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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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Short and Medium-Term Campus Electricity Load Forecasting with the CNN-Transformer-LSTM Hybrid Model 8

Short and Medium-Term Campus Electricity Load Forecasting wi...

引用

8th International conference on Electrical, Mechanical and computer Engineering, ICEMCE 2024

作者： He, Haoqian University of Warwick CoventryCV4 7AL United Kingdom

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506230

Accurate power load forecasting is critical for effective energy management in a campus environment where power use is affected by dynamic factors such as campus schedules, events, and weather. In order to improve the accuracy of short-to-medium term campus load prediction, this paper proposes a hybrid model, which combines Convolutional Neural Network (CNN) for local feature extraction and Transformer encoder for capturing global dependencies. Long Short-Term Memory (LSTM) Network is utilized to model long-term time patterns. The experimental results show that the CNN-Transformer-LSTM model consistently outperforms both the CNN-LSTM and conventional LSTM models, achieving significantly lower RMSE (57.5259), MAE (44.7930), and MAPE (1.05%) values, especially in medium-term prediction, with significant improvement in short-term prediction as well (RMSE: 44.6703, MAE: 36.5093, MAPE: 0.96%). This hybrid model demonstrated excellent learning capabilities in capturing complex time dependencies, making it an effective and powerful solution for enhancing campus energy management and strategic planning. © 2024 ieee.

关键词： Convolutional neural networks

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UnifiedGT: Towards a Universal Framework of Transformers in Large-Scale Graph Learning

UnifiedGT: Towards a Universal Framework of Transformers in ...

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2024 ieee International conference on Big Data, BigData 2024

作者： Lin, Junhong Guo, Xiaojie Zhang, Shuaicheng Zhou, Dawei Zhu, Yada Shun, Julian Mit Csail United States Ibm Research United States Virginia Tech United States

ISBN: (纸本)9798350362480

Graph learning plays a pivotal role in many high-impact application domains. Despite significant advances in this field, there is currently no single solution that effectively handles (1) data heterogeneity, (2) long-range dependencies, (3) graph heterophily, and (4) scalability to large graphs, all at the same time. Classical graph neural networks (GNNs) and graph transformers (GTs) address some but not all of these issues, often suffering from limitations such as quadratic computation complexity and/or suboptimal generalization performance in realistic applications. This paper introduces UnifiedGT, a novel framework that systematically addresses all of these challenges by automatically providing a graph transformer architecture with multiple components via neural architecture search. UnifiedGT consists of five major components: (1) graph sampling, (2) structural prior injection, (3) graph attention, (4) local/global information mixing, and (5) type-specific feedforward networks (FFNs). This modular approach enables the efficient processing of large-scale graphs and effective management of heterogeneity and heterophily while capturing long-range dependencies. We demonstrate the versatility of UnifiedGT through comprehensive experiments on several benchmark datasets, revealing insights such as the efficacy of graph sampling for GTs, the importance of explicit graph structure injection via attention masking, and the synergistic effect of local/global information mixing via a combination of global attention with local message passing. Furthermore, we formulate these design choices into a search space, where an optimal combination can be discovered for a particular dataset via neural architecture search. Notably, UnifiedGT improves generalization performance on various graph datasets, outperforming state-of-the-art GT models by a margin of about 3.7% on average. The framework is available on Github (https://***/junhongmit/H2GB) and PyPI (https://***/project

关键词： Graph neural networks

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Adaptive Sparse Self-attention for Object Detection

Adaptive Sparse Self-attention for Object Detection

引用

ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Xu, Mingjie Song, Yonghong Xie, Kangkang Guo, Pengcheng Mu, Jiaxi Liu, Wen Wang, Zhengyang Xi An Jiao Tong Univ Sch Software Engn Xian Shaanxi Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Object detection is a fundamental task for computer vision. The majority of prior methods employ global contextual information to enhance features representation. However, we argue that prior methods suffer from the superfluous features. To address the above mentioned problems, we explore the sparsity on object detection tasks in two dimensions. Specifically, a sparse spatial attention is proposed to capture global sparse long-range relationship into local features adaptively, where a learnable channel-wise mask is obtained to reduce the superfluous channels. Meanwhile, a sparse channel attention module is used to enhance the representation of each channel by introducing sparse global semantics. Experiments demonstrate that our proposed method outperforms comparative methods on the commonly used benchmark dataset, i.e., MS-COCO. The ablative experiments show that the sparsity the effectiveness in feature extraction and bounding boxes selection.

关键词： computer vision Adaptation models Semantics Neural networks Object detection Benchmark testing Feature extraction

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An Effective Point Cloud Classification Method Based on Improved Non-local Neural networks 25

An Effective Point Cloud Classification Method Based on Impr...

