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multi-view convolutional neural network for Data Spoofing Cyber-Attack Detection in Distribution Synchrophasors

IEEE TRANSACTIONS ON SMART GRID 2020年第4期11卷 3457-3468页

作者： Qiu, Wei Tang, Qiu Wang, Yajun Zhan, Lingwei Liu, Yilu Yao, Wenxuan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Oak Ridge Natl Lab Elect & Elect Syst Res Div POB 2009 Oak Ridge TN 37830 USA

Security of Distribution Synchrophasors Data (DSD) is of paramount importance as the data is used for critical smart grid applications including situational awareness, advanced protection, and dynamic control. Unfortunately, the DSD are attractive targets for malicious attackers aiming to damage grid. Data spoofing is a new class of deceiving attack, where the DSD of one Phasor Measurement Units (PMUs) is tampered by other PMUs thereby spoiling measurement based applications. To address this issue, a source authentication based data spoofing attack detection method is proposed using multi-view convolutional neural network (MCNN). First, common components embedded in raw frequency measurements from DSD are removed by Savitzky-Golay (SG) filter. Second, fast S transform (FST) is utilized to extract representative spatial fingerprints via time frequency analysis. Third, the spatial fingerprint is fed to MCNN, which combines dilated and standard convolutions for automatic feather extraction and source identification. Finally, according to the output of MCNN, spoofing attack detection is performed via threshold criterion. Extensive experiments with actual DSD from multiple locations in FNET/Grideye are conducted to verify the effectiveness of the proposed method.

关键词： Data spoofing attack distribution synchrophasors data multi-view convolutional neural network source authentication

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Hierarchical Graph Attention Based multi-view convolutional neural network for 3D Object Recognition

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IEEE ACCESS 2021年 9卷 33323-33335页

作者： Zeng, Hui Zhao, Tianmeng Cheng, Ruting Wang, Fuzhou Liu, Jiwei Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528399 Peoples R China

For multi-view convolutional neural network based 3D object recognition, how to fuse the information of multiple views is a key factor affecting the recognition performance. Most traditional methods use max-pooling algorithm to obtain the final 3D object feature, which does not take into account the correlative information between different views. To make full use of the effective information of multiple views, this paper introduces the hierarchical graph attention based multi-view convolutional neural network for 3D object recognition. At first, the view selection module is proposed to reduce redundant view information in multiple views, which can select the projective views with more effective information. Then, the correlation weighted feature aggregation module is proposed to better fuse multiple view features. Finally, the hierarchical feature aggregation network structure is designed to further to make full use of the correlation information of multiple views. Extensive experimental results have validated the effectiveness of the proposed method.

关键词： Three-dimensional displays Object recognition Two dimensional displays neural networks Feature extraction Solid modeling convolutional neural networks 3D object recognition multi-view convolutional neural network graph attention network feature aggregation

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Feature Classification Method of Resting-State EEG Signals From Amnestic Mild Cognitive Impairment With Type 2 Diabetes Mellitus Based on multi-view convolutional neural network

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2020年第8期28卷 1702-1709页

作者： Wen, Dong Li, Peng Zhou, Yanhong Sun, Yanbo Xu, Jian Liu, Yijun Li, Xiaoli Li, Jihui Bian, Zhijie Wang, Lei Yanshan Univ Sch Informat Sci & Engn Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao 066004 Hebei Peoples R China Hebei Normal Univ Sci & Technol Sch Math & Informat Sci & Technol Qinhuangdao 066004 Hebei Peoples R China Beijing Normal Univ Natl Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Beijing Friendship Hosp Dept Neurol Beijing 100050 Peoples R China Rocket Force Gen Hosp Chinese Peoples Liberat Arm Dept Neurol Beijing 100088 Peoples R China

The convolutional neural network (CNN) model is an active research topic in the field of EEG signals analysis. However, the classification effect of CNN on EEG signals of amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) is not ideal. Even if EEG signals are transformed into multispectral images that are more closely matched with the model, the best classification performance can not be achieved. Therefore, to improve the performance of CNN toward EEG multispectral image classification, a multi-view convolutional neural network (MVCNN) classification model based on inceptionV1 is designed in this study. This model mainly improves and optimizes the convolutional layers and stochastic gradient descent (SGD) in the convolutional architecture model. Firstly, based on the discreteness of EEG multispectral image features, the multi-view convolutional layer structure was proposed. Then the learning rate change function of the SGD was optimized to increase the classification performance. The multi-view convolutional nerve was used in an EEG multispectral classification task involving 19 aMCI with T2DM and 20 normal controls. The results showed that compared with the traditional classification models, MVCNN had a better stability and accuracy. Therefore, MVCNN could be used as an effective feature classification method for aMCI with T2DM.

关键词： multi-view convolutional neural network aMCI with T2DM EEG signals feature classification

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Learning-Based multiple Pooling Fusion in multi-view convolutional neural network for 3D Model Classification and Retrieval

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2019年第5期15卷 1179-1191页

作者： Zeng, Hui Wang, Qi Li, Chen Song, Wei Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Engn Res Ctr Ind Spectrum Imaging Beijing Peoples R China North China Univ Technol Sch Comp Sci & Technol Beijing Peoples R China

We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its "learning" ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.

