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The image-Based Automatic Detection Method for Cutter Ring Edge Wear of Shield Machine 16th

The Image-Based Automatic Detection Method for Cutter Ring E...

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16th CCF conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Bo, Wen Li, Fengyuan Cai, Xiaofeng Zhang, Changyou Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Beijing Peoples R China State Key Lab Shield Machine & Boring Technol Zhengzhou 450001 Peoples R China

ISBN: (纸本)9789811945465;9789811945458

In order to satisfy the intelligent requirements of industrial systems and assist in automatic recognition of cutter wear, this paper proposes an image-based automatic detectionmethod for cutter ring edge wear of shieldmachine. The paper mainly studies: (1) Preprocess the original cutter images, the pixel image is generated by graying and thresholding methods, using the gray characteristics to suppress background, it has only two gray values;(2) Based on DBSCAN clustering algorithm, the optimization of cutter ring edge clusters is realized, and the edge pixel clusters are retained;(3) An ring edge extraction method based on structural constraints is proposed, the internal pixels are removed by orthogonal bidirectional projection, we obtained the preliminary image edge extraction results;(4) The circular edge of cutter-image is obtained by remaining pixels polynomial fitting based on polar coordinates. Finally, through the reference the actual size of cutter, the actual radius error is less than 3%. The experimental results show that this method can automatically and accurately detect the actual cutter wear of shield machine, and it provides an effective solution for the intelligent detection of cutter wear.

关键词： image processing Shield machine Cutter ring edge Wear detection Automation

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FedAvg with Fine Tuning: Local Updates Lead to Representation Learning 36

FedAvg with Fine Tuning: Local Updates Lead to Representatio...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Collins, Liam Hassani, Hamed Mokhtari, Aryan Shakkottai, Sanjay Univ Texas Austin ECE Dept Austin TX 78712 USA Univ Penn ESE Dept Philadelphia PA 19104 USA

ISBN: (纸本)9781713871088

The Federated Averaging (FedAvg) algorithm, which consists of alternating between a few local stochastic gradient updates at client nodes, followed by a model averaging update at the server, is perhaps the most commonly used method in Federated Learning. Notwithstanding its simplicity, several empirical studies have illustrated that the model output by FedAvg leads to a model that generalizes well to new unseen tasks after a few fine-tuning steps. This surprising performance of such a simple method, however, is not fully understood from a theoretical point of view. In this paper, we formally investigate this phenomenon in the multi-task linear regression setting. We show that the reason behind the generalizability of the FedAvg output is FedAvg's power in learning the common data representation among the clients' tasks, by leveraging the diversity among client data distributions via multiple local updates between communication rounds. We formally establish the iteration complexity required by the clients for proving such result in the setting where the underlying shared representation is a linear map. To the best of our knowledge, this is the first result showing that FedAvg learns an expressive representation in any setting. Moreover, we show that multiple local updates between communication rounds are necessary for representation learning, as distributed gradient methods that make only one local update between rounds provably cannot recover the ground-truth representation in the linear setting, and empirically yield neural network representations that generalize drastically worse to new clients than those learned by FedAvg trained on heterogeneous image classification datasets.

关键词： Gradient methods

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Memetic Algorithm Small Survey For 2019 Published Papers

Memetic Algorithm Small Survey For 2019 Published Papers

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Harco Leslie Hendric Spits Warnars Laurens Spits Warnars Wiranto Herry Utomo Antoine Doucet Arief Ramadhan Teddy Siswanto Department of Computer Science Computer Science Bina Nusantara University Jakarta Indonesia Department of Information Systems School of Information Systems Bina Nusantara University Jakarta Indonesia Master of Science and Technology President University West Java Indonesia Laboratoire L3i Université de La Rochelle La Rochelle France School of Computing Telkom University Bandung Indonesia Department of Information Systems Trisakti University Jakarta Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

