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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： You Xin Liu Chen Yang Yang School of Information North China University of Technology Beijing China Beijing Key Laboratory of Large-scale Stream Data Integration and Analysis Technology Beijing China Beijing Huaxia Qixin Technology Co. Ltd China

There are many types of documents in fossil-fuel power station to describe equipment failures, including maintenance records, treatment diagnosis suggestions, historical cases, and equipment knowledge. The knowledge of equipment anomaly diagnosis and handling is scattered in different documents. Extract and fuse scattered knowledge from these scattered documents to generate a knowledge graph for equipment fault di-agnosis, providing necessary decision support for maintenance personnel to discover and handle equipment faults. This article proposes an implementation method for extracting and integrating equipment fault knowledge from diverse and multi type text records to form a knowledge graph. A thermal power plant equipment fault Q&A system based on the fusion of open source large language models and knowledge graphs has been developed. Main contributions: (1) A knowledge extraction algorithm integrating BERT-WWM model and pointer annotation method is proposed to extract entity relations of fault text jointly. Experiments show that the method performs well in extracting overlapped triples, and F1 is improved by 8.51 % compared with existing algorithms; (2) A knowledge fusion model based on RoBERTa-BiLSTM is proposed, which fully utilizes the feature information of the entity text to be disambiguated and the entity mention text, and cap-tures the interdependent features within the sentence through attention mechanism. The experiment shows that this method improves F1 by 9.56% compared to existing fusion algorithms. (3) Based on the open-source large model ChatGLM, a fusion method of knowledge graph and large model ChatGLM was explored, and a device fault question answering system for thermal power plants was implemented, achieving high accuracy in practical applications.

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T2I-CycleGAN: A CycleGAN for Maritime Road Network Extraction from Crowdsourcing Spatio-Temporal AIS Trajectory data 16th

T2I-CycleGAN: A CycleGAN for Maritime Road Network Extractio...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Yang, Xuankai Wang, Guiling Yan, Jiahao Gao, Jing School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

ISBN: (纸本)9783030675394

Maritime road network is composed of detailed maritime routes and is vital in many applications such as threats detection, traffic control. However, the vessel trajectory data, or Automatic Identification System (AIS) data, are usually large in scale and collected with different sampling rates. And, what’s more, it is difficult to obtain enough accurate road networks as paired training datasets. It is a huge challenge to extract a complete maritime road network from such data that matches the actual route of the ship. In order to solve these problems, this paper proposes an unsupervised learning-based maritime road network extraction model T2I-CycleGAN based on CycleGAN. The method translates trajectory data into unpaired input samples for model training, and adds dense layer to the CycleGAN model to handle trajectories with different sampling rates. We evaluate the approach on real-world AIS datasets in various areas and compare the extracted results with the real ship coordinate data in terms of connectivity and details, achieving effectiveness beyond the most related work. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Unsupervised learning

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Unpaired Learning of Roadway-Level Traffic Paths from Trajectories 17th

Unpaired Learning of Roadway-Level Traffic Paths from Traj...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Jia, Weixing Wang, Guiling Yang, Xuankai Zhang, Fengquan School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing100144 China

ISBN: (纸本)9783030926342

Traffic path data can be used as the basis for traffic monitoring and other technologies, which is essential for developing traffic-related technologies. Traditional methods of traffic path data extraction can no longer meet the needs because they cannot solve the problem of lacking standard benchmark data that may exist in the traffic field. Deep learning-based path extraction methods using large-scale data are a class of promising approaches. However, most of the deep learning-based path extraction methods are supervised and rely on paired training data. This paper proposes an unpaired learning method for fine-grained roadway-level paths from trajectory data based on CycleGAN. The method constructs spatio-temporal features based on HSV color space from trajectories which can enhance the model’s ability to recognize the roadway details. It transforms the features using convolutional layers, which can preserve the spatio-temporal information of the features, thus making the extraction results more accurate. We conduct experiments using urban and maritime traffic trajectory data and compare the proposed method with the state-of-the-art methods. The results of our model have more roadway level details, higher precision and F1 score than the other existing unsupervised traffic path learning methods. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Trajectories

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H-MGSR: A Hierarchical Motif-based Graph Attention Neural Network for Service Recommendation

H-MGSR: A Hierarchical Motif-based Graph Attention Neural Ne...

