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Digital Transformation Application of Precision Industrial Quotation System 6th

Digital Transformation Application of Precision Industrial Q...

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6th international conference on Smart Grid and Internet of Things, SGIoT 2022

作者： Kao, Wen-Hsing Zeng, Yi-Mei Nian, Zi-Ying Cheng, Rung-Shiang Department of Information Technology Overseas Chinese University Taichung Taiwan Department of Business Administration Overseas Chinese University Taichung Taiwan

ISBN: (纸本)9783031312748

The digital era is coming. In recent years, due to the continuous improvement of information technology, Artificial intelligence, big data, Cloud computing and IoT related technology has flourished. For traditional industries, demand is gradually changing from less diverse product to more diverse product. And most of them are customized orders, so the change of production mode has also become an opportunity for the digital transformation of enterprises. In addition, under the impact of the epidemic at the end of 2019, enterprises pay more attention to digital transformation. This study cooperates with a precision industrial company in Taichung. The company used to record all the data in the quotation process in paper. In addition to the difficulty of data backtracking, quotation data such as cost, delivery period, production process, outsourced manufacturers and prices are also difficult to compare with the actual order data. Also, departments often affect the quotation time due to the cumbersome communication process. In order to solve the above problems and improve the quotation process, this study is discussed in a faster, more accurate, more effective and profitable way. By building a digital quotation system, digitally optimize the difficulties encountered by quotations, and finally solve the problem to make the quotation system successfully digitally transformed. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： data mining

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Application Research of Cloud computing Technology in Computer data Processing

Application Research of Cloud Computing Technology in Comput...

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CAIBDA 2022;2nd international conference on Artificial intelligence, big data and Algorithms

作者： Shuang Han

In the context of the era of big data, it not only creates a lot of opportunities for the development of modern society but also poses more challenges. If the society wants to develop better, it must take good technical means to process computer data to accurately mine the contained value. The application of cloud computing technology can achieve this purpose. Based on this, this paper analyzes the correlation between cloud computing technology and computer data processing through an overview of cloud computing technology, and then analyzes the cloud computing process based, so as to provide support for better application of cloud computing technology.

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Explore Deep Feature Learning to Power Equipment Monitoring and Defect Detection 22

Explore Deep Feature Learning to Power Equipment Monitoring ...

引用

6th international conference on High Performance Compilation, computing and Communications, HP3C 2022

作者： Lu, Xiaoxiong Zhang, Jie Chen, Kaihua Wu, Mini Li, Qingxue Yu, Xiaomeng Hangzhou Power Supply Company Marketing Technology Center China

ISBN: (纸本)9781450396295

The research of power equipment defect detection based on image feature has become a hot issue nowadays. In order to solve the problems of low efficiency and accuracy in traditional power equipment defect detection methods, a defect detection method of power metering equipment based on image deep learning is proposed in this work. We train the deep feature learning network model and obtain the optimal solution of network weights in right of training. The association rules are designed and the defect detection mechanism is designed in combination with the collected meter reading dataset. Based on the designed deep network model, defects are identified with the preprocessed images. In the meantime, in order to reduce the power consumption and time delay of data transmission in the process of defect recognition, we introduce the idea of edge computing, so that part of the defect recognition tasks can realize end-to-end intelligence while taking images. Experimental results show that the proposed method can improve defect detection capability and guarantee the normal operation of power metering equipment largely. © 2022 ACM.

关键词： Edge computing

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Aligned Objective for Soft-Pseudo-Label Generation in Supervised Learning 41

Aligned Objective for Soft-Pseudo-Label Generation in Superv...

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41st international conference on Machine Learning, ICML 2024

作者： Xu, Ning Hu, Yihao Qiao, Congyu Geng, Xin School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China

Soft pseudo-labels, generated by the softmax predictions of the trained networks, offer a probabilistic rather than binary form, and have been shown to improve the performance of deep neural networks in supervised learning. Most previous methods adopt classification loss to train a classifier as the soft-pseudo-label generator and fail to fully exploit their potential due to the misalignment with the target of soft-pseudo-label generation, aimed at capturing the knowledge in the data rather than making definitive classifications. Nevertheless, manually designing an effective objective function for a soft-pseudo-label generator is challenging, primarily because datasets typically lack ground-truth soft labels, complicating the evaluation of the soft pseudo-label accuracy. To deal with this problem, we propose a novel framework that alternately trains the predictive model and the soft-pseudo-label generator guided by a meta-network-parameterized label enhancement objective. The parameters of the objective function are optimized based on the feedback from both the performance of the predictive model and the soft-pseudo-label generator in the learning task. Additionally, the framework offers versatility across different learning tasks by allowing direct modifications to the task loss. Experiments on the benchmark datasets validate the effectiveness of the proposed framework. Source code is available at https://***/palm-ml/SEAL. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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Simulation and Design of a 56Gbps Cross-backplane Transmission Channel 11

Simulation and Design of a 56Gbps Cross-backplane Transmissi...