The Memetic Algorithm (MA), introduced by Pablo Moscato in 1989, integrates Evolutionary Algorithms with local search methods, enhancing its effectiveness in solving complex optimization problems. This paper provides a comprehensive survey of MA research published in 2019, reviewing 75 selected papers from an initial pool of 112 identified through Google Scholar. The selected papers were categorized into five types: optimization problems (40 papers), image processing (10 papers), parallel processing (5 papers), gene/DNA datasets (4 papers), and other applications (16 papers). The survey highlights MA’s versatility and effectiveness across various domains, particularly its potential for solving complex optimization problems. Key findings include the adaptability of MA for diverse applications, its ongoing relevance in addressing challenging issues, and promising opportunities for combining MA with other algorithms to enhance performance. The paper also emphasizes the significance of MA in fields such as image processing, where it improves pattern recognition and image enhancement, and in bioinformatics, where it optimizes gene selection and genetic algorithms. Despite the extensive study of MA, there remains a significant research gap in non-English literature, particularly in Bahasa, limiting accessibility for Indonesian researchers. This survey aims to bridge this gap by providing valuable insights and encouraging further exploration and application of MA to solve increasingly complex problems. It offers a comprehensive overview that underscores the importance of MA and its potential for future research and innovation.

关键词： Surveys Memetics Technological innovation Search methods Evolutionary computation parallel processing Robustness Internet Ensemble learning Optimization

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Proceedings of 2022 15th International conference Management of Large-Scale System Development, MLSD 2022

Proceedings of 2022 15th International Conference Management...

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15th International conference Management of Large-Scale System Development, MLSD 2022

ISBN: (纸本)9781665497015

The proceedings contain 151 papers. The topics discussed include: critical information infrastructure facility status monitoring: risk potential of the process;categorization and criticality assessment of facilities of critical infrastructure;the Internet shutdown during the protest: a model of changing the network structure with an adjustable level of continuity of connections;sanctions and modeling of the financial stability of companies;the concept of joint navigation and communication for a heterogeneous group of autonomous uncrewed vehicles located in different environments;trajectory planning of crane trolley for smooth payload transportation;digital methods for control adaptive production;an approach to organizing parallel distributed processing of protected information queries in multi-server environment;analysis of vaccine dose effectiveness in different age groups of the population: a conceptual mathematical model of lymphopoiesis;theoretical and methodological aspects of assessing the level of information development in the world and the EAEU countries;and approach to the formation of requirements for the design of the aircraft with increased load capacity and flight range.

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A Hybrid Approach of Semantic Weight Based Re-Propagation For Convolutional Neural Networks in Content Based Medical image Retrieval

A Hybrid Approach of Semantic Weight Based Re-Propagation Fo...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Gourav Sood Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Content-based image retrieval (CBIR) is a homogeneous search technology based on visual features, and information has been leaked to potential users of pharmaceutical testing, education, and research. However, the CBIR collects images related to acquisition properties, as a result of the inability to predict the similarity of images using the medical part, and the difference between the user’s search destination and object space. In the existing methods, effective points of interest are searched for, and L2 normalization is applied to the spatial arrangement of the original and global features and the RGB channels in the index image by effectively detecting their points of interest. The principal component analysis is used for redundant feature sets and feature sets to provide significant effects. The existing system has certain limitations, such as information loss during the hard task completion coding process and ignores the spatial relationship among the patches in image representation. To overcome the existing method issues, the integrated feature of the images is preferred. In this work, Semantic Weight Based Feed Forward Recurrent Back Propagation (SWFFRBP) is used to retrieve the images, and then based on the semantic weight the correctness of retrieval of images is calculated. The semantic weight determines the accuracy of retrieved images. After that, the recurrent backpropagation calculates the weight error derivatives arising from this entire episode of processing in the network. The existing features compared with the proposed features were Bag of Features (BoF), SURF, and HOG. The retrieval methods taken to compare with proposed retrieval techniques are RFRM and KNN. Thus, medical image retrieval for diagnosis using SWFFRBP, accuracy, False positive rate, relevance, feedback, precision, and recall parameters were tested and proved to be efficient than other methods.