关键词： Learning-Based multiple Pooling Fusion multi-view convolutional neural network 3D Model Classification 3D Model Retrieval

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Whole Tumor Segmentation from Brain MR images using multi-view 2D convolutional neural network 43

Whole Tumor Segmentation from Brain MR images using Multi-vi...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Lahoti, Ritu Vengalil, Sunil Kumar Venkategowda, Punith B. Sinha, Neelam Reddy, Vinod Veera Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9781728111797

In this paper, a study is reported on the popular BraTS dataset for segmentation of brain tumor. The BraTS 2019 dataset is used that comprises four MR modalities along with the ground-truth for 259 high grade glioma (HGG) and 76 low grade glioma (LGG) patient data. We have employed U-Net architecture based 2D convolutional neural network (CNN) for each of the orthogonal planes (sagittal, coronal and axial) and fused their predictions. The objective function is aimed to minimize Dice loss between the binary prediction and its actual labels. Samples having tumor information are considered for each patient data to avoid training on non-informative data. The models are trained on 222 HGG data and tested on 37 HGG data using performance metrics such as sensitivity, specificity, accuracy and Dice score. Test-time augmentation is also performed to improve the segmentation performance. 7-fold cross validation is conducted to analyze the performance on different sets of training and testing data.

关键词： Brain tumor segmentation MRI multi-view convolutional neural network

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Two-dimensional Seismic Velocity Inversion via Enhanced multi-view convolutional neural networks for Regression

Two-dimensional Seismic Velocity Inversion via Enhanced Mult...

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International Joint Conference on neural networks (IJCNN)

作者： Pan, Chuang Huang, Yiran Wang, Qingzhen Li, Jun Xu, Jianhua Nanjing Normal Univ Sch Artificial Intelligence Sch Comp & Elect Informat Nanjing Peoples R China Boston Univ Dept Comp Sci Sch Arts & Sci 111 Cummington St Boston MA 02215 USA CNOOC Res Inst Ltd Natl Engn Res Ctr Offshore Oil & Gas Explorat Beijing Peoples R China Nanjing Normal Univ Sch Artificial Intelligence Nanjing Peoples R China New Jersey Inst Technol Dept Data Sci Newark NJ 07102 USA

ISBN: (纸本)9798350359329;9798350359312

Seismic exploration is a mainstream technique to find subsurface raw oil and gas in energy industry, which stimulates seismic wave on the Earth's surface, receives seismic waves from the subsurface layers, processes these seismic data and lastly infers oil and gas existence. All these steps are associated with a physical parameter: velocity. Nowadays, this parameter is estimated via a so-called seismic waveform inversion method, which is to derive a subsurface velocity model of the Earth by inverting seismic data observed at the surface. Besides this traditional physics-guided full waveform inversion, recently, more attention has paid on data-driven inversion techniques, mainly deep learning based pre-stack inversion, which builds a nonlinear mapping from a multi-shot record to a velocity profile for two-dimensional situation. Usually, a multi-shot record is widely simulated as a multiple channel color image. However, the multiple shots are located at the distinct surface positions, which is different from the fixed camera position of images, and whose shot gathers include less complementary and much redundant information. In order to characterize this situation, we regard each shot gather as a view in machine learning and propose an enhanced multi-view convolutional neural network for regression (MVCNNR) to velocity inversion, in this paper. Our MVCNNR generalizes multi-view convolutional neural network for 3D shape recognition into a pixel-level regression network that further is added both addition connections in residual network and skipping concatenation connections in U-net, to improve its performance. Experiments on four types of velocity datasets (Layered, Faulted, SaltBody, and SaltDome) show that our MVCNNR is superior to two representative data-driven techniques (FCNVMB and VelocityGAN), via quantitative evaluation and visualization analysis.

关键词： velocity inversion multi-view convolutional neural network concatenation connection U-net addition connection residual network

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DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第5期20卷 9530-9571页

作者： Muniyappan, Saranya Rayan, Arockia Xavier Annie Varrieth, Geetha Thekkumpurath Anna Univ CEG Campus Comp Sci & Engn Chennai Tamil Nadu India

Motivation: In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). Methods: In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. Results: The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.

