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Numerical Solution of the Combustion Process Using the computer Package Ansys Fluent 1

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22nd International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2022

作者： Khujaev, Ismatulla Ko’shaevich Fayziev, Rabim Alikulovich Hamdamov, Muzaffar Muhiddinovich Institute of Mechanics and Seismic Stability of Structures of the Academy of Sciences of the Republic of Uzbekistan Tashkent Uzbekistan Department Mathematical Methods in Economics Tashkent State University of Economics Tashkent Uzbekistan

ISBN: (数字)9783031302589

ISBN: (纸本)9783031302572

The results of numerical studies of the processes of mixing, ignition and combustion during the expiration of jets of methane-air mixture in an axisymmetric channel. Numerical modeling of a turbulent subsonic nonequilibrium flow was performed within the framework of two-dimensional stationary equations of a "narrow channel" taking into account final rates of chemical kinetics within the framework of the global mechanism. The purpose of this work is to develop a numerical method for calculating the mixing, combustion, and propagation of various compositions of combustible mixtures in a cylindrical chamber, which makes it possible to conduct a computational experiment to study the processes of heat and mass transfer. The main problem of mathematical modeling of gas fuel combustion processes within the framework of the ANSYS Fluent software package is the choice of a turbulence model. In this work, the Patankar-Spaulding finite-difference method is used. The application of this method makes it possible to move from a rectilinear finite-difference grid to a modified curvilinear grid, which automatically "adjusts" to the flow area. Such a grid is especially convenient when calculating expanding free flows, since it does not require the addition of additional finite-difference grid nodes associated with the expansion of jet particles during the calculation process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Turbulence models

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Towards a Lightweight CNN for Semantic Food Segmentation 27th

Towards a Lightweight CNN for Semantic Food Segmentation

引用

27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Muñoz, Bastián Remeseiro, Beatriz Aguilar, Eduardo Department of Computing and Systems Engineering Catholic University of the North Antofagasta Chile Departament de Matematiques i Informatica Universitat de Barcelona Barcelona Spain Department of Computer Science Universidad de Oviedo Gijon Spain

ISBN: (纸本)9783031766060

Semantic food segmentation is an important task for the development of nutritional systems that effectively manage daily diets. Recent advances in semantic segmentation have brought great performance improvements. However, these methods require high computational resources that limit their use in a mobile application without relying on external servers to perform the segmentation. Lightweight Convolutional Neural Networks (CNNs) have emerged as an efficient alternative for deploying deep network-based models on mobile devices, a solution not yet applied to semantic food segmentation. In this paper, we propose a lightweight variant of DeepLabv3+, replacing the standard backbone with the lightweight CNN EfficientNet-B1 and the Atrous Spatial Pyramid Pooling (ASPP) with the Cascade Waterfall ASPP (CWASPP). Validation of the proposed lightweight DeepLabv3+ method, in terms of mIoU, parameters and FLOPs, was performed on two public food datasets: UNIMIB2016 and UECFoodPixComplete. The experimental results show a better tradeoff between segmentation performance and computational resources than the state-of-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Attention4Align: Align Multi-view Parts Via Part2Part Hierarchical Attention Map for Fine-Grained 3D Object Classification 17th

Attention4Align: Align Multi-view Parts Via Part2Part Hierar...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Zhang, Runchu Yue, Jiahe Zhang, Zhe Ma, Jie Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China

ISBN: (纸本)9789819609680

Multi-view methods offer an effective approach to address 3D object classification, and there is a growing interest in handling more practical scenarios, such as fine-grained distinctions and arbitrary viewpoints. Fine-grained object distinctions often appearance around local parts, inspiring various part-based methods. However, the parts generated by these methods are typically unordered, significantly impacting the aggregation operation across different views and consequently diminishing overall performance. To address this issue, we propose a Part2Part Hierarchical Attention, facilitating information exchange and feature enhancement among parts in different viewpoints through attention within and across views. Subsequently, the Part2Part Attention Map generated during the attention process is utilized to measure the distance between multi-view parts, aiding in their alignment. We also employ multi-scale feature fusion to enhance the quality of parts generated by weakly supervised learning. Experimental results indicate that, under the same settings, our approach achieves state-of-the-art performance on the FG3D and MVP-N datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Supervised learning

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Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Belgaum, Mohammad Riyaz Momina, Shaik Maryam Farhath, L. Nousheen Nikhitha, K. Naga Jyothi, K. Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal Cancer 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2022