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Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration

Multi-scale Frequency Attention Fusion Network for Optical a...

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International conference on Signal processing Proceedings (ICSP)

作者： Xin Hu Yan Wu Zhikang Li Zhifei Yang Ming Li School of Electronic Engineering Xidian University Xi’an China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

image registration is a prerequisite for remote sensing image fusion and classification, and registration accuracy affects the performance of these tasks. However, there are significant nonlinear radiation differences between optical and synthetic aperture radar (SAR) images and speckle noise in SAR, which limits the ability of pure convolutional neural networks (CNNs) to obtain modality-shared features. To solve these problems, this paper proposes a Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration (MFAF-OS-RegNet), which includes a Multi-scale Frequency Channel Attention Fusion (MFCAF) module and a Momentum Contrastive Learning (MCL) strategy. Firstly, we extract repeatable keypoints distributed on the edges and develop a novel MFCAF module to obtain modality-shared features. In the MFCAF module, multi-scale feature fusion is used to improve the positioning accuracy of matching points, and frequency channel attention is used to mine more favorable features. Secondly, we design a MCL strategy to reduce the interference of speckle noise in network training. The momentum teacher corrects handcrafted labels by learning the essential characteristics of the data for robust matching. Finally, we filter outliers and estimate transformation parameters based on the initial matching correspondence output by MFAF-OS-RegNet. Experimental results on a publicly available OS-dataset show that the proposed method outperforms other state-of-the-art methods.

关键词： Optical filters Integrated optics Optical fiber networks Optical imaging Radar polarimetry Adaptive optics Optical sensors Optical signal processing Nonlinear optics Synthetic aperture radar

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Research on classification method based on multi-scale segmentation and hierarchical classification

Research on classification method based on multi-scale segme...

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2021 International conference on Communication Technology and Information Technology, ICCTIT 2021

作者： Zhang, Xiaohua Wang, Hui Xue, Wenxiang Qin, Chaoyun Wu, Yuping Wang, Shuyuan Qiu, Peng State Grid Jibei Electric Power Co. Ltd. Beijing100045 China State Grid Jibei Electric Power Co. Ltd Beijing100045 China State Grid Jibei Maintenance Company Beijing100045 China

This paper transmission line corridors covering area in Hebei north area as the research object to explore multi-scale segmentation threshold suitable for Hebei north image, found applicable to Hebei north region segmentation threshold rules. Main methods are the object-oriented multi-scale segmentation and hierarchical classification, using image segmentation principle, make full use of high resolution image rich features such as shape, texture, object relationships. It is used for the follow-up investigation of hidden danger of external damage of power transmission channel in northern Hebei region. The main conclusions of the experiment are as follows: 1. By comparing and analyzing the results of five groups of different thresholds (40, 50, 60, 70 and 80), it is concluded that the single threshold of the multi-scale segmentation method suitable for most mountainous images in the study area is 60, and this method can achieve high-precision ground object classification for images in northern Hebei region. 2. The ground feature cover classification method that is more suitable for the parallel processing of a large number of study areas is the object-oriented hierarchical classification method, and preliminary exploration results of detailed parameters have been achieved. © Published under licence by IOP Publishing Ltd.

关键词： image segmentation

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parallel Computing Techniques for Accelerating Machine Learning Algorithms on Big Data

Parallel Computing Techniques for Accelerating Machine Learn...

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International conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Rahul Mishra Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

In the era of Big Data, the computational demands of machine learning (ML) algorithms have grown exponentially, necessitating the development of efficient parallel computing techniques. This research paper delves into the exploration and evaluation of advanced parallel computing methodologies tailored for accelerating ML algorithms when applied to vast datasets. We commence by providing a comprehensive overview of the existing parallelization paradigms, highlighting their strengths and limitations in the context of ML. Subsequently, we introduce novel techniques that harness the combined power of distributed systems, multicore processors, and graphics processing units (GPUs) to achieve significant speed-ups. Experimental results, derived from processing multi-terabyte datasets, demonstrate that our proposed methods can achieve up to a tenfold increase in computational efficiency compared to traditional approaches. Furthermore, we present a detailed analysis of the scalability, fault-tolerance, and resource utilization of our techniques, providing insights for practitioners aiming to deploy ML algorithms on large-scale data infrastructures. This work not only paves the way for faster ML computations but also offers a blueprint for the next generation of scalable, parallel ML systems.

