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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Chen, Yanping Ren, Lele Xia, Hong Wang, Zhongmin Gao, Cong Wang, Fengwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-techRoadSouth Shenzhen518057 China

ISBN: (纸本)9781665499781

At present, the research object of fault diagnosis focuses on single fault diagnosis. This method of diagnosis does not consider the relationship between mechanical equipment faults, and cannot fully identify the type of fault. In the actual industrial process, mechanical equipment is mostly complex system, and compound fault diagnosis is more in line with industrial needs. In order to solve the problem of compound fault diagnosis of mechanical equipment and improve the accuracy of fault identification, this paper establishes a compound fault diagnosis model for gearbox by means of multi-task learning, uses multi-gated networks to extract features from the task sharing layer, and finally classifies the fault through the task specific *** experimental results show that, the compound fault diagnosis method based on Multi-task Learning with Multi-gate Mixture-of-Experts (ML-MMoE) can accurately identify compound fault types under different working conditions. © 2022 IEEE

关键词： Failure analysis

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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Chronic Disease Detection Via Non-negative Latent Feature Analysis 18

Chronic Disease Detection Via Non-negative Latent Feature An...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Zhou, Leming Li, Qing Shang, Mingsheng Academy of Sciences College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chongqing China Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Chongqing China

ISBN: (纸本)9781665440486

Chronic diseases such as coronary heart disease (CHD) are of significant harm to human health. However, chronic disease detection commonly relies on many examinations to implement reliable diagnosis, resulting in a fatal delay in therapy. On the other hand, obtained examination data are often incomplete with numerous missing data, which further increases the difficulty in detection. To address this issue, this paper develops an inherently non-negative latent feature analysis-incorporating into a chronic disease detection (INC) model. The main idea are as follows: 1) adopting an inherently non-negative latent feature analysis model with a non-linear function to extract the non-negative latent features from incomplete examination data for accurate prediction of the unknown missing ones;2) detecting the chronic disease based on the high-dimensional and different types of inspection features via classifiers. Experiments on actual CHD datasets demonstrate that the proposed INC model outperforms state-of-the-art models in detection accuracy, which well fits the desire of preliminary screening on coronary heart disease (CHD). © 2021 IEEE.

关键词： Diagnosis

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Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation

arXiv

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arXiv 2024年

作者： Zhou, Wenlve Zhou, Zhiheng Wang, Tianlei Zeng, Delu School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou510641 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou510641 China The Department of Intelligent Manufacturing Wuyi University Jiangmen529020 China

Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source domain to perform well on a target domain without requiring additional annotations. In the context of domain adaptive semantic segmentation, which tackles UDA for dense prediction, the goal is to circumvent the need for costly pixel-level annotations. Typically, various prevailing methods baseline rely on constructing intermediate domains via cross-domain mixed sampling techniques to mitigate the performance decline caused by domain gaps. However, such approaches generate synthetic data that diverge from real-world distributions, potentially leading the model astray from the true target distribution. To address this challenge, we propose a novel auxiliary task called Guidance Training. This task facilitates the effective utilization of cross-domain mixed sampling techniques while mitigating distribution shifts from the real world. Specifically, Guidance Training guides the model to extract and reconstruct the target-domain feature distribution from mixed data, followed by decoding the reconstructed target-domain features to make pseudo-label predictions. Importantly, integrating Guidance Training incurs minimal training overhead and imposes no additional inference burden. We demonstrate the efficacy of our approach by integrating it with existing methods, consistently improving performance. The implementation will be available at https://***/Wenlve-Zhou/Guidance-Training. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Deep Generative Modeling in Constraint Discovery and Anomaly Detection

SSRN

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SSRN 2023年

作者： Hu, Wentao Jiang, Dawei Wu, Sai Chen, Ke Chen, Gang Zhejiang Police College Hangzhou China The Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Zhejiang University China

Domain experts in fraud detection and data Security often face challenges in automatically discovering constraints from relational databases, which is a critical process for identifying anomalous data. Its objective is to discover a set of inherent constraints underlying the database such that tuples violating them are considered anomalous. IceBerg, a novel system, is designed to simultaneously detect anomalous tuples and discover associated human-readable constraints, enabling both efficient anomaly detection and interpretable fraud. The backbone of IceBerg is a novel generative network, namely KD-VAE. By integrating Kernel Density estimation with Variational AutoEncoder, KD-VAE addresses the challenge of capturing complex data distributions, a critical aspect for effective anomaly detection. The KD-VAE, with its unique capability of combining kernel density estimation with variational autoencoders, provides superior performance in capturing the distributions of normal tuples, paving the way for effective anomaly detection. We can perform anomalous data detection by calculating the likelihood that the tuple fits the distributions of normal tuples and abnormality interpretation by comparing the detected anomalous tuples with their generated normal counterparts. We conducted empirical comparisons of IceBerg with several state-of-the-art outlier detection methods, evaluating performance metrics such as F1-score, across 13 real-world datasets. The results show that IceBerg outperforms its competitors in most cases. Its inherent capability to model complex, high-dimensional distributions makes it especially potent when handling datasets with high-dimensional features. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Relep: Region and Edge Synergetic Level Set Model Based on Edge-Enhanced Entropy and Variable Exponential P-Laplace Equation

