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检索条件"主题词=Graph Convolutional Neural Network"

共 408 条记录，以下是51-60 订阅

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Production Scheduling based on Deep Reinforcement Learning using graph convolutional neural network 12

Production Scheduling based on Deep Reinforcement Learning u...

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12th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Seito, Takanari Munakata, Satoshi Hitachi Solut East Japan Ltd Tokyo Japan

ISBN: (纸本)9789897583957

While meeting frequently changing market needs, manufacturers are faced with the challenge of planning production schedules that achieve high overall performance of the factory and fulfil the high fill rate constraint on shop floors. Considerable skill is required to perform the onerous task of formulating a dispatching rule that achieves both goals simultaneously. To create a useful rule independent of human expertise, deep reinforcement learning based on deep neural networks (DNN) can be employed. However, conventional DNNs cannot learn the important features needed for meeting both requirements because they are unable to process qualitative information included in these schedules, such as the order of operations in each resource and correspondence between allocated operations and resources. In this paper, we propose a new DNN model that can extract features from both numeric and nonnumeric information using a graph convolutional neural network (GCNN). This is done by applying schedules as directed graphs, where numeric and nonnumeric information are represented as attributes of nodes and directed edges, respectively. The GCNN transforms both types of information into the feature values by transmitting and convoluting the attributes of each component on a directed graph. Our investigation shows that the proposed model outperforms the conventional one.

关键词： Production Scheduling Deep Reinforcement Learning graph convolutional neural network

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Text classification problems via BERT embedding method and graph convolutional neural network

Text classification problems via BERT embedding method and g...

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International Conference on Advanced Technologies for Communications (ATC)

作者： Loc Tran Lam Pham Tuan Tran An Mai Paris Saclay Univ EPHE CHArt Lab Paris France Univ Informat Technol HCMC Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Int Univ Dept Comp Sci & Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781665433792

This paper presents a hybrid technique of combining the BERT embedding method and the graph convolutional neural network. This combination is then employed to solve the text classification problem. Initially, we apply the BERT embedding method to the whole corpus in order to transform all the texts into numerical vectors. Then, the graph convolutional neural network will be applied to these numerical vectors to classify these texts into their appropriate classes. Especially, in our approach, we need only a few labeled texts for the model training. For the illustration, in this paper, we use the BBC news and the IMDB movie reviews datasets to perform our experiments, showing that the performance of the graph convolutional neural network model is better than the performances of the combination of the BERT embedding method with other classical machine learning models.

关键词： text classification graph convolutional neural network BERT classical machine learning model

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Identifying Biomarkers of Subjective Cognitive Decline Using graph convolutional neural network for fMRI Analysis 19

Identifying Biomarkers of Subjective Cognitive Decline Using...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhang, Zhao Li, Guangfei Niu, Jiaxi Du, Sihui Gao, Tianxin Liu, Weifeng Jiang, Zhenqi Tang, Xiaoying Xu, Yong Beijing Inst Technol Dept Biomed Engn 5 South Zhongguancun St Beijing Peoples R China Chinese Peoples Liberat Army Gen Hosp Dept Cardiol 28 Fuxing Rd Beijing Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease (AD). People with SCD have a higher chance of developing mild cognitive impairment and AD than those aging normally. In the present study, we collected resting state functional magnetic resonance imaging (rsfMRI) data for 69 patients with SCD and 75 normal controls (NC);using statistical analysis, a support vector machine (SVM), and graph convolutional neural networks (GCNs), we examined the brain-related differences between patients with SCD and NC. Clinical scale scores show the best distinguishing ability between patients with SCD and NC, and we further used the two-sample ttest, SVM, and GCN model with an attention mechanism to obtain the top 10 brain regions contributing to performance on recognition tasks. The results showed that the thalamus, and cingulum in the Anatomical Automatic Labeling template showed significant differences between patients with SCD and NC. We further discussed the roles of these identified brain regions in the diagnosis of SCD and AD. Our research thus provided statistical evidence that can aid in identifying early-stage AD.

关键词： Alzheimer's disease Subjective cognitive decline graph convolutional neural network

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Research Proposal: Light Field Image Quality Assessment Method based on Deep graph convolutional neural network 13

Research Proposal: Light Field Image Quality Assessment Meth...

