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Residual and Attention Mechanism-Based Network for Aluminum-Silicon Alloy Microstructure Image Classification 11

Residual and Attention Mechanism-Based Network for Aluminum-...

引用

11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Yang, Xiao Shen, Tao Yin, Jiancheng Liu, Yingli Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Materials Science and Engineering Kunming650093 China

ISBN: (纸本)9798350342406

To enhance the accuracy of aluminum-silicon alloy microstructure image classification, we propose an aluminum-silicon alloy microstructure image classification model based on an improved residual network, incorporating attention mechanisms and multi-scale thinking. The model is built upon the ResNet18 architecture, utilizing enhanced basic residual blocks to select informative features. Additionally, a multi-scale attention module is incorporated into the network to fuse multi-scale features and further extract effective features. Experimental results on an aluminum-silicon alloy microstructure image classification dataset demonstrate that compared to the best-performing model currently available, the proposed model achieves an increase in classification accuracy, precision, recall, and F1 score by 3.17%, 0.74%, 5.53%, and 3.26%, respectively, reaching 94.97%, 96.39%, 93.97%, and 95.17%. These results indicate that the proposed model exhibits strong capabilities in classifying aluminum-silicon alloy microstructure images and can facilitate further research on the properties of aluminum-silicon alloys. © 2023 IEEE.

关键词： Image classification

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A Wide Fuzzy Apriori Classifier with Triple Diversity Guarantee from Feature, Sample and Rule Levels

A Wide Fuzzy Apriori Classifier with Triple Diversity Guaran...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Shitong Wang Runshan Xie Jiangsu Key Construction Laboratory of IoT Application Technologies TaiHu University JiangSu China School of AI and Computer Science Jiangnan University Wuxi China

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

While fuzzy Apriori method (FAM) features its linguistic interpretability and uncertainty-handling ability, it still has difficulties in computational complexity and generalization capability, caused by the curse-of-dimensionality of input data. In order to overcome these two drawbacks, this study proposes a novel wide ensemble (WTD-FAM) of FAM sub-classifier with triple diversity guarantee. WTD-FAM can be realized as follows. Firstly, WTD-FAM draws several data subsets from input data by using both random subspace method and improved bagging. Then, WTD-FAM constructs FAM sub-classifiers on their respective data subsets, and discards some fuzzy rules in the ruleset of each FAM sub-classifier by knowledge oblivion. Finally, WTD-FAM obtains the final outputs by taking majority voting to aggregate the outputs of all the FAM sub-classifiers. WTD-FAM has three advantages: (1) lower computational complexity by building up each FAM sub-classifier on its own data subset; (2) more diversity among FAM sub-classifiers by triple diversity guarantee from feature, sample and rule levels. (3) extra generalization capability by wide ensemble learning method and knowledge oblivion. Both experimental results and ablation study on eight benchmarking datasets verify the effectiveness of WTD-FAM in the sense of classification performance and training speed.

关键词： Training Aggregates Linguistics Benchmark testing Association rule learning Ensemble learning Computational complexity

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Long-range Brain Graph Transformer 38

Long-range Brain Graph Transformer

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shuo Jin, Shan Li, Ming Sarwar, Tabinda Xia, Feng School of Computer Science and Technology Dalian University of Technology China Ministry of Education China School of Software Dalian University of Technology China Zhejiang Institute of Optoelectronics China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computing Technologies RMIT University Australia

Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependencies within brain networks while neglecting the long-range dependencies, limiting an integrated understanding of brain-wide communication. To address this limitation, we propose Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk. Specifically, we present a novel long-range aware strategy to explicitly capture long-range dependencies between brain ROIs. By guiding the walker towards the next hop with higher correlation value, our strategy simulates the real-world brain-wide communication. Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multilevel communication across the entire brain. Extensive experiments on ABIDE and ADNI datasets demonstrate that ALTER consistently outperforms generalized state-of-the-art graph learning methods (including SAN, Graphormer, GraphTrans, and LRGNN) and other graph learning based brain network analysis methods (including FBNETGEN, BrainNetGNN, BrainGNN, and BrainNETTF) in neurological disease diagnosis. Cases of long-range dependencies are also presented to further illustrate the effectiveness of ALTER. The implementation is available at https://***/yushuowiki/ALTER. © 2024 Neural information processing systems foundation. All rights reserved.