SSRN

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SSRN 2023年

作者： Wu, Jiangfeng Zhou, Zhiheng Zhou, Wenlue Tao, Xiyuan Deng, Ming School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Image segmentation under conditions such as noise and intensity inhomogeneity poses a significant challenge for models. Hybrid level set approaches integrating area and edge information show promise in addressing these challenges. In this study, we propose a novel hybrid level set model, called region and edge synergetic level set model based on edge-enhanced entropy and variable exponential p-Laplace equation (RELEP), which is based on an edge-enhanced entropy and a variable exponential p-Laplace equation. Firstly, a new probability distribution is defined to enhance the edge extraction ability of image entropy in the presence of image intensity inhomogeneity and noise, termed as edge-enhanced entropy. The feature map obtained after edge-enhanced entropy processing is then used to co-fit the energy function with the original map to form the region term. Furthermore, the incorporation of edge-enhanced entropy into the p-Laplace equation leads to the formation of a variable exponential p-Laplace equation edge term. This integration facilitates the acquisition of precise and smooth boundaries, while simultaneously bolstering the robustness of the model. Finally, by incorporating the region term and edge term into the variational level set framework, the RELEP model is proposed. Experimental results on both synthetic and natural images demonstrate that the proposed RELEP model outperforms several representative and state-of-the-art models in terms of segmentation performance. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Reconstruction Algorithm of Heterogeneous Network data Flow in Smart Factory Based on SDN 14

Reconstruction Algorithm of Heterogeneous Network Data Flow ...

引用

14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Chen, Yanping Peng, Jiajia Xia, Hong Wang, Zhongmin Gao, Cong Wang, Fengwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-techRoadSouth Shenzhen518057 China

ISBN: (纸本)9781665499781

In smart factories, the wide application of a variety of intelligent manufacturing devices and advanced sensors leads to the increasing demand for data exchange between different devices. In order to realize the flexible forwarding of multi-source manufacturing data in heterogeneous networks and the optimal utilization of network resources, in this article, we combined smartphone manufacturing scenarios, referred to the data flow reconstruction algorithm in related documents, analyzed the high dynamics of the data flow and proposed a data flow reconstruction algorithm based on routing-protocol, according to the particularity of different data forwarding and processing procedures, redesigned and defined the data flow in the scene. Finally, the results of Mininet simulation experiments showed that our proposed algorithm reduces the average transmission delay. © 2022 IEEE

关键词： Heterogeneous networks

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Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

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Computer Modeling in Engineering & Sciences 2021年第10期129卷 31-45页

作者： Lejun Gong Xingxing Zhang Li Zhang Zhihong Gao Jiangsu Key Laboratory of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China

Novel coronavirus disease 2019(COVID-19)is an ongoing health *** studies are related to ***,its molecular mechanism remains *** rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational *** paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine *** method obtains seed genes based on prior *** genes are mined from biomedical *** candidate genes are scored by machine learning based on the similarities measured between the seed and candidate ***,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of *** results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

关键词： COVID-19 SARS-CoV-2 computational method bioinformatics genotype machine learning

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Automatic mapping of gully from satellite images using asymmetric non-local LinkNet: A case study in Northeast China

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International Soil and Water Conservation Research 2024年第2期12卷 365-378页

作者： Panpan Zhu Hao Xu Ligang Zhou Peixin Yu Liqiang Zhang Suhong Liu College of Computer Sciences and Technology Chongqing University of Posts and TelecommunicationsChongqing Engineering Research Center for Spatial Big Data Intelligent TechnologyChongqing400065China College of Environment and Ecology Chongqing UniversityChongqing400044China Chongqing Municipal Ecological and Environmental Monitoring Center Chongqing401120China Beijing Key Laboratory of Environmental Remote Sensing and Digital Cities Faculty of Geographical ScienceBeijing Normal UniversityBeijing100875China China National Environmental Monitoring Centre Beijing100012China

Gully erosion can lead to the destruction of farmland and the reduction in crop *** mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control *** narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images *** implement gully erosion mapping,we developed a small training samples-oriented lightweight deep leaning model,called asymmetric non-local LinkNet(ASNL-LinkNet).The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted *** results show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning *** quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to *** study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.

关键词： Gully erosion mapping High-resolution satellite images Deep learning Black soil

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Dlr: Adversarial Examples Detection and Label Recovery for Deep Neural Networks

SSRN

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SSRN 2023年

作者： Han, Keji Ge, Yao Wang, Ruchuan Li, Yun Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

Deep neural networks (DNNs) are demonstrated to be vulnerable to the adversarial example crafted by the adversary to fool the target model. Adversarial training and adversarial example detection are two popular methods to mitigate the adversarial issue. The adversarial training makes the target model recognize the adversarial example correctly for the image classification task, while it does not generalize well. The adversarial detection generalizes well but cannot help the target model recognize the adversarial example. We first define the label recovery task to address the adversarial issue for DNNs. Then we propose a new generative classifier and apply it to the adversarial example label recovery task. The proposed method is named as Detection and Label Recovery (DLR) defense method, which consists of Detector and Recover. Detector feeds the legitimate and adversarial examples to the target model and Recover, respectively. The Recover explores the ground-truth label of the detected adversarial example. DLR combines the merits of adversarial training and adversarial example detection. Experimental results demonstrate that our method outperforms some state-of-the-art adversarial training methods. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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