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13th ACM Multimedia Systems Conference (MMSys)

作者： Alamgeer, Sana Irshad, Muhammad Farias, Mylene C. Q. Univ Brasilia Dept Elect Engn Brasilia DF Brazil

ISBN: (纸本)9781450392839

This paper contains the research proposal of Sana Alamgeer that was presented at the MMSys 2022 doctoral symposium. Unlike regular images that represent only light intensities, Light Field (LF) contents carry information about the intensity of light in a scene, including the direction light rays are traveling in space. This allows for a richer representation of our world, but requires large amounts of data that need to be processed and compressed before being transmitted to the viewer. Since these techniques may introduce distortions, the design of Light Field Image Quality Assessment (LF-IQA) methods is important. The majority of LF-IQA methods based on traditional convolutional neural network (CNN) have limitations, i.e. they are unable to increase the receptive field of a neuron-pixel to model non-local image features. In this work, we propose a novel no-reference LF-IQA method that is based on Deep graph convolutional neural network (GCNN). Our method not only takes into account both LF angular and spatial information, but also learns the order of pixel information. Specifically, the method is composed of one input layer that takes a pair of graphs and their corresponding subjective quality scores as labels, 4 GCNN layers, fully connected layers, and a regression block for quality prediction. Our aim is to develop the quality prediction method with maximum accuracy for distorted LF content.

关键词： 4D Light Field Image Quality Assessment graph convolutional neural network Epipolar Plane Images

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A graph convolutional neural network Model for Trajectory Prediction 13

A Graph Convolutional Neural Network Model for Trajectory Pr...

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13th International Conference on graphics and Image Processing (ICGIP)

作者： Di, Zichao Zhou, Yue Chen, Kun Chen, Zongzhi Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China

This paper proposes a joint trajectory prediction algorithm based on graph neural networks and map scene information assistance. And proposed a kernel function to calculate the influence between different targets in t... 详细信息

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

关键词： graph convolutional neural network Kernel function Rasterization Mobilenet

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Measuring Decision Confidence Levels from EEG Using a Spectral-Spatial-Temporal Adaptive graph convolutional neural network 29th

Measuring Decision Confidence Levels from EEG Using a Spectr...

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29th International Conference on neural Information Processing

作者： Li, Rui Wang, Yiting Lu, Bao-Liang Shanghai Jiao Tong Univ Ctr Brain Comp & Machine Intelligence Dept Comp Sci & Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ RuiJin Hosp Clin Neurosci Ctr RuiJin Mihoyo LabSch Med 197 RuiJin 2nd Rd Shanghai 200020 Peoples R China

ISBN: (纸本)9789819916412;9789819916429

Decision confidence can reflect the correctness of people's decisions to some extent. To measure the reliability of human decisions in an objective way, we introduce a spectral-spatial-temporal adaptive graph convolutional neural network (SST-AGCN) for recognizing decision confidence levels based on EEG signals in this paper. The advantage of our proposed method is that it fully utilizes the knowledge from the spectral, spatial, and temporal dimensions of the EEG signals. The experiments based on a confidence text exam task within limited time are designed and conducted. The experimental results demonstrate that the SST-AGCN enhances the performance compared with the models without using the spatial or temporal information for classifying five decision confidence levels, achieving the average F1-score of 57.92% and the average accuracy of 58.16%. As for the two extreme confidence levels, the average F1-score reaches to 93.17% with the average accuracy of 94.11%. Furthermore, the neural patterns of decision confidence are analyzed in this paper through the brain topographic maps and the learned functional connectivities by the SST-AGCN. The experimental results indicate that the delta, theta and alpha bands may be critical in measuring human decision confidence levels with better recognition performance than other frequency bands.

关键词： Decision confidence Electroencephalogram graph convolutional neural network Functional connectivity

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On the Design of Quantum graph convolutional neural network in the NISQ-Era and Beyond 40

On the Design of Quantum Graph Convolutional Neural Network ...

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IEEE 40th International Conference on Computer Design (ICCD)

作者： Hu, Zhirui Li, Jinyang Pan, Zhenyu Zhou, Shanglin Yang, Lei Ding, Caiwen Khan, Omer Geng, Tong Jiang, Weiwen George Mason Univ Fairfax VA 22030 USA Univ Rochester Rochester NY 14627 USA Univ Connecticut Storrs CT USA

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

The rapid growth in the size of graph convolutional neural networks (GCNs) encounters both computational- and memory-wall on classical computing platforms (e.g., CPU, GPU, FPGA, etc.). Quantum computing, on the other hand, provides extremely high parallelism for computation. Although quantum neural networks have been recently studied, the research on quantum graph neural networks is still in its infancy. The key challenge here is how to integrate both the graph topology information and the learning ability of GCNs into quantum circuits. In this work, we leverage the Givens rotations and its quantum implementation to encode graph information;in addition, we employ the widely used variational quantum circuit to bring the learnable parameters. On top of these, we present a full-quantum design of graph convolutional neural networks, namely "QuGCN", for semi-supervised learning on graph-structured data. Experiment results show our design is competitive with classical GCNs in terms of node classification accuracy on Cora sub-dataset. More importantly, we show the potential advantages that can be achieved by the proposed quantum GCN design when the number of features grows.

