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Generative Zero-shot Learning Compound Fault Diagnosis of Bearings 2

Generative Zero-shot Learning Compound Fault Diagnosis of Be...

2nd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2021

作者： Xu, Juan Li, Kang Hefei University of Technology School of Computer and Information Key Laboratory of Knowledge Engineering with Big Data Hefei China

ISBN: (纸本)9781665427470

Diagnosis of compound faults remains a challenge during fault diagnosis of bearings, owing to the different fault parameters coupling, fault characteristics diversity, and the exponential increasement of the number of possible failure modes. Current compound faults diagnostic methods, which are usually based on supervised or semi-supervised learning, require sufficient labeled or unlabeled training data for each compound faults. In industrial scenarios, neither labeled nor unlabeled training data of compound faults are usually difficult to collect and sometimes even inaccessible, whereas single faults samples are easy to obtain. Based on these issues, we construct a novel generative zero-shot learning (ZSL) compound faults diagnosis model identifies unseen compound faults using only single faults samples as training set. This model comprises several modules, namely semantic vector definition, feature extractor, generative adversarial modules. Firstly, we devise a unified semantic vector definition method for expressing single and compound faults based on theoretical correlation of characteristics between single fault and compound faults vibration data. Secondly, a CNN-based feature extractor is designed for extraction the fault features from the time-frequency domain of vibration data. Thirdly, a generative adversarial module performs adversarial training of semantic vectors and fault features of single faults to learn the mapping relationship between the fault features and the fault semantic vectors. Once trained, the generator is able to generate compound fault features using the compound fault semantic vectors, rather than any compound fault samples. Finally, the K-nearest neighbor method is adopted to identify the unseen compound faults by measuring the distance between the extracted feature from the testing compound fault samples and the generated features. The effectiveness of the proposed method is verified on a self-built bearing test stand. The results show

关键词： Semantics

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ATPRINPM: A single-source domain generalization method for the remaining useful life prediction of unknown bearings 3

ATPRINPM: A single-source domain generalization method for t...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xu, Juan Ma, Bin Fan, Yuqi Ding, Xu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

The remaining useful life (RUL) of bearings is critical to the proper operation of mechanical equipment, maintenance of equipment costs and availability. The existing domain adaptation methods have had great success in RUL prediction. However, when the target bearing data are unavailable or unknown to be involved in model training, the domain adaptation approaches also incapable. To solve the problem, we propose a parallel reversible instance normalization method based on adaptive threshold stage division for remaining useful life prediction of unknown bearings. First, we design an adaptive threshold method to find degradation points to divide the healthy and degradation stages. Then according to time series, we merge the original vibration data and its instance normalized data to increase the data distribution diversity. Finally, we combine instance normalization and parallel reversible normalization of the source bearing data into unified RUL learning framework to solve the uncertainty of counterfactual data and improve RUL prediction performance. The results show that the method is superior to the state-of-the-art methods for RUL prediction of unknown bearings. © 2022 IEEE.

关键词： Adaptive Threshold Reversible Instance Normalization RUL Prediction Stage Division

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Clustering moving objects in spatial networks

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12th International Conference on database Systems for Advanced Applications

作者： Chen, Jidong Lai, Caifeng Meng, Xiaofeng Xu, Jianliang Hu, Haibo School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)9783540717027

Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering analysis is becoming one of the most important requirements. In this paper, we present the problem of clustering moving objects in spatial networks and propose a unified framework to address this problem. Due to the innate feature of continuously changing positions of moving objects, the clustering results dynamically change. By exploiting the unique features of road networks, our framework first introduces a notion of cluster block (CB) as the underlying clustering unit. We then divide the clustering process into the continuous maintenance of CBs and periodical construction of clusters with different criteria based on CBs. The algorithms for efficiently maintaining and organizing the CBs to construct clusters are proposed. Extensive experimental results show that our clustering framework achieves high efficiency for clustering moving objects in real road networks.

关键词： spatial-temporal databases moving objects clustering spatial networks

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Distributed join algorithms on multi-CPU clusters with GPUDirect RDMA 19

Distributed join algorithms on multi-CPU clusters with GPUDi...

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48th International Conference on Parallel Processing, ICPP 2019

作者： Guo, Chengxin Chen, Hong Zhang, Feng Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE School of Information Renmin University of China China

ISBN: (纸本)9781450362955

In data management systems, query processing on GPUs or distributed clusters have proven to be an effective method for high efficiency. However, the high PCIe data transfer overhead between CPUs and GPUs, and the communication cost between nodes in distributed systems are usually bottleneck for improving system performance. Recently, GPUDirect RDMA has been developed and has received a lot of attention. It contains the features of the RDMA and GPUDirect technologies, which provides new opportunities for optimizing query processing. In this paper, we revisit the join algorithm, one of the most important operators in query processing, with GPUDirect RDMA. Specifically, we explore the performance of the hash join and sort merge join with GPUDirect RDMA. We present a new design using GPUDirect RDMA to improve the data communication in distributed join algorithms on multi-GPU clusters. We propose a series of techniques, including multi-layer data partitioning, and adaptive data communication path selection for various transmission channels. Experiments show that the proposed distributed join algorithms using GPUDirect RDMA achieve up to 1.83x performance speedup compared to the state-of-the-art distributed join algorithms. To the best of our knowledge, this is the first work for distributed GPU join algorithms. We believe that the insights and implications in this study shall shed lights on future researches using GPUDirect RDMA. © 2019 ACM.