引用

25th ieee International conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Song, Yanan Liu, Xianfei Shen, Weiming Gao, Yiping Zhou, Xianke Lu, Peng College of Computer Science and Technology Zhejiang University Institute of Computing Innovation Hangzhou China Zhejiang University Institute of Computing Innovation Hangzhou China State Key Lab. of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665405270

Deep learning is an important method to deal with point cloud, but its ability is limited to extract local features of point cloud. Many deep learning networks are designed to capture the local information, but they ignore the importance of non-local features to the point cloud. This paper proposes an improved non-local neural networks for point cloud classification. The non-local module can extract local and non-local features of the point cloud simultaneously. The local information is obtained based on the feature distance between neighborhood points searched by k-nearest neighbor method. The extracted local features are integrated into the non-local network, which can capture non-local features from the entire point cloud. The designed non-local module can be easily inserted into the existing point cloud processing network. The proposed method is evaluated on well-known ModelNet40 shape classification benchmark. Experimental results show that the proposed method achieves a significant improvement in classification accuracy. © 2022 ieee.

关键词： Deep learning

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System Architecture for a Wireless Sensor Network for Sustainable Agriculture, Forestry and Green Spaces in Smart Cities 12

System Architecture for a Wireless Sensor Network for Sustai...

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12th ieee International conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Rosewick, Sebastian Schorlemer, Felix Kunemund, Frank Fachhochschule Dortmund Department of Computer Science Germany

ISBN: (纸本)9798350358056

This paper presents a wireless sensor network employing heterogeneous mobile robot platforms designed for data-acquisition and research in agriculture, forestry and green spaces in smart cities. Where conventional sensor networks struggle with a lack in flexibility, little variation in their reading points and no possibilities in mobile manipulation, this approach offers new capabilities in accomplishing complex tasks in outdoor and indoor scenarios. A wheel driven robot platform in combination with multiple UAVs11Unmanned Aerial Vehicle forms the basis for the mobile sensor network which can make use of a wide variety of sensors and actuators. The system benefits from new developments in controlling multi-agent systems as well as machine-learning-algorithms for local and independent data-analysis. © 2023 ieee.

关键词： forestry machine learning mesh-networks mobile robots multi-agent systems neural networks precision agriculture UAVs wireless sensor network

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Mamba-Based Residual Network for Remote Sensing Image Super-Resolution 7

Mamba-Based Residual Network for Remote Sensing Image Super-...

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7th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhao, Gongbo Wang, Feng Shanghai200433 China Yiwu Research Institute Fudan University Yiwu322000 China

ISBN: (纸本)9798350350890

Image super-resolution is the process of recovering high-resolution images from low-resolution inputs, which is crucial in low-level computer vision tasks. In recent years, progress in deep learning has significantly propelled the development of image super-resolution technology. Various models, such as convolutional neural networks (CNNs), generative adversarial networks, Transformers, and diffusion models, have been employed to address this problem with significant impact. Among these models, CNNs are effective in capturing local features but have limitations in modeling long-range dependencies;Transformers, despite their powerful modeling capabilities, face computational bottlenecks due to the quadratic complexity of the self-attention mechanism when dealing with large images. To address these issues, we propose the Residual Mamba Block, which combines state space models (SSMs) with residual connections, providing a linear complexity attention mechanism that effectively enhances long-range dependency modeling. Experimental results demonstrate the effectiveness and superior performance of the proposed method. Furthermore, our research indicates that vision state space models have great potential to become a foundational framework for future super-resolution tasks. © 2024 ieee.

关键词： Convolutional neural networks

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Domain knowledge guided deep neural networks (DKG-DNN) for prediction of diaphragm wall deformation induced by excavation 36

Domain knowledge guided deep neural networks (DKG-DNN) for p...

引用

36th International conference on Tools with Artificial Intelligence

作者： Zhao, Huajing Wang, Meng Yan, Yujie Soochow Univ Sch Rail Transportat Suzhou Peoples R China Southeast Univ Natl & Local Joint Engn Res Ctr Intelligent Const Nanjing Peoples R China

ISBN: (纸本)9798331527242;9798331527235

Prediction of diaphragm wall deformation is vital to intelligent construction and environment safety in excavation engineering. This paper presents a novel prediction model entitled domain knowledge guided deep neural networks (DKG-DNN) which is tailored for the particular problem of diaphragm wall modeling. In the proposed model, deep neural network (DNN) is being trained to improve the prediction accuracy, whereas domain knowledge (DK) enables that the deformation predictions translate to the relationships between other physical variables, namely inclination location and vertical depth. To illustrate the effectiveness of DKG-DNN, experiments are conducted based on datasets from a real-world excavation project. The results show that DKG-DNN is competent to extract the complex spatiotemporal correlations of the wall deformations and to present satisfactory prediction accuracy. With the incorporation of the DK module, the robustness of the DNN model is improved to a large extent. Furthermore, collaboration between domain knowledge and deep learning in terms of framework design and learning objectives provides a new research paradigm which is highly possible to be generalized to other prediction problems in geotechnical engineering.

