe-Ni_(0.85)Se/NF electrocatalysts demonstrated remarkable catalytic activity for the oxygen evolution reaction(OER),with an acceptable overpotential of 276 mV and a Tafel slope of 58.1 mV dec^(−1)at 100 mA cm^(−2).Moreover,they demonstrated exceptional *** situ Raman and X-ray photoelectron spectroscopy(XPS)analyses showed that the excellent OER performance stemmed from the reconstruction-induced hydroxyl *** study offers a novel approach for streamlining the synthesis procedures and reducing the experimental costs for developing high-efficiency electrocatalysts.

关键词： Doping Selenylation Hydrothermal reaction Oxygen evolution reaction Electrocatalysts

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CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule Evaluation

CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chen, Gong Liu, Jun Li, Chen-Qian Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350337020

Lung cancer is the most common malignant tumor worldwide, with high mortality rates. Pulmonary nodule is a common manifestation of lung cancer. Accurate segmentation and detection of pulmonary nodules from CT scans are essential for proper assessment of patient prognosis. However, this task remains challenging due to various factors, such as class imbalance and the need for detailed characterization of lung nodule segmentation. To address these issues, we propose a novel network, CPR-Net (Convolutional Pyramid Residuals-Net), that combines segmentation and benign-malignant classification for CT images of lung nodules. Our approach utilizes a PBMM module to expand the perception field and enhance the representation capability of the model, allowing it to learn more detailed information. We evaluate the classification performance using accuracy, precision, recall, F1 score, AUC, and the segmentation performance using the Dice coefficient. Experiments on both publicly available datasets and self-constructed datasets demonstrate that our proposed method in this paper outperforms other methods in terms of classification and segmentation performance under limited labeled data conditions. Moreover, Our results suggest that this model has great potential for improving lung nodule diagnosis in radiology for heterogeneous intranodal images. © 2023 IEEE.

关键词： class imbalance multi-task learning pulmonary nodule

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Predicting Protein-Ligand Binding Affinity with Multi-Scale Structural Features

Predicting Protein-Ligand Binding Affinity with Multi-Scale ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Han Zhao, Jingtong Wang, Shengkun He, Zhiquan Ouyang, Xike Gao, Ting Northeast Normal University School of Information Science and Technology Changchun China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9798350337488

Predicting protein-ligand binding affinity is important in areas such as drug discovery, gene regulation and signal transduction. The DTA(Drug-Target Affinity) method based on protein structure can not only effectively compensates for the lack of binding information, but also more in line with real biological processes. Although the structure-based DTA methods have achieved good performance, the existing methods still have the problem of only considering single-scale structural features and ignoring multi-scale structural features. In order to solve this problem, we propose the MSSDTA (Multi-Scale Structural Representation Drug-Target Affinity Prediction), which extracts multi-scale protein features by integrating the surface node features and structural node features of proteins. At the same time, the drug representation network is used to fuse the 2D molecular structure characteristics and chemical characteristics of the drug to effectively distinguish the drug molecules with similar planar structures. Finally, the affinity prediction network is used to generate protein-ligand binding affinity scores. We verify the performance of this model on the PDBbind v.2019 dataset. The experimental results show that the proposed method achieves excellent performance. © 2023 IEEE.

关键词： Affinity Prediction Network Drug Representation Network Multi-Scale Protein Features Protein-Ligand Binding Affinity

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A Low Complexity Beam Selection Method for MmWave Massive MIMO Systems

A Low Complexity Beam Selection Method for MmWave Massive MI...

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2023 International Conference on Ubiquitous Communication, Ucom 2023

作者： Zhu, Chunhua Ji, Qinwen Guo, Xinying Henan University of Technology The Key Laboratory of Grain Information Processing and Control The Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou China Henan University of Technology Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Zhengzhou China

ISBN: (纸本)9798350340433

The levels of inter-user interference (IUI) usually vary due to the different geographical locations of users, but, most existing beam selection methods apply the same beam selection criteria for all users, which is detrimental to maintain an attractive trade-off between complexity and system performance. Considering the discrepancy of IUI comprehensively, a low complexity beam selection method for millimeter-wave massive multiple-input multiple-output (MIMO) systems is proposed. Firstly, a candidate beam set (CBS) is constructed in light of the sparsity and power distribution of the beamspace channel, thereby all users are classified into non-interfering users (NIUs), low-interference users (LIUs) and high-interference users (HIUs). For NIUs, the beams with large power are selected from CBS under the magnitude maximization (MM) criterion;for LIUs, a novel MM criterion with a tabu list is designed, which can effectively tackle the drawback of assigning shared beams in traditional MM;for HIUs, the incremental algorithm based on the sum-rate maximization criterion is developed to find the optimal beams from CBS. Simulation results validate that the proposed method can achieve almost the same sum-rate performance as the existing methods with the lower computational complexity. © 2023 IEEE.

关键词： Millimeter waves

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

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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

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