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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Wang, Jiahui Yue, Kun Duan, Liang Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9781665492324

Extraction of relations among entities from texts is critical for domain knowledge representation. In this paper, an association graph was constructed to represent the dependencies among entities and relations, upon which a relation extraction method was proposed to predict the relations in domain texts. Experimental results on various annotated domain datasets demonstrate that the recall of our proposed method outperforms the other relation extraction models. © 2022 IEEE.

关键词： Extraction

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Revocable and certificateless public auditing for cloud storage

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Science China(information Sciences) 2020年第10期63卷 282-284页

作者： Yinghui ZHANG Tiantian ZHANG Shengmin XU Guowen XU Dong ZHENG School of Cyberspace Security Xi'an University of Posts and Telecommunications Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications Secure Mobile Centre School of Information SystemsSingapore Management University School of Computer Science and Engineering University of Electronic Science and Technology of China

Dear editor,Plenty of computing and storage resources in the cloud are provided for users with restricted computing and storage resources,which has attracted the attention of many researchers[1,2].A generic blockchain-based cloud data auditing scheme[3] is proposed,which is compatible with any blockchains including the bitcoin *** the data integrity checking scheme,certificate-

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Investigating the Performance of ECA-YOLOv5n Model for Appearance Quality Detection in Air Conditioner External Units 18

Investigating the Performance of ECA-YOLOv5n Model for Appea...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Guo, Zihao Li, Dongyang Yuan, Dongfeng University of Melbourne School of Computing and Information Systems Melbourne Australia Shandong University Shandong Key Laboratory of Wireless Communication Technologies Jinan China

ISBN: (纸本)9798350312201

In an era of rapid technological advancements, keeping up with the iterative updates of factory technology in Intelligent manufacturing presents a daunting challenge. Against this backdrop, this paper investigates the performance of the ECA-YOLOv5n model, a novel approach for appearance quality detection in air conditioner external units. Building upon an open-source dataset, ECA-YOLOv5n model integrates the ECA-Net attention mechanism into the YOLOv5-nano model. ECA-Net effectively captures channel-wise dependencies and studies discriminative features, allowing for seamless integration into the convolutional neural networks of the YOLOv5 architecture without the need for extensive modifications. Furthermore, the efficient channel attention mechanism of ECA-Net demands fewer parameters and computations in comparison to other attention mechanisms, conferring a significant advantage in terms of computational efficiency. Experimental results show that the newly proposed model achieves approximately 98% precision, 99% recall, and 99% mAP on the dataset. The ECA-YOLOv5n model reduces storage usage and minorly increases detection speed in comparison to other YOLO models. © 2023 IEEE.

关键词： Object detection

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Temperature Control Method for Batch Reactors Based on Adaptive Delay Compensation

Temperature Control Method for Batch Reactors Based on Adapt...

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2024 China Automation Congress, CAC 2024

作者： Mao, Dehui Li, Peng Ai, Mingxi Wu, Jiande Yang, Chuangyan Wang, Hancheng School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

To address the instability issue caused by time-delay and nonlinear characteristics of intermittent reactions in batch reactors, a temperature control method for batch reactors based on adaptive delay compensation was proposed. By integrating the chemical reaction mechanism inside the reactor with graphical analysis and actual production data, a time-varying second-order delayed transfer function model for the reactor is established. The adaptive rate for controlling the time-varying delayed system is designed based on the control error between the actual controlled object and the nominal model. Furthermore, standard Smith predictor is employed to compensate for the delay in the error term. When applied to temperature control in batch reactors, experimental results demonstrate that this method effectively reduces the temperature control errors caused by time-varying delays, significantly accelerates the convergence speed and diminishes the oscillation amplitude of the temperature curve. © 2024 IEEE.

关键词： Batch reactors

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Continuous Sub-prevalent Co-location Pattern Mining 4th

Continuous Sub-prevalent Co-location Pattern Mining

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4th International Conference on Spatial Data and Intelligence, SpatialDI 2023

作者： Wang, Qilong Chen, Hongmei Wang, Lizhen School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9783031329098

Spatial co-location pattern represents a subset of spatial features whose instances are frequently located together in space. Sub-prevalent co-location pattern mining discovers patterns with richer spatial relationships based on star instance model instead of clique instance model. Further, discovering spatiotemporal sub-prevalent co-location pattern is important to reveal the spatiotemporal interaction between spatial features and promote the application of patterns. However, the methods for mining spatiotemporal sub-prevalent co-location pattern measure the interestingness of patterns by the frequency of patterns in time slice set, and ignore the duration of patterns which is an important spatiotemproal information in patterns. Thus, this paper presents mining spatiotemporal sub-prevalent co-location pattern by considering the duration and the frequency of patterns. Specifically, a novel pattern, is proposed by defining the continuous sub-prevalent index. Then, an efficient algorithm is designed to mine the proposed patterns by utilizing the anti-monotonicity of continuous sub-prevalent index to prune unpromising patterns. Extensive experiments on synthetic and real datasets verify the practicability of the proposed patterns and the effectiveness of the proposed algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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Complex Industrial Process Monitoring Using Improved Dynamic Latent Variables - Neighborhood Preserving Embedding

Complex Industrial Process Monitoring Using Improved Dynamic...

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2024 China Automation Congress, CAC 2024

作者： Wang, Hancheng Li, Peng Ai, Mingxi Wu, Jiande Yang, Chuangyan Xu, Qingmin School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

In complex industrial process monitoring, prevalent challenges include high dimensionality, extensive redundancies, and dynamic characteristics. In this paper, we propose an Improved Dynamic Latent Variable-Neighborhood Preserving Embedding (DLV-NPE) algorithm specifically designed to tackle these issues. The methodology begins with the implementation of a variance-covariance feature selection method to minimize redundancies among monitoring variables. This is followed using a dynamic latent variable algorithm to extract dynamic regression properties from the selected variables. Subsequently, the NPE algorithm is