关键词： drug-target interaction prediction similarity network integration information entropy-based random walk multi-view convolutional neural network meta-graph-based representation learning graph neural network

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Accurate classification of power quality disturbance based on 3D visualized spiral curve and hybrid ER-MVCNN model

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MEASUREMENT 2024年 231卷

作者： Song, Juncai Wu, Jing Wang, Xiaoqing Duan, Zhangling Wang, Xiaoxian Lu, Siliang Anhui Univ Sch Internet Hefei 230039 Anhui Peoples R China Anhui Univ Sch Elect & Informat Engn Hefei 230601 Anhui Peoples R China Anhui Univ Sch Elect Engn & Automat Hefei 230601 Anhui Peoples R China

This work proposes a new method for accurate power quality disturbance (PQD) classification. Firstly, 3D-visualized spiral curve (3D-VSC) is innovatively proposed to convert 1D PQD signal into 3D spiral curve. It can display time-frequency information in multi-dimensional perspectives and the disturbance characteristics more intuitively in image visual sense, which can realize PQD features enhancement. Secondly, efficient channel attention-ResNet multi-view convolution neural network (ER-MVCNN) hybrid model is creatively proposed by introducing ECA-ResNet as feature extraction module. It can solve limitations of multi-view convolution neural network (MVCNN) in single-view feature extraction and defects of multi-view feature fusion in the weighting problem, which can effectively achieve classification accuracy to 99.68 %. Comparison experiments with MVCNN, AlexNet-MVCNN, VGGNet-MVCNN, ResNet18-MVCNN, and SENet-ResNet-MVCNN show that ERMVCNN has higher accuracy by 12.4 %, 6.79 %, 6.14 %, 3.86 %, and 0.88 % respectively. Finally, through establishing PQD experiment platform and IEEE PES datasets to verify high accuracy and robustness of proposed method.

关键词： Power quality disturbance 3D-visualized spiral curve multi-view convolutional neural network (MVCNN) Efficient channel attention-ResNet multi-view convolution neural network (ER-MVCNN) Recognition and classification

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multi-modality and multi-view 2D CNN to Predict Locoregional Recurrence in Head & Neck Cancer

Multi-modality and Multi-view 2D CNN to Predict Locoregional...

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International Joint Conference on neural networks (IJCNN)

作者： Guo, Jinkun Wang, Rongfang Zhou, Zhiguo Wang, Kai Xu, Rongbin Wang, Jing Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China Univ Cent Missouri Sch Comp Sci & Math Warrensburg MO 64093 USA Univ Texas Southwestern Med Ctr Dallas Adv Imaging & Informat Radiat Therapy AIRT Lab Dallas TX 75390 USA Putian Univ Sch Informat Engn Engn Res Ctr Big Data Applicat Private Hlth Med Putian 351100 Peoples R China

ISBN: (纸本)9780738133669

Locoregional recurrence (LRR) remains one of leading causes in head and neck (H&N) cancer treatment failure despite the advancement of multidisciplinary management. Accurately predicting LRR in early stage can help physicians make an optimal personalized treatment strategy. In this study, we propose an end-to-end multi-modality and multi-view convolutional neural network model (mMmV-CNN) for LRR prediction in H&N cancer. In mMmV, a dimension reduction operator is designed, projecting the 3D volume onto 2D images in different directions, and a multi-view strategy is used to replace the original 3D method, which reduces the complexity of the algorithm while preserving important 3D information. Meanwhile, multi-modal data is used for the classification by making full use of the complementary information from cross modality data. Furthermore, we design a multi-modality deep neural network which is trained in an end-to-end manner and jointly optimize the deep features of CT, PET and clinical features. A H&N dataset which consists of 206 patients was used to evaluate the performance. Experimental results demonstrated that mMmV-CNN can obtain an AUC value of 0.81 and outperform a state of the art CNN-based method.

关键词： head and neck cancer multi-view convolutional neural network local recurrence outcome prediction

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multi-view deep learning for rigid gas permeable lens base curve fitting based on Pentacam images

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2020年第7期58卷 1467-1482页

作者： Hashemi, Sara Veisi, Hadi Jafarzadehpur, Ebrahim Rahmani, Rouhollah Heshmati, Zainabolhoda Univ Tehran Fac New Sci & Technol Tehran Iran Iran Univ Med Sci Sch Rehabil Sci Dept Optometry Tehran Iran Digikala Co Tehran Iran

Many studies in the rigid gas permeable (RGP) lens fitting field have focused on providing the best fit for patients with irregular astigmatism, a challenging issue. Despite the ease and accuracy of fitting in the current fitting methods, no studies have provided a high-pace solution with the final best fit to assist experts. This work presents a deep learning solution for identifying features in Pentacam four refractive maps and RGP base curve identification. An authentic dataset of 247 samples of Pentacam four refractive maps was gathered, providing a multi-view image of the corneal structure. Scratch-based convolutional neural network (CNN) architectures and well-known CNN architectures such as AlexNet, GoogLeNet, and ResNet have been used to extract features and transfer learning. Features are aggregated through a fusion technique. Based on a comparison of means square error (MSE) of normalized labels, the multi-view scratch-based CNN provided R-squared of 0.849, 0.846, 0.835, and 0.834 followed by GoogLeNet, comparable with current methods. Transfer learning outperforms various scratch-based CNN models, through which proper specifications some scratch-based models were able to increase coefficient of determinations. CNNs on multi-view Pentacam images have enabled fast detection of the RGP lens base curve, higher patient satisfaction, and reduced chair time.

关键词： Transfer learning multi-view convolutional neural network Image analysis RGP lens base curve fitting

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