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IEEE International Conference on Web Services (ICWS)

作者： Xin Zheng Guiling Wang Jiayu Zhang Yuqi Zhang Nancy Wang Jian Yu Yanbo Han Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China School of Information Science and Technology North China University of Technology Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand

The rapid development of web services has made it increasingly challenging for developers to find desired web services. To address this issue, researchers have developed various powerful models for service recommender systems. Recently, graph neural networks have shown promising performance in various deep learning tasks including service recommendation. This paper proposes a novel graph neural network for web service recommendation using a hierarchical attention mechanism that combines a node-level and a motif-level attention mechanisms. The node-level attention mechanism is responsible for aggregating information by the importance of different neighbors, while the motif-level attention mechanism performs a weighted combination of the node embeddings generated from different motif adjacency matrices. Finally, the generated node embeddings are optimized by the multi-layer perceptron (MLP), which in turn provide recommendations. Experimental results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art approaches. Additionally, we conduct a model analysis to investigate the importance of different motifs. Overall, our proposed method shows promising performance for web service recommendation and highlights the potential of using graph neural networks in this domain.

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Spatial-Temporal Aware Business Event Forecasting for Proactive Services from IoT Sensory data

Spatial-Temporal Aware Business Event Forecasting for Proact...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jiayu Zhang Guiling Wang Yongpeng Shi Xin Zheng Jian Yu Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China School of Information Science and Technology North China University of Technology Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand

With the development of IoT and AI, better knowledge and information can be learned and extracted from IoT sensory data which enables business systems to proactively provide services to customers. This paper is the first study that attempts to forecast high-level business events from raw IoT sensory event data to improve the proactivity of services and applications. We propose a deep learning based business event forecasting framework, i.e., IoT2BE, which extracts prior knowledge to identify business events from IoT sensory data, extracts features in multi-views including the spatial and temporal view, generates spatio-temporal business event embeddings, and uses a seq2seq model with attention to predict the future business events. Extensive experiments are based on two datasets including one real-world maritime ship trajectory dataset and one publicly available raw sensor dataset from a smart home environment. The results demonstrate that our framework can be effectively applied in various business scenarios.

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A Dynamic Binding Method for Situation-aware IoT Services Targeting Proactive BPM

A Dynamic Binding Method for Situation-aware IoT Services Ta...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiuxian Li Guiling Wang Yongpeng Shi Jian Yu Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China School of Information Science and Technology North China University of Technology Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand

By leveraging IoT data, Business Process Management (BPM) systems can sense the physical world situation and make more accurate decisions proactively, but the current BPM systems lack the effective method to take good use of IoT data. In this paper, a situation-aware dynamic binding method for IoT services targeting proactive BPM is proposed to meet this requirement. The method first establishes a prediction model to predict the bindable IoT services under given situations by constructing an encoder-decoder structure network model using bi-directional gated recurrent units (Bi-GRU) and incorporating an attention mechanism. Then our method creates a Dynamic IoT Service Task (DIT) model for BPM. This enables the BPM system to integrate the prediction model with dynamic switching of IoT services and a parallel running architecture for multiple IoT services. A prototype system is developed and a case study is conducted to evaluate the performance of the method. The study focuses on the safety supervision scenario for the transportation of hazardous Liquefied Natural Gas (LNG) by sea. Results show that the prediction model proposed in this paper outperforms other models on multiple indicators. Also, the case study and experimental results verify the effectiveness of the method.

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A GAN-Based Power Quality Anomaly Detection Method for Imbalanced Multivariate Time Series

A GAN-Based Power Quality Anomaly Detection Method for Imbal...

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IEEE International Conference on Computer and Communication Engineering Technology (CCET)

作者： Jiachen Huang Chen Liu Yang Yang Yunjia Liu Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China School of Computer Science. North China University of Technology Beijing China Huaxia Qixin Limited Company Beijing China

Time series anomaly detection is an important and well-researched topic with extensive applications in finance, healthcare, biological data, and industrial production. However, in the field of power quality, detecting anomalies in complex and imbalanced multivariate long time series data remains a challenge. This paper proposes a GAN-based method for multivariate time series anomaly detection, which directly trains on raw data captured by power monitoring stations. It addresses the issues of the lack of original examples, difficulty in acquisition, and simulation of abnormal data in power quality. Additionally, the proposed method introduces an attention mechanism and a multi-scale feature fusion mechanism, reducing the accumulation of errors during model training and effectively addressing the problem of overfitting due to the scarcity of abnormal samples and high noise in power quality data. The comprehensive experiments conducted on four original power quality datasets demonstrate that the proposed method significantly improves the performance of anomaly detection in the field of power quality compared to baseline methods.

