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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. This makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. Therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. The global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. The verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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SMART: towards pre-trained missing-aware model for patient health status prediction 24

SMART: towards pre-trained missing-aware model for patient h...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhihao Yu Xu Chu Yujie Jin Yasha Wang Junfeng Zhao School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University School of Computer Science Peking University and Center on Frontiers of Computing Studies Peking University Beijing China and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Peking University Information Technology Institute (Tianjin Binhai) National Research and Engineering Center of Software Engineering Peking University and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Peking University Information Technology Institute (Tianjin Binhai) School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Nanhu Laboratory Jiaxing China

ISBN: (纸本)9798331314385

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess difficult-to-interpolate details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via missing-aware temporal and variable attentions and learns to impute missing values through a novel self-supervised pre-training approach which reconstructs missing data representations in the latent space rather than in input space as usual. By adopting elaborated attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods. Our code is available at https://***/yzhHoward/SMART.

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PD-Refiner: An Underlying Surface Inheritance Refiner with Adaptive Edge-Aware Supervision for Point Cloud Denoising 24

PD-Refiner: An Underlying Surface Inheritance Refiner with A...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Chengwei Zhang, Xueyi Yue, Xianghu Lao, Mingrui Jiang, Tao Wang, Jiawei Zhang, Fubo Chen, Longyong National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China Department of Electrical and Computer and Engineering National University of Singapore Singapore Singapore National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity. © 2024 ACM.

关键词： edge-aware supervision

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Comparison of ResNet-50 and Vision Transformer Models for Trash Classification 3

Comparison of ResNet-50 and Vision Transformer Models for Tr...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Liu, Jiongli Sun, Jiayou Zhou, Xuan College of Materials science and Engineering Sun Yat-sen University 132 Outer Ring Road East Guangdong Province Guangzhou510006 China College of Mathematics and Informatics South China Agricultural University 483 Wushan Road Guangdong Province Guangzhou510642 China College of Computer and Software Engineering Huaiyin Institute of Technology 1 Knights East Road Jiangsu Province Huai'an City223003 China

ISBN: (纸本)9781510663473

Paper, glass, fruit, scrap metal equipment, and other materials have become domestic garbage as people's living standards have dramatically improved. Plastic trash, including bags, and disposable lunch boxes, has become a global environmental threat. Carrying out waste separation and encouraging waste reduction at the source is a meaningful way to realize the quantification, harmlessness, and resourcefulness of domestic waste. Pictures contain lots of information data and many methods for successfully extracting and examining that information data. Consequently, one of the primary research projects in the field of pictures is the picture classification problem. Traditional image classification techniques can no longer process and extract information from vast amounts of image data quickly. Throughout the experiments, the accuracy of ResNet-50 is lower than ViT, most likely due to its less extensive set of parameters. And the more the parameters of the model, the higher the precision of training. Additionally, the experiment examined the performance of the ResNet-50 model with and without pretraining, and the results showed that the performance of the model without pretraining was noticeably inferior to the model with pretraining. It is better to have a model which has numerous parameters. © 2023 SPIE.

关键词： Image classification

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Enhancing Structural Cracks Segmentation Through Generative Image Synthesis 5

Enhancing Structural Cracks Segmentation Through Generative ...

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5th International Conference on Frontiers technology of Information and computer, ICFTIC 2023

作者： Liao, Yunping Zhang, Tao Yang, Yong Li, Boyi Li, Hui Yan, Qi Southwest University College of Computer and Information Science & College of Software Chongqing China Ministry of Natural Resources Chongqing Institute of Geology and Mineral Resources Technology Innovation Center of Geohazards Automatic Monitoring Chongqing China Chongqing University School of Civil Engineering Chongqing China

ISBN: (纸本)9798350309034

Using deep neural networks to detect cracks in architectural structures presents significant challenges due to the irregular and diverse nature of cracks, along with variations in lighting conditions and backgrounds. Moreover, the absence of a sufficient number of crack sample images can significantly impact the accuracy of crack detection. In this study, we propose a novel method to enhance the semantic segmentation accuracy of structural crack images. This is achieved by generating images and their corresponding masks. Our approach cleverly combines synthetic crack data generated through unconditional GAN-based methods with real-world data, addressing the issue of limited data availability. Additionally, we employ transformer-based architecture, specifically the SegFormer segmentation model, to assess our methodology. Experimental results demonstrate that the SegFormer small model, trained iteratively on this integrated dataset, achieved an impressive mIoU of 87.13%, marking a significant improvement over the 85.59% obtained on the original dataset. Our work demonstrates the power of generative techniques in computer vision tasks by offering a viable solution in the face of challenging data constraints, especially in the realm of cracks detection. © 2023 IEEE.