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11th international conference on computing and Pattern Recognition, ICCPR 2022

作者： Yao, Kai Wu, Qiang Cui, Jinling Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781450397056

Over the past decade, new technologies and applications such as artificial intelligence, cloud services, and big data have led to an exponential increase in Internet-connected devices and data traffic. This puts forward higher requirements for bandwidth and stability of data transmission. In order to achieve the goal of device integration and miniaturization, the backplane structure is often used to realize the interconnect between board and card systems. The backplane is used as the basis for data exchange. However, the long-distance transmission across the backplane will cause serious losses and various signal integrity problems. In recent years, with the development of serial transmission, 56Gbps PAM4 modulation transmission has gradually shown transmission efficiency beyond 28Gbps NRZ. The change of modulation mode brings an inherent loss of 9.5dB, and PAM4 modulation has more stringent requirements on signal integrity. In this paper, ADS is used to model and simulate the cross-backplane long-distance transmission channel, and a set of high-speed transmission channel design scheme based on OIF CSI-56G-LR specification is established from the aspects of plate, laminates and holes. © 2022 ACM.

关键词： Electronic data interchange

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Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations

Tracking trajectories of multiple long-term conditions using...

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2022 IEEE international conference on big data, big data 2022

作者： Kremer, Ron Raza, Syed Mohib Eto, Fabiola Casement, John Atallah, Christian Finer, Sarah Lendrem, Dennis Barnes, Michael Reynolds, Nick J Missier, Paolo Newcastle University School of Computing Newcastle upon Tyne United Kingdom Queen Mary University of London Wolfson Institute of Population Health London United Kingdom Newcastle University Bioinformatics Support Unit Newcastle upon Tyne United Kingdom Newcastle University Faculty of Medical Sciences Newcastle upon Tyne United Kingdom Queen Mary University of London Centre for Translational Bioinformatics London United Kingdom

ISBN: (纸本)9781665480451

Momentum has been growing into research to better understand the dynamics of multiple long-term conditions - multimorbidity (MLTC-M), defined as the co-occurrence of two or more long-term or chronic conditions within an individual. Several research efforts make use of Electronic Health Records (EHR), which represent patients' medical histories. These range from discovering patterns of multimorbidity, namely by clustering diseases based on their co-occurrence in EHRs, to using EHRs to predict the next disease or other specific outcomes. One problem with the former approach is that it discards important temporal information on the co-occurrence, while the latter requires "big"data volumes that are not always available from routinely collected EHRs, limiting the robustness of the resulting *** this paper we take an intermediate approach, where initially we use about 143,000 EHRs from UK Biobank to perform time-independent clustering using topic modelling, and Latent Dirichlet Allocation specifically. We then propose a metric to measure how strongly a patient is "attracted"into any given cluster at any point through their medical history. By tracking how such gravitational pull changes over time, we may then be able to narrow the scope for potential interventions and preventative measures to specific clusters, without having to resort to full-fledged predictive *** this preliminary work we show exemplars of these dynamic associations, which suggest that further exploration may lead to actionable insights into patients' medical trajectories. © 2022 IEEE.

关键词： Statistics

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Privacy-Preserving Logistic Regression Model Training Scheme by Homomorphic Encryption 26th

Privacy-Preserving Logistic Regression Model Training Scheme...

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26th international conference on Information and Communications Security, ICICS 2024

作者： Miao, Weijie Wu, Wenyuan Chongqing Key Laboratory of Secure Computing for Biology Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9789819787975

In the field of big data, logistic regression for binary and multi-class classification is widely used. Nowadays, there is growing concern about data privacy protection issues. This paper focuses on scenarios involving two parties participating and data being horizontally distributed. Based on homomorphic encryption, a logistic regression model training scheme is designed. This scheme reduces the number of iterations in the training process by using the second-order approximation Newton’s method. It employs the conjugate gradient method to solve the Newton’s method, and introduces a small amount of interaction to reduce ciphertext domain division operations, greatly reducing the computational overhead of the ciphertext domain. Additionally, a new encoding method is used to reduce the number of ciphertext multiplications and communication overhead. Furthermore, the "One-vs-Rest" decomposition strategy is adopted, combined with SIMD(Single Instruction, Multiple data) technology, extending the binary model to multi-classification. Experimental results show that with the use of Privacy-preserving Logistic Regression scheme (PPLR), for most datasets, setting the number of iterations to within 3 rounds can achieve accuracy comparable to existing privacy protection schemes of 5 to 7 rounds. For sample datasets with 60 and 112 dimensions, existing similar schemes require 90 s and 165 s to complete 5 rounds of iteration, while this scheme only requires 8s and 27s. Moreover, the communication overhead is reduced to half of the original scheme, requiring only 30.8MB and 62.7MB to complete training. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Research on fatigue detection System based on Facial Feature Recognition in big data environment 2

Research on fatigue detection System based on Facial Feature...