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Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction

arXiv

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arXiv 2023年

作者： Ding, Weilong Zhang, Tianpu Wang, Jianwu Zhao, Zhuofeng The School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing100144 China Department of Information Systems University of Maryland Baltimore County United States

Inter-city highway transportation is significant for urban life. As one of the key functions in intelligent transportation system (ITS), traffic evaluation always plays significant role nowadays, and daily traffic flow prediction still faces challenges at network-wide toll stations. On the one hand, the data imbalance in practice among various locations deteriorates the performance of prediction. On the other hand, complex correlative spatiotemporal factors cannot be comprehensively employed in long-term duration. In this paper, a prediction method is proposed for daily traffic flow in highway domain through spatio-temporal deep learning. In our method, data normalization strategy is used to deal with data imbalance, due to long-tail distribution of traffic flow at network-wide toll stations. And then, based on graph convolutional network, we construct networks in distinct semantics to capture spatio-temporal features. Beside that, meteorology and calendar features are used by our model in the full connection stage to extra external characteristics of traffic flow. By extensive experiments and case studies in one Chinese provincial highway, our method shows clear improvement in predictive accuracy than baselines and practical benefits in business. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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MHPS: Multimodality-Guided Hierarchical Policy Search for Knowledge Graph Reasoning

MHPS: Multimodality-Guided Hierarchical Policy Search for Kn...

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

作者： Chen Gao Xugong Qin Peng Zhang Yongquan He Xinjian Huang Ming Zhou Liehuang Zhu Qingfeng Tan School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China Meituan Bejing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

Recently, path inference-based knowledge graph reasoning (KGR) methods have attracted great attention due to their good performance and interpretability. However, as the number of hops increases, the search space grows exponentially, making the reward sparse and the process of reasoning difficult. To alleviate this problem, we propose the Multimodality-guided Hierarchical Policy Search (MHPS) for KGR, which introduces multimodal hierarchical guidance to each layer of policies during policy search. On the one hand, multimodal guidance reserves rich information on different dimensions, providing more opportunities to find better paths. On the other hand, this leads to better interaction between the two agents, resulting in more concise guidance for policy stepping. Experimental results on two public datasets demonstrate that the proposed approach outperforms state-of-the-art methods on multi-hop KGR.

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A Dual-Level Graph Attention Network and Transformer for Enhanced Trajectory Prediction Under Road Network Constraints

SSRN

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SSRN 2024年

作者： Zhang, Jiayu Guo, Lucas Wang, Guiling Yu, Jian Zheng, Xin Mei, Yusheng Han, Boyang Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China School of Information Science and Technology North China University of Technology Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand Southwest Jiaotong University Chengdu China JD Intelligent Cities Research

Predicting vehicle future trajectories enables various Location-Based Services (LBS), including traffic flow monitoring, vehicle route planning, etc. Vehicles are constrained to predefined road networks. Their traveling behaviors are influenced by both their own attributes and road network characteristics hidden in other vehicles' travel histories. Most of the existing methods fail to sufficiently leverage such collaborative trajectory data under road network *** paper proposes a novel model, called HGT-RN (Hierarchical Graph Attention Network and Transformer Networks for Enhanced Trajectory Prediction under Road Network Constraints), which exploits all vehicles' trajectories and integrates road network constraints. Specifically, HGT-RN creates a dual-level Graph Attention Network (GAT) based on global traffic flow and local individual trajectory graph to learn trajectory embeddings: (i) Weighted-directional global traffic flow graph and Weighted-Directional GAT to learn the global-level trajectory embedding by aggregating the neighbors' embeddings based on the spatial transitional relationships among road intersections over all vehicles' trajectories, considering the preference of current trajectory;and (ii) Local individual trajectory graph attention network to learn the local-level trajectory embedding by modeling the transitions within the current trajectory at each intersection. When incorporating the trajectory embeddings into a transformer model for future trajectory prediction, a road network topology-aware loss function is designed to improve accuracy. Extensive experiments conducted on three city-scale real-world taxi trajectory datasets demonstrate that HGT-RN significantly outperforms existing baseline models. The code is available at https://***/zjy9826/HGT-RN. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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