关键词： Graphics processing unit

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Research progress of CDT 8

Research progress of CDT

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8th International Conference on Bioinformatics and Biomedical Science, ICBBS 2019

作者： Yuting, Gao Shutong, Shen Sanhong, Deng School of Information Management Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu China

ISBN: (纸本)9781450372510

With the high mortality rate for the cases of malignant tumors, the discovery and early treatment of cancer is critical to improving the 5-year survival rate of cancer. The biggest challenge in control and prevention is how to detect tumors as early as possible, so that the most effective means can be used for treatment. With the help of the WOS database, researchers found that liquid biopsy, genetic material/protein expression and computer imaging play an important role in cancer detection. Through analysis of the history and current status of cancer detection technology development, this paper also aims to provide guidance for the clinical practice of current cancer screening and provide inspiration for future technological development. © 2019 Association for Computing Machinery.

关键词： Gene expression

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knowledge Graph for China's Genealogy1

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IEEE Transactions on knowledge and data engineering 2023年第1期35卷 634-646页

作者： Wu, Xindong Jiang, Tingting Zhu, Yi Bu, Chenyang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei230009 China Hefei University of Technology Research Institute of Big Knowledge Anhui Hefei230009 China Mininglamp Technology Mininglamp Academy of Sciences Beijing100102 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230009 China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230009 China

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model. © 1989-2012 IEEE.

关键词： History

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Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer:A multicenter study

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Chinese Journal of Cancer Research 2025年第1期37卷 28-47页

作者： Qin Wang Feng Zhao Haicheng Zhang Tongpeng Chu Qi Wang Xipeng Pan Yuqian Chen Heng Zhou Tiantian Zheng Ziyin Li Fan Lin Haizhu Xie Heng Ma Lan Liu Lina Zhang Qin Li Weiwei Wang Yi Dai Ruijun Tang Jigang Wang Ping Yang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases Yantai Yuhuangding HospitalYantai 264000China Department of Radiology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Jiangxi Cancer Hospitalthe Second Affiliated Hospital of Nanchang Medical CollegeNanchang 330006China Department of Radiology the First Affiliated Hospital of China Medical UniversityShenyang 400042China Department of Radiology Weifang Hospital of Traditional Chinese MedicineWeifang 262600China Department of Medical Imaging Affiliated Hospital of Jining Medical UniversityJining 272029China Department of Radiology the Peking University Shenzhen HospitalShenzhen 518036China Department of Pathology Guilin Traditional Chinese Medicine HospitalGuilin 541002China Department of Pathology the Affiliated Hospital of Qingdao UniversityQingdao 266555China Department of Pathology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China

Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer *** aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more ***:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to *** approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention ***:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing *** the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing *** the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,*** significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the ***,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis ***:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

关键词： Artificial intelligence breast cancer digital pathology whole slide images

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Virtualizing system and ordinary services in Windows-based OS-level virtual machines 11

Virtualizing system and ordinary services in Windows-based O...

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Proceedings of the 2011 ACM Symposium on Applied Computing

作者： Shan, Zhiyong Chiueh, Tzi-Cker Wang, Xin Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China Stony Brook University United States

ISBN: (纸本)9781450301138

OS-level virtualization incurs smaller start-up and run-time overhead than HAL-based virtualization and thus forms an important building block for developing fault-tolerant and intrusion-tolerant applications. A complete implementation of OS-level virtualization on the Windows platform requires virtualization of Windows services, such as system services like the Remote Procedure Call Server Service (RPCSS), because they are essentially extensions of the kernel. As Windows system services work very differently from their counterparts on UNIX-style OS, i.e., daemons, and many of their implementation details are proprietary, virtualizing Windows system services turned out to be the most challenging technical barrier for OS-level virtualization for the Windows platform. In this paper, we describe a general technique to virtualize Windows services, and demonstrate its effectiveness by applying it to successfully virtualize a set of important Windows system services and ordinary services on different versions of Windows OS, including RPCSS, DcomLaunch, IIS service group, Tlntsvr, MySQL, Apache2.2, CiSvc, ImapiService, etc. © 2011 ACM.

关键词： Virtual machine

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IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings 3

IDIDNG: A Domain Generalization Remaining Useful Life Predic...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xu, Juan Xu, Zhen Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings. © 2022 IEEE.

关键词： adaptive weights cross-swap generalization inter-domain intra-domain normalization RUL

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The Approximation Operators Within Formal Contexts

The Approximation Operators Within Formal Contexts

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International Conference on Multimedia and Ubiquitous engineering (MUE)

作者： Ming-Wen Shao Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang Jiangxi China

In this paper, based on concept lattices and dual concept lattices, we introduced a pair of rough set approximation operators within formal contexts. The proposed approximations operators don't require the equival... 详细信息

关键词： Lattices Set theory Finance data analysis Bismuth Information technology Laboratories knowledge engineering Artificial intelligence Uncertainty

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