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Explore augmented reality and deep feature learning to visual field operation and maintenance for electric power equipment

Explore augmented reality and deep feature learning to visua...

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2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Yuan, Qi Feng, Jianfeng Yan, Nannan Xue, Xiangyang Shen, Bin Yang, Yongcheng Liu, Ziqi Zhou, Yunjie Yuan, Jialiang State Grid Shanghai Electric Power Company Shanghai200122 China School of Big Data Institute of Brain-Like Intelligence Science and Technology Fudan University Shanghai200433 China Institute of Brain and Intelligence Science and Technology School of Computer Science Fudan University Shanghai200433 China

ISBN: (纸本)9781510682597

This work mainly studies the visual field operation and maintenance method based on human-computer interaction, and establishes the field operation and maintenance management module through the deep image feature analysis method to realize the operation and maintenance function of augmented reality (AR) intelligent devices. For the on-site electricity inspection, the visual image feature mining model based on deep convolutional neural network is established. The basic information, operation condition, box condition, etc. of the field collection and metering equipment for regular inspection needs to be recorded and uploaded to the deep network model for analysis. The experimental results show that the proposed method has high efficiency in equipment operation and maintenance. © 2024 SPIE.

关键词： Inspection equipment

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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K-ON: Stacking Knowledge On the Head Layer of Large Language Model 39

K-ON: Stacking Knowledge On the Head Layer of Large Language...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Lingbing Zhang, Yichi Bo, Zhongpu Chen, Zhuo Sun, Mengshu Zhang, Zhiqiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

ISBN: (纸本)157735897X

Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next token, aligning well with many NLP tasks. However, in knowledge graph (KG) scenarios, entities are the fundamental units and identifying an entity requires at least several tokens. This leads to a granularity mismatch between KGs and natural languages. To address this issue, we propose K-ON, which integrates KG knowledge into the LLM by employing multiple head layers for next k-step prediction. K-ON can not only generate entity-level results in one step, but also enables contrastive loss against entities, which is the most powerful tool in KG representation learning. Experimental results show that K-ON outperforms state-of-the-art methods that incorporate text and even the other modalities. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Graph classification based fault detection in nuclear power plants with graph formulation 10

Graph classification based fault detection in nuclear power ...

引用

10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Qu, Yi Xue, Xiaodong Shi, Yong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska OmahaNE68182 United States

In the routine production and operation of nuclear power plants (NPPs), safety management is extremely important, and consequently, a fault detection system yielding high accuracy could provide effective decision support and prevent the accidents to the maximum extent. Most of existing studies are based on the analysis of time-series data, while researches oriented to the implementation of graph data mining are barely seen. In this paper, we propose an improved fault detection method to address this task from the perspective of graph representation learning. By calculating the similarity between indicators, the multivariate temporal sequences of the running characteristics of NPPs have been formulated as a group of graphs with corresponding labels, on which the graph convolutional network (GCN) has been introduced to detect fault samples in the way of graph classification. Compared to conventional supervised learning classifiers, our proposed methodology performs more superior identification of major faults, and the sensitivity regarding significant parameter or settings in the model design has been analyzed to investigate their optimal arrangements. The findings in this article not only present the promising prospects of introducing graph learning techniques into safety management, also indicate that various kinds of digital signals could be reformulated into graph dataset based on which the original task could be executed more effectively in the way of graph representation learning. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative Management.

关键词： Nuclear power plants

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A Polynomial-Time Algorithm for MCS Partial Search Order on Chordal Graphs 48

A Polynomial-Time Algorithm for MCS Partial Search Order on ...

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48th International Symposium on Mathematical Foundations of Computer science, MFCS 2023

作者： Rong, Guozhen Yang, Yongjie Li, Wenjun Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology China Chair of Economic Theory Saarland University Saarbrücken Germany

ISBN: (纸本)9783959772921

We study the partial search order problem (PSOP) proposed recently by Scheffler [WG 2022]. Given a graph G together with a partial order on the set of vertices of G, this problem determines if there is an S-ordering that is consistent with the given partial order, where S is a graph search paradigm like BFS, DFS, etc. This problem naturally generalizes the end-vertex problem which has received much attention over the past few years. It also generalizes the so-called F-tree recognition problem which has just been studied in the literature recently. Our main contribution is a polynomial-time dynamic programming algorithm for the PSOP of the maximum cardinality search (MCS) restricted to chordal graphs. This resolves one of the most intriguing open questions left in the work of Scheffler [WG 2022]. To obtain our result, we propose the notion of layer structure and study numerous related structural properties which might be of independent interest. © Guozhen Rong, Yongjie Yang, and Wenjun Li

关键词： Dynamic programming

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CHSTS: Chameleon Hash based on Schnorr threshold signature on Editable Blockchain

CHSTS: Chameleon Hash based on Schnorr threshold signature o...