关键词： graph convolutional neural network Quantum circuit design Givens rotation NISQ

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A Robust graph convolutional neural network-Based Classifier for Automatic Modulation Recognition 18

A Robust Graph Convolutional Neural Network-Based Classifier...

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18th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Ghasemzadeh, Pejman Hempel, Michael Sharif, Hamid Univ Nebraska Lincoln Dept Elect & Comp Engn Adv Telecommun Engn Lab TEL Lincoln NE 68588 USA

ISBN: (纸本)9781665467490

The procedure of automatically recognizing the modulation scheme of the received signal without any knowledge of the communications parameters employed by the transmitter has gained tremendous attention for developing various applications such as dynamic spectrum access in next generation communications systems and in electronic warfare (EW) applications. Amongst the proposed approaches for this procedure, the feature-based approach working on the principles of deep learning models has consistently demonstrated more favorable properties for its integration with real-world applications and realtime operations. However, this approach faces the challenges of low performance efficiency caused by low classification accuracy and high computational complexity in environments characterized by low signal-to-noise (SNR) ratio conditions. Therefore, in this paper, we present our research findings on a deep learning-based classifier for Automatic Modulation Recognition (AMR) with robust classification accuracy and lower computational complexity, thus leading to higher overall performance efficiency. The feature extraction stage of the proposed classifier operates on the translation of received signal constellations into a graph, which then follows a process of extracting graph information. Mapping the extracted information into features is implemented by a convolutional block. This design employed in our feature extraction stage is the main element for the robustness of our proposed classifier. For our performance evaluations we selected 8QAM, 64QAM and 256QAM, representing candidates for low-, medium- and high-order modulation schemes, respectively. Our simulation results exhibit higher classification accuracy compared to related literature works, by an average of 11 percentage points (p.p.), 11.31 p.p. and 9.3 p.p., respectively, for the successful classification of 8QAM, 64QAM and 256QAM across an SNR range from -10 to 30 dB. The computational complexity analysis indicates t

关键词： Automatic modulation recognition (AMR) graph convolutional neural network robust classification low execution latency efficient performance

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An Improved graph convolutional neural network based on graph Auto-encoder 16

An Improved Graph Convolutional Neural Network based on Grap...

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16th International Conference on Computer and Automation Engineering (ICCAE)

作者： Wang, Dongqi Du, Tianqi Liu, Zhongwu Chen, Dongming Ren, Tao Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9798350370058;9798350370164

graph convolutional neural networks (GCN) is a rapidly advancing deep learning algorithm for learning graph representations. One limitation of GCN is that it cannot guarantee optimal low-pass characteristics, thus struggling to effectively filter out high-frequency noises. In this study, we propose a GCN -based autoencoder that addresses this issue by incorporating Laplace-based filters for high-frequency noise reduction. To address potential overfitting caused by the mean-square error loss function used for reconstructing feature matrices, which is sensitive to the number of vector paradigms and dimensions, we introduce a cosine error loss function to mitigate this impact. Additionally, we employ a feature enhancement strategy during training. Through experiments conducted on three real-world datasets, we demonstrate that our proposed autoencoder clustering algorithm outperforms baseline graph representation learning algorithms in node clustering tasks. Furthermore, we assess the parameter sensitivity of our algorithm through extensive experiments.

关键词： Complex network graph Autoencoder graph Clustering graph convolutional neural network

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Intermittent Fault Diagnosis Method Based On graph convolutional neural network 14

Intermittent Fault Diagnosis Method Based On Graph Convoluti...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Shi, Junyou Zhou, Huidong Yang, Zhilin School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798350301359

Intermittent faults are widespread in various systems, which often greatly reduce the mean time to failure of the system, lead to the replacement of normal components, and increase unnecessary maintenance and support costs. The research of intermittent fault diagnosis methods is of great significance for improving equipment reliability, reducing system maintenance and support costs, and improving the level of intelligent manufacturing. At present, various machine learning methods have received more and more attention in the field of fault diagnosis, but the research on intermittent fault diagnosis based on graph convolutional neural network is less carried out. Compared with convolutional neural networks, graph convolutional neural networks are good at processing graph data which is more widely existing. Based on this, this paper decides to construct an intermittent fault diagnosis model based on graph convolutional neural network for intermittent fault localization. This paper proposes a method to convert the intermittent fault voltage waveform data into graph data. The original waveform is decomposed into six sub-waveforms by wavelet decomposition, and the edge connection is defined by the Pearson correlation coefficient between the sub-waveforms. On this basis, six different graph convolutional network models are built, the network is trained and optimized, and the accuracy of 90.38% is obtained on the test set. © 2023 IEEE.

关键词： fault diagnosis graph convolutional neural network Intermittent fault Wavelet Decomposition

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