作者： La Greca Saint-Esteven, Agustina Motisi, Laura Balermpas, Panagiotis Tanadini-Lang, Stephanie University Hospital Zurich University of Zurich Rämistr. 100 Zurich8091 Switzerland Computer Vision Laboratory ETH Zurich Sternwartstr. 7 Zurich8092 Switzerland

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Head and Neck (HN) cancer has the sixth highest incidence rate of all malignancies worldwide. One of the two main curative treatments for this malignancy is radiotherapy, whose delivery depends on accurate contouring of the primary tumor and affected lymph nodes among other structures. In this study, we present a transfer learning-based approach for the automatic primary tumor and lymph nodes segmentation in fused positron emission tomography (PET) and computed tomography (CT) images belonging to the HECKTOR challenge dataset. Transfer learning is performed from the Genesis Chest CT model, a publicly available 3D U-net, pre-trained on chest CT scans. Three-fold cross-validation is employed during training, so that, on each fold, two different binary segmentation models are chosen, one for the primary tumor and one for the lymph nodes. During testing, majority voting is applied. Our results show promising performance on the training and validation cohorts, while moderate performance was observed in the test cohort. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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The (Un)Answered Question: A Data science Powered Music Experiment 12th

The (Un)Answered Question: A Data Science Powered Music Expe...

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12th International Conference on Culture and Computing (C and C) Held as Part of the 26th International Conference on Human-computer Interaction (HCII)

作者： von Kurnatowski, Lynn Wolff, Benjamin Kernchen, Sophie Klapproth-Rieger, Adriana Heidrich, David Haupt, Carina Schreiber, Andreas Niendorf, Thoralf Kosmider, Andreas Lobbes, Marcus Hennecke, Martin German Aerosp Ctr DLR Inst Software Technol D-51147 Cologne Germany Max Delbrueck Ctr Mol Med MDC Berlin Ultrahigh Field Facil D-13125 Berlin Germany Expt & Clin Res Ctr ECRC D-13125 Berlin Germany Falling Walls Fdn gGmbH D-10969 Berlin Germany Acad Theatre & Digital D-44137 Dortmund Germany Saarland Staatstheater D-66111 Saarbrucken Germany

ISBN: (纸本)9783031611469;9783031611476

This paper describes the intentions, setup, and live performance of a musical experiment that explores the complex intersection of human-technology interactions, music, and data collection. It brings art and data science together through a novel experimental music installation. The interdisciplinary project "The (Un)Answered Question: A Data science Powered Music Experiment" explored integrating data science and biomedical imaging techniques with theatrical and compositional ideas. This combination leads to the creation of interactive music. Gestural interfaces and sensory input devices translate physiological behavior into music through digital signal processing. Ralph Waldo Emerson's poem "The Sphynx" and Charles Ives' composition "The Unanswered Question" serve as foundational elements to create a live remix of the original music using biometric data from performers and an audience of 180 people. The audience became a powerful instrument of musical expression. Each live performance was experiential and unique, depending on the different people involved.

关键词： Peroforming Arts Music Data science Biometrics

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An Encoder-Based Framework for Privacy-Preserving Machine Learning 1

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Jiayun Ren, Wei Zhang, Xianchao Zheng, Xianghan School of Computer Science China University of Geosciences Wuhan430074 China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China Ministry of Education Chengdu China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China College of Computer and Big Data Fuzhou University Fujian Fuzhou China School of Information and Intelligent Engineering Sanya College Hainan China

ISBN: (数字)9789819615513

ISBN: (纸本)9789819615506

As a data-driven science, machine learning requires vast amounts of training data and computational resources. However, for highly privacy-sensitive data, it is crucial to protect the privacy of the data during both the training and utilization of machine learning models. In this paper, we propose a privacy-preserving machine learning approach using autoencoders and differential privacy mechanisms to safeguard data privacy while minimizing the impact on data availability. Specifically, we augment logistic regression and ResNet18 models with different architectures of autoencoders to perform data encryption? without compromising the machine learning tasks. Additionally, we employ differential privacy mechanisms to introduce gradient perturbations in the encoding part of the autoencoder, enhancing the algorithm’s security and further protecting data privacy. We also design the cosine similarity between the encoded and original data as a metric for evaluating data privacy, considering model performance, privacy budget, and data privacy collectively to balance data availability and privacy. Extensive experiments conducted on MNIST, CIFAR-10, PathMNIST, and BloodMNIST datasets demonstrate that for simple logistic regression models handling easily classifiable datasets, employing simple autoencoder structures can enhance classification accuracy, with significant performance impact after adding differential privacy. For ResNet18, utilizing convolutional autoencoders for data encryption generally has minimal impact on model classification performance and can even improve accuracy in most cases. Adding differential privacy has minor effects on model classification performance. Selecting appropriate model structures and privacy budgets for different usage scenarios can ensure both data availability and privacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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DSTNet: Distinguishing Source and Target Areas for Image Copy-Move Forgery Detection 27th