关键词： excavation engineering diaphragm wall deformation domain-knowledge spatial-temporal prediction

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Joining CNNs and Transformer networks for enhanced breast ultrasound image segmentation 6

Joining CNNs and Transformer networks for enhanced breast ul...

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6th International conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Tagnamas, Jaouad Ramadan, Hiba Yahyaouy, Ali Tairi, Hamid University of Sidi Mohamed Ben Department of Informatics Faculty of Sciences Dhar El Mahraz Abdellah Fez Morocco USPN LaMSN - La Maison des Sciences Numérique Paris France University of Sidi Mohamed Ben Abdellah Fez Morocco

ISBN: (纸本)9798350350180

Breast cancer is a significant global health concern, primarily affecting women. Numerous methods for segmenting breast ultrasound (BUS) images have been developed using convolutional neural networks (CNNs) to ensure accurate medical image diagnosis. However, CNNs have limitations in capturing long-range relationships, resulting in reduced segmentation accuracy for BUS. On the other hand, Transformers excel at capturing global information but struggle with extracting local details and require extensive training datasets. In this study, we introduce a novel hybrid CNNs-Transformer architecture for segmenting BUS images. Our approach is built based on the Ushaped design with dual encoders. The first encoder employs CNN blocks to extract local and fine-grained features, while the second encoder utilizes the abilities of Swin-Transformer architecture to proficiently model global dependencies. We incorporate the Efficient Channel Attention (ECA) module to improve the features extracted from the encoders and suppress the irrelevant ones. The decoder part of our architecture combines the extracted features and employs skip-connections to reconstruct the segmented BUS images. Through thorough testing and assessment on publicly available BUS datasets, we demonstrate that our proposed method surpasses recent approaches for BUS image segmentation with a dice coefficient of 80.5% on the Breast Ultrasound Images dataset. © 2024 ieee.

关键词： Ultrasonic imaging

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Position-aware Hypergraph Message-Passing Neural Network

Position-aware Hypergraph Message-Passing Neural Network

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Xinyu Jiang, Qize Zhang, Hanyuan Sun, Weiwei School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (纸本)9798350368741

Hypergraph neural networks can model more flexible connectivity relationships, are used to model higher-order interactions, and have produced strong results in many real-world applications. However, the currently existing hypergraph neural networks need more exploration in capturing the global positional information of nodes in hypergraphs. Although there have been many explorations of the problem in graph neural networks, extending these approaches to hypergraphs is fraught with challenges. The major challenge is that hyperedges in hypergraphs are the other dimensional element of the incidence structure, have more flexible definitions than edges in graphs, and require more attention when learning global positional information. We propose a novel position-aware hypergraph message-passing neural network framework to address the above challenges. Specifically, we propose a global positional embedding learning approach that can separately model global positional information for nodes and hyperedges. At the same time, we also optimize the learning of local structures with hyperedges. Experiments on several publicly available benchmark datasets find that our proposed method outperforms many state-of-the-art methods. © 2025 ieee.

关键词： Hypergraph Learning Hypergraph Neural Network Message Passing Neural Network

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A Hybrid Perturbed Secret Sharing Federated Learning Framework 6

A Hybrid Perturbed Secret Sharing Federated Learning Framewo...

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6th International conference on Next Generation Data-Driven networks, NGDN 2024

作者： Feng, Yinglin Chen, Shuhong Cheng, Zhaoyi Su, Weifeng Wang, Tian Guangzhou University School of Computer Science and Network Engineering Guangzhou China Beijing Normal University BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing China

ISBN: (纸本)9798350388374

In recent years, the problem of privacy leakage in federated learning has received much attention. To address this issue, researchers have applied privacy preserving techniques to federated learning. However, differential privacy adding too much noise can affect the generalization of the model and the availability of data. Secret sharing may also lead to data leakage if attacked. In order to tackle the problems of excessive noise and data security, this paper proposes a hybrid perturbation secret sharing federated learning framework (HPS-FL). It optimizes local difference privacy based on mean statistics and mitigates the effect of excessive noise by changing the perturbation boundary and reducing the variance. It also introduces secret sharing technology, which further increases the security of parameter transmission by adding a mask before secret sharing. Experimental results on the MINIST dataset demonstrate that HPS-FL outperforms other methods in terms of model accuracy, providing reliable support for privacy preserving. © 2024 ieee.

关键词： Federated learning

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