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Privacy Preserving CSI Fingerprint Device-Free Localization 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Tianxin Zhao, Lingjun Dai, Zeyang Lin, Liang Huang, Huakun School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China Greater Bay Area Institue of Precision Medicine Guangzhou China Guangdong Provincial Key Laboratory of Building Energy Efficiency and Application Technologies Guangzhou University Guangzhou China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Device-Free Localization (DFL) method based on wireless signal fingerprint is a mainstream indoor location method at present. The main concerns in this field are location accuracy and complexity of computing, while the problem of privacy protection is easily neglected. Since the leakage of wireless signal data is likely leading to the leakage of location information, this paper proposes a privacy-preserving Channel State Information(CSI) fingerprint device-free localization method based on federated learning(PPDFL). Through federated learning, each client trains a local model using the collected local data and then transmits the trained parameters to the server. The server consolidates these parameters and then distributes the resulting parameters to each client for local model’s updating. After several rounds of training and updating, each client ends up with a fully trained model. Compared with the existing DFL methods, this method would not share the data of each client, and only transmits model parameters between server and client, thus achieving the effect of privacy protection. Several experiments are performed to prove the effectiveness of the proposed PPDFL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Channel state information

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Multi-Modal Instrument Recognition Method Based on Improved YOLOv5s and ESPNet

Multi-Modal Instrument Recognition Method Based on Improved ...

引用

IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Shixian Shen Jianguo Wang Yong Feng Yingna Li Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China China Copper Industry Co. Ltd Kunming China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Addressing the challenges of high cost, low accuracy, and poor real-time performance in anomaly detection within industrial production processes, this study proposes a multi-modal instrument recognition method based on improved YOLOv5s and ESPNet. Specifically, a dynamic non-monotonic focusing mechanism is introduced into the YOLOv5s object detection model, enhancing its ability to accurately detect the position of pointer-type instruments. Simultaneously, affine and perspective transformations are employed to rectify skewed and rotated images. Secondly, variable convolutions are integrated into the ESPNet segmentation network to adaptively capture nonlinear deformation elements of pointers and dials in images, thereby extracting comprehensive key information from both. Subsequently, a Hough transform operation is applied to fit the straight line representing the pointer, while contour tracking methods are utilized to extract the valid range of dial data. Based on linear proportional relationships, the reading of pointer-type instruments is computed. Finally, a comprehensive model is built by integrating image-acquired instrument data with corresponding sensor data, enabling anomaly detection from a multimodal data analysis perspective. Experimental results demonstrate an identification accuracy of 96.21% for pointer-type instruments, with an average detection speed of 0.216 seconds and a data anomaly detection rate of 99.43%. This method accurately and rapidly identifies data anomalies, meeting the demands for key indicator monitoring in industrial production.

关键词： YOLO Industries Analytical models Image segmentation Accuracy Instruments Focusing Data models Anomaly detection Monitoring

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Spectral-Spatial Fusion Hypergraph Convolutional Network for Multispectral Point Cloud Classification 11

Spectral-Spatial Fusion Hypergraph Convolutional Network for...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Huang, Jiangbo Wang, Qingwang Zhang, Zifeng Liu, Sivang Wang, Zhifeng Shen, Tao Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming China Kunming University of Science and Technology School of Information Engineering and Automation Kunming China Real Estate Resource Management Center General Logistics Department of Pla Beijing China Beijing Anlu International Technology Co Beijing China