关键词： computer vision crack detection generative adversarial network image processing segmentation

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End-to-End Rockfall Detection Based on DINO 3

End-to-End Rockfall Detection Based on DINO

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3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum, AIBDF 2023

作者： Liao, Yunping Li, Shichao Yang, Yong Li, Boy Li, Hui Yan, Qi Technology Innovation Center of geohazards automatic monitoring Ministry of Natural Resources Chongqing Institute of Geology and Mineral Resources Chongqing China Southwest University College of Computer and Information Science College of Software Chongqing China School of Civil Engineering Chongqing University Chongqing China

ISBN: (纸本)9798400716362

In this paper, we utilized the DINO object detection model to achieve end-to-end deep learning-based rockfall detection. Rockfalls cause significant economic losses and casualties every year, and timely detection and response are crucial to prevent accidents and reduce human casualties. By detecting a small number of rockfall features in the early stages of a potential rockfall, this study analyzed the severity of the disaster and aimed to protect people’s lives and health by providing critical time to avoid disasters. In this paper, we annotated 2,201 images from 28 confirmed rockfall videos, of which 66.2% were primarily small targets according to the COCO dataset definition. By using the Transformer-based DINO model, we achieved an AP of 43.5%, which outperformed other excellent object detection models, especially in its ability to detect small targets. © 2023 Copyright held by the owner/author(s).

关键词： Object detection

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Construction of Index System of International Economic Cooperation Innovation Model in Digital Era-based on TIF Theory and DIKWP Model 25

Construction of Index System of International Economic Coope...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Shanqi, Zhan Tang, Fuliang Li, Yongmei Qiurong, Mei Zining, Lu Xing-Sen, Li Biyun, Zhang School of Software and Microelectronics Peking University Beijing China Dikwp Research Group School of Computer and Technology Hainan University China College of Engineering Peking University China Center of Chinese and Foreign Think Tanks China Beijing Financial Holdings Group China Research Institute of Extenics and Innovation Methods Guangdong University of Technology China China

ISBN: (纸本)9798350330014

With the deepening of globalization, international economic cooperation has become a key factor in promoting economic growth and development. However, the rapid changes and technological advances brought about by the digital age are changing the rules and methods of international economic cooperation. In this digital age, data is no longer just a resource, it is more like a new economic currency, and the development of information technology has made it easier to move data across borders, providing unprecedented opportunities for all kinds of economic activities. However, international economic cooperation in the digital age also comes with a series of challenges. Data privacy and security issues are more prominent, the digital divide persists, and regulatory and legal issues become more complex. The issues of fairness, fairness, efficiency and dialectical openness of digital platforms have become major issues that attract the attention of all countries. Therefore, in these turbulent times, we need innovative methods and indicators to assess and guide international economic cooperation to ensure that it leads to sustainable, beneficial, and balanced growth that benefits everyone around the world. © 2023 IEEE.

关键词： Economics

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Notes on the Architecture, League Training and PFSP in AlphaStar 16

Notes on the Architecture, League Training and PFSP in Alpha...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Zhao, Jianyang Zhou, Ningning Hong, Long Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China School of Computer Nanjing University of Posts and Telecommunications Nanjing China Research Center for Logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaian China

ISBN: (纸本)9781665405539

AlphaStar, a multi-agent intelligence program, is a new member of Alpha's gamer software. Rated at Grandmaster level in the empirical evaluation shows that AlphaStar has been a great success in engineering, which is another milestone in AI history. After a brief introduction to AlphaStar, we discussed its computational architecture, league training, PFSP, and proposed an interesting topic direction on the basis of our suggestions, which was how to combine the capacities of diverse ANNs that successful completed learning. Additionally, we proposed the viewpoint that is big data - space, intelligence - man' world, parallel computing - time. © 2021 IEEE.

关键词： Multi agent systems

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Write-Optimized Persistent Hash Index for Non-Volatile Memory

Write-Optimized Persistent Hash Index for Non-Volatile Memor...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Xiao, Renzhi Feng, Dan Hu, Yuchong Zhang, Yucheng Cui, Lanlan Wang, Lin School of Software Engineering Jiangxi University of Science and Technology Nanchang China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute China School of Computer Science and Engineering XI'AN University of Technology Xi'an China

ISBN: (纸本)9783982674100

A hashing index provides rapid search performance by swiftly locating key-value items. Non-volatile memory (NVM) technologies have driven research into hashing indexes for NVM, combining hard disk persistence with DRAM-level performance. Nevertheless, current NVM-based hashing indexes must tackle data inconsistency challenges caused by NVM write reordering or partial writes, and mitigate rapid local wear due to frequent updates, considering NVM's limited endurance. The temporary allocation of buckets in NVM-based chained hashing to resolve hash collisions prolongs the critical path for writing, thus hampering write performance. This paper presents WOPHI, a write-optimized persistent hash index scheme for NVM. By utilizing log-free failure-atomic writes, WOPHI minimizes data consistency overhead and addresses hash conflicts with bucket pre-allocation. Experimental results underscore WOPHI's significant performance enhancements, with insertion latency slashed by up to 88.2% and deletion latency boosted by up to 82.6% compared to existing state-of-the-art schemes. Moreover, WOPHI substantially mitigates data consistency overhead, reducing cache line flushes by 59.3%, while maintaining robust write throughput for insert and delete operations. © 2025 EDAA.

关键词： Nonvolatile storage

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