引用

2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and big data Computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Yuan, Linlin Yang, Yuxiang Tan, Chaoyue Dou, Hui Luo, Yun Chen, Yuling College of Information Engineering Guizhou Open University Guiyang China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798350304602

Blockchain's immutability, while a core feature, can pose challenges in cases involving sensitive information or compliance with legal regulations, hindering its development. Many subsequent works designed editable blockchains based on the chameleon hash function proposed by H. Krawczyk et al. to solve the above problems. However, existing editable blockchain solutions lack security and are inefficient. To address the security and efficiency issues, we introduce the novel concept of Chameleon hashing based on Schnorr threshold signatures (CHSTS). The CHSTS scheme has double trapdoors, the publisher of the message owns the long-term trapdoor, and the editorial committee owns the ephemeral trapdoor. Message publishers can collaborate with the editorial committee to modify their published information, preventing the exposure of private data, facilitating oversight during the editing process, and enabling the tracking of malicious users. We have designed an updatable ephemeral trapdoor based on Schnorr signatures, effectively avoiding the inefficiencies associated with bilinear pairing. Experimental results show that our CHSTS scheme has a significant advantage in computational cost. © 2023 IEEE.

关键词： Blockchain

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Spatio-temporal analysis of power demand in smart grid based on stacked autoencoder 10

Spatio-temporal analysis of power demand in smart grid based...

引用

10th International Conference on Energy Materials and Electrical Engineering, ICEMEE 2024

作者： Li, Hai Feng, Jianfeng Gu, Huangjing Xue, Xiangyang Yan, Nannan Cao, Yu Lv, Haosheng Wang, Xiaodi Yang, Tianyu State Grid Shanghai Electric Power Company Shanghai200122 China School of Big Data Institute of Brain-Like Intelligence Science and Technology Fudan University Shanghai200433 China Institute of Brain and Intelligence Science and Technology School of Computer Science Fudan University Shanghai200433 China

ISBN: (纸本)9781510686243

Energy demand in modern cities is becoming more and more complex. Knowing its pattern can help managers make proper decisions. However, finding the pattern of large-scale spatial coverage fields, such as a whole city, is not easy. In this paper, a data-driven stacked autoencoder-based framework is proposed to capture the spatio-temporal pattern for electricity consumption. The data can be organized as a one-time series and the geography coverage of the analysis can be determined by the collected data. An experiment with collected data proves the feasibility of the framework. © 2024 SPIE.

关键词： Time series

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MobileViT-based classification of Alzheimer's disease 6

MobileViT-based classification of Alzheimer's disease

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Li, Yanmei Tang, Jinghong Li, Lihong Wang, Xingyu Ding, Weiwu Li, Xiaoshuang Yu, Tao Wu, Xia Chongqing University of Technology School of Artificial Intelligence Chongqing China Chengdu Aerospace Science and Industry Big Data Research Institute Co. Ltd. Chengdu China

ISBN: (纸本)9798350325485

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the detection and discovery of early AD is still difficult due to the insidious nature of its pathogenesis. With the rapid development of deep learning nowadays, Transformer has been commonly used in the field of computer vision and the results are very good. Therefore, in this paper, we propose a MobileViT style model BrMobile for AD diagnosis using 1.5T T1-weighted baseline magnetic resonance images (MRI). The model combines the features of the Transformer and Self-Attention mechanisms and was tested on the ADNI dataset, achieving excellent accuracy. The accuracy of the method was 96.7%, 91.8% and 90.9% for the classification of AD with NC, MCI with NC and AD with MCI, respectively. In addition to these dichotomous tasks we also tested multiclassification AD vs. NC vs. MCI with a classification accuracy of 88.8%. Compared to other state-of-The-Art studies, this method provides an effective and accurate diagnosis in AD classification due to the light weight and efficiency of the model. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation 39

Tokenization, Fusion, and Augmentation: Towards Fine-grained...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yichi Chen, Zhuo Guo, Lingbing Xu, Yajing Hu, Binbin Liu, Ziqi Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

ISBN: (纸本)157735897X

Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC methods usually extract multi-modal features with pre-trained models, resulting in coarse handling of multi-modal entity information, overlooking the nuanced, fine-grained semantic details and their complex interactions. To tackle this shortfall, we introduce a novel framework MYGO to tokenize, fuse, and augment the fine-grained multi-modal representations of entities and enhance the MMKGC performance. Motivated by the tokenization technology, MYGO tokenizes multi-modal entity information as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MYGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 19 of the latest models, underlining its superior performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Collaborative List-and-Pairwise Filtering from Implicit Feedback : Extended Abstract 39

Collaborative List-and-Pairwise Filtering from Implicit Feed...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Yu, Runlong Liu, Qi Ye, Yuyang Cheng, Mingyue Chen, Enhong Ma, Jianhui University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology Hefei China Rutgers University Management Science and Information Systems Rutgers Business School Newark United States

ISBN: (纸本)9798350322279

Collaborative filtering (CF) from implicit datasets has attracted much attention in recent years. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proven to be helpful to exploit implicit feedback, but lead to not addressing the rank-biased scenarios where positive items are supposed to be placed on the top-k positions. Although there exist listwise methods, they have low efficiency and are not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we first smooth a well-known rank-biased measure called Mean Average Precision (MAP) as a low-bound version to make it can be optimized. After that, we combined the objective functions of optimizing the MAP with pairwise comparisons. The CLAPF framework is a new hybrid model that provides the idea of utilizing a listwise measure in a pairwise way on implicit feedback. © 2023 IEEE.

关键词： Collaborative filtering

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