DSTNet: Distinguishing Source and Target Areas for Image Cop...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Kaiqi Yuan, Xiaochen Huang, Guoheng Liu, Kun School of Cyberspace Security Shandong University of Political Science and Law Jinan China Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9783031783111

In copy-move forgery detection, most relevant studies concern locating the copy-move areas without the distinction of source and target regions. This paper proposes an end-to-end network, DSTNet, to identify the source and target based on consistency detection between the copy-move region and the non-copy-move region. The DSTNet is composed of two stages, the Pre-processing stage and the Discrimination stage. Pre-processing Stage extracts internal information of copy-move and non-copy-move areas and conducts a series of operations to meet the requirements of network input. Discrimination stage allows multiple patches for input and classifies the input patches. Specifically, the Pre-processing stage, contains the Copy-move Patches Selection (CM Patches Selection) and Genuine Patches Selection, can select pairs of copy-move and none copy-move patches. We train the proposed DSTNet on two large synthetic datasets and use the public datasets CASIA and Comofod for evaluation. The experiment shows that our method achieves excellent results. Particularly, we achieve a 5.4% higher F1 based on ground-truth of copy-move mask (GT-CM) on CASIA dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Large datasets

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A Learnable Graph Convolutional Neural Network Model for Relation Extraction 1

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28th China Conference on Information Retrieval, CCIR 2022

作者： Xu, Jinling Chen, Yanping Qin, Yongbin Huang, Ruizhang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (数字)9783031247552

ISBN: (纸本)9783031247545

Relation extraction is the task of extracting the semantic relationships between two named entities in a sentence. The task relies on semantic dependencies relevant to named entities. Recently, graph convolutional neural networks have shown great potential in supporting this task, wherein dependency trees are usually adopted to learn semantic dependencies between entities. However, the requirement of external toolkits to parse sentences poses a problem, owing to them being error prone. Furthermore, entity relations and parsing structures vary in semantic expressions. Therefore, manually designed rules are required to prune the structure of the dependency trees. This study proposed a novel learnable graph convolutional neural network model (L-GCN) that directly encodes every word of a sentence as nodes of a graph neural network. Then, the L-GCN uses a learnable adjacency matrix to encode dependencies between nodes. The model offers the advantage of automatically learning high-order abstract representations of the semantic dependencies between words. Moreover, a fusion module was designed to aggregate the global and local semantic structure information of sentences. Further, the proposed L-GCN was evaluated on the ACE 2005 English dataset and Chinese Literature Text Corpus. The experimental results confirmed the effectiveness of L-GCN in learning the semantic dependencies of a relation instance. Moreover, it clearly outperformed previous dependency-tree-based models. © 2023, Springer Nature Switzerland AG.

关键词： Neural network models

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Multi-step Air Quality Index Forecasting Based on Parallel Multi-input Transformers 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Xie, Jie Li, Jun Zhu, Mingying Wang, Qiong School of Computer and Electronic Information and School of Artificial Intelligence Nanjing Normal University Nanjing210046 China Key Laboratory of Modern Acoustics MOE Nanjing University Nanjing China School of Economics Nanjing University 22 Hankou Road Jiangsu Nanjing210093 China Johns Hopkins University Hopkins United States Nanjing Center for Chinese and American Studies Nanjing University 162 Shanghai Road Jiangsu Nanjing China

ISBN: (数字)9783031476655

ISBN: (纸本)9783031476648

Air quality index (AQI) forecasting is a hot research topic that has been widely explored by the whole society. To better understand environmental quality, numerous methods have been proposed for investigating air pollutant data. Previous studies have used deep learning-based methods for AQI forecasting, but few studies investigate parallel multi-input deep models for multi-step hourly AQI forecasting. In this study, a novel parallel multi-input transformer architecture is proposed for multi-step hourly AQI forecasting. To model the air quality data, the transformer is selected and compared with four bidirectional-long-short term memory-based models. Moreover, parallel variable embedding is used to boost forecasting performance. Experimental results using air quality data collected in Shanghai show that our proposed method achieves superior performance against various competing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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