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2nd international conference on Artificial intelligence, Automation, and High-Performance computing, AIAHPC 2022

作者： Cheng, Yunli Meng, Hainie Tan, Yanxian Software Engineering Institute of Guangzhou Guangdong510990 China Graduate University of Mongolia Ulaanbaatar Mongolia Guangzhou Nanyang Polytechnic College Guangdong510925 China

ISBN: (纸本)9781510657717

In recent years, driving fatigue for a long time has been a very dangerous behavior. People under the scenario of the need to stay awake for a long time, can grow over time and cumulative fatigue, drivers, in particular, this kind of distracting behavior is more dangerous, even threatened the life safety of the driver, the car have fatigue detection system has played a crucial role. In order to effectively remind drivers to avoid fatigue driving and reduce the number of car accidents, the system, based on pycharm environment, plans to use MTCNN face detection algorithm and ERT cascade regression algorithm to locate 68 key feature points of the face by calling the camera, and collect the features of previous faces[1]. Focus on monitoring the characteristic areas of eyes, nose and mouth, set a good threshold to judge the degree of fatigue state, using Open CV+Dlib these image processing library design logic algorithm to achieve the system program, through the face blink, yawn and nod times and relative to the threshold. This paper focuses on the design and implementation of fatigue detection system based on face facial feature recognition. The system uses Python+ Open CV to realize the simulation program and test, and uses the face detector of Dlib library to load the 68-point face facial feature recognition model. It can perform state analysis on behaviors such as yawning, eye closing, nodding and nodding, so as to judge whether the driver is tired or not, and use Flask data visualization and analysis[2]. © 2022 SPIE. All rights reserved.

关键词： Python

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Deep U-Net Architecture with Curriculum Learning for Left Atrial Segmentation 1

引用

1st Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, held in Conjunction with the 25th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2022

作者： Jiang, Lei Li, Yan Wang, Yifan Cui, Hengfei Xia, Yong Zhang, Yanning National Engineering Laboratory for Integrated Aero -Space -Ground -Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

ISBN: (数字)9783031317781

ISBN: (纸本)9783031317774

Segmentation of the late-stage gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is a critical step in the ablation therapy for atrial fibrillation (AF). In this work, we propose an end-to-end deep learning-based segmentation method for delineating 3D left atrial (LA) structures in multiple domains. The proposed method uses the 6 layers deep U-Net architecture as the segmentation backbone. Curriculum learning is integrated into the deep U-Net architecture, helping the network learn step by step from easy to difficult scene. We have tested normal and strong version of data augmentation methods, to verify the effect of reducing domain shifts. Other techniques like Fourier-based data augmentation and Swin Transformer Block have also been explored to further improve the segmentation performance. The experimental results demonstrate that the strong version of data augmentation method can reduce the domain shifts and achieve more accurate result, with mean Dice score of 0.881 on the validation set of LAScarQS 2022 challenge. The evaluation results demonstrate our method’s effectiveness on left atrial segmentation in multi-sequence cardiac magnetic resonance (CMR) data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Curricula

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Role of Artificial intelligence in the Smart City: A Bibliometric Review 1

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24th international conference on Human-Computer Interaction, HCII 2022

作者： Adi Bhaskara, Julio Nurmandi, Achmad Department of Government Affairs and Administration Jusuf Kalla School of Government Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9783031196829

ISBN: (纸本)9783031196812

This study aims to identify the main trends in the scientific literature regarding the role of Artificial intelligence in implementing six dimensions of Smart City: mobility, environment, people, living, governance, and economy. Scientific literature data was taken from the Scopus database, which was searched from 2016 to 2021 with limitations on authors or affiliations from the top 4 countries in literature publications, namely China, the United States of America, the United Kingdom, and India. The keywords used in this study are Smart City and Artificial intelligence. data analysis used simple statistical methods, and bibliometric analysis was performed using VOSviewer software. This analysis includes a number of publications, citation analysis, and visualizing co-occurrence patterns of keywords. Bibliometric analysis shows there are 194 articles in research publication. The results of data analysis show that AI technology has been adopted in some dimensions, such as intelligent transportation systems, big data management, cloud computing, and decision-making using machine learning. Research in the field of Smart City, especially related to Artificial intelligence, by authors with affiliations in these four countries is increasing rapidly along with the advancement of science and technology. Of the four countries, the Internet of Things is a significant concern because the development of the Smart City concept today is strongly influenced by the IoT and the existence of Artificial intelligence which is helpful for assisting the policymaker in managing Smart City that it can be sustainable. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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