ISBN: (纸本)9798350342406

Graph Neural Networks (GNNs) based on simple graph can directly model 3D multispectral point clouds, thus avoiding the problem of 3D spatial information loss when converting 3D point clouds to 2D feature images. However, considering the dense distribution of objects in remote sensing scenes, GNNs only consider the association between pairs of vertices, which is insufficient for exploring high-order correlations and long-term dependence correlations. Hypergraph neural network (HGNN) is a more generalized framework that overcomes the limitations of traditional simple graph. In this paper, we propose a spectral-spatial fusion hypergraph convolutional network (S2FHGCN) for 3D multispectral point cloud classification. S2FHGCN first generates two hypergraphs from multidimensional features including spectral feature and spatial feature, respectively. The hypergraph structure can better handle complex high-order correlations for representation learning. Then the two hypergraphs are fused to obtain global representation information. Finally, the multidimensional features and the fused hypergraph are input to the hypergraph convolution network. The experimental results on real multispectral point clouds demonstrate the effectiveness of our proposed S2FHGCN for multispectral point cloud classification. © 2023 IEEE.

关键词： Remote sensing

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Existence of periodic and S-asymptotically periodic solutions to fractional diffusion equations with analytic semigroups

Existence of periodic and S-asymptotically periodic solution...

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作者： Mu, Jia Nan, Jiecuo Zhou, Yong Key Laboratory of China's Ethnic Languages and Information Technology of Ministry Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730000 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Xiangtan University Hunan411105 China Nonlinear Analysis and Applied Mathematics Research Group Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

In this paper, we study periodic and S-asymptotically periodic solutions for fractional diffusion equations (FDE). As we all know, there is no exact periodic solution to differential equations with Caputo or Riemann-Liouville fractional derivatives. Even so, in this paper, periodic (S-asymptotically periodic) mild or classical solutions for FDE with Weyl-Liouville fractional derivatives could be obtained in various fractional power spaces. In addition, a numerical simulation example and a specific example of fractional diffusion equation are given to verify the main theoretical results. © 2019 John Wiley & Sons, Ltd.

关键词： Diffusion

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Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset

Multi-objective performance prediction based on a self-const...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Dan Hu Yingli Liu Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

The combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. Therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. Then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction.

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A Novel Approach with Fusion Edge Prediction Strategy for Table Structure Recognition

A Novel Approach with Fusion Edge Prediction Strategy for Ta...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Yingli Liu Guangtao Zhang Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

Table, as an information carrier with high information density and direct display form, are widely used in scientific literature and social production documents. Accurately identifying tables from various documents will significantly promote the intelligent application of documents. The table’s structural information is crucial for comprehending how the text content in the table relates to one another, but teaching a computer to fully understand a table’s structure is still a very difficult undertaking. In this research work, we provided a unique edge-based method to recognize table structures. Specifically, we define the inter-cell relationships as an edge prediction problem and leverage graph convolutional networks to learn representations of the cells. These learned representations are then utilized to directly predict the connections between the cells. The experimental results conducted on three publicly available datasets for table structure recognition provide compelling evidence supporting the feasibility and usefulness of our presented methodology.

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Cross Modal Training for ASR Error Correction with Contrastive Learning

Cross Modal Training for ASR Error Correction with Contrasti...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jin Jiang Xiaojun Wan Wei Peng Rongjun Li Jingyuan Yang Yanquan Zhou Beijing University of Posts and Telecommunications Wangxuan Institute of Computer Technology Peking University The MOE Key Laboratory of Computational Linguistics Peking University Artificial Intelligence Application Research Center Huawei Technologies

ASR Error Correction (AEC) aims to post-process the output of ASR systems and further reduce the word error rate. In this paper, we propose a cross-modal training framework with contrastive learning on the AEC task. This framework enables a shared encoder-decoder model to learn text, pinyin (phoneme 1 ) and audio information simultaneously, which is trained by three subtasks: text correction, pinyin to text and ASR. On this basis, we introduce contrastive learning loss to shrink the distance between the three modalities and construct a unified representation. Experiments 2 on four AEC datasets show that our method effectively corrects a large number of ASR errors to state-of-the-art levels.

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