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High Performance Spatial and Spatio-Temporal Big Data processing

High Performance Spatial and Spatio-Temporal Big Data Proces...

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作者： Baig, Furqan State University of New York at Stony Brook

学位级别：Ph.D.

This dissertation presents our research and development works to address the challenges for delivering effective, scalable, and high performance methods and systems for managing, and querying complex spatial and spatio-temporal big data at multiple dimensions. This is driven mainly by big data problems emerging from geospatial applications, medical image analysis, satellite imaging, weather analytics, and collaborative spatial data collection. Managing and analyzing such data poses major challenges, including explosion of data volume, an unprecedented rate of data generation, and high complexity of handling spatial and/or temporal dynamics. Traditional spatial data processing algorithms and data structures have been developed mostly for linear processing. Despite the need, these are not optimized for modern requirements of big data processing. To this end, we present strategies for adapting spatial and spatio-temporal principles and methods to the parallel and distributed domain. In particular, we approached the problem from three different aspects. In distributed domain, recent attempts have proposed Hadoop based strategies to accelerate big spatial data processing. Hadoop based systems, however, rely on disk IO, fairly limiting system performance. Apache Spark, on the other hand, is a widely popular framework, based on similar distributed principles, providing iterative in-memory processing. Despite their efficiency, Spark based systems are oblivious to spatial characteristics and provide limited memory optimizations catering to spatial data. To address these, we designed and implemented SparkGIS; which not only provid in-memory spatial querying capabilities but also introduce a novel resource-aware dynamic query re-writing mechanism to efficiently manage distributed resources for large spatial workloads. Spatial processing is inherently compute intensive due to its complexity and multi-dimensional properties. While SparkGIS addresses in-memory aspects of spatial

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distributed Data Register Technology as the Main Component of Economic Decentralization 7th

Distributed Data Register Technology as the Main Component o...

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7th International conference on Computing in Engineering and Technology, ICCET 2022

作者： Shvachych, Gennady Moroz, Boris Khylko, Maksym Perepolkina, Olena Busygin, Volodymyr Ukrainian State University of Science and Technology Dnipro Ukraine Dnipro University of Technology Dnipro Ukraine National Academy of Science of Ukraine Kyiv Ukraine Lviv University of Trade and Economics Lviv Ukraine Higher School of Insurance and Finance VUZF University Sofia Bulgaria

ISBN: (纸本)9789811927188

The paper shows that the widespread use of advanced information and communication technologies presupposes the transition of different industries and economies to the digital area, directly reflected in management methods at the macro and commercial entities, making the transition to a decentralized management system. In this regard, the paper illustrates that the digital economy reveals new opportunities. However, the proposed approach to the study allowed noting that the digital economy has several subtleties associated with insufficient knowledge, its flexibility, and various problems of technical implementation. The research’s main task is to reveal the features and principles of the formation of one of the significant components of the digital economy, the distributed ledger technology (blockchain). As a decentralized data registry, the paper notes that blockchain technology is today the most relevant and discussed topic of digital economy development. The technology strengths are analyzed: cost reduction, high-security level, and transaction transparency. The main mechanisms of contradictions, implementation, and application of blockchain technology are highlighted in detail. Finally, the author’s approach to eliminating the identified contradictions is provided. The studies in this paper demonstrate that one of the main problems is the features of the modeling process, both machine and mathematical. For instance, this technology requires powerful computing equipment and high-performance ones for servicing and solving security problems. It is noted that the most promising approach to increase the speed and productivity of computing facilities is parallel computing. distributed modeling allows processing the problems which solution demands huge processing time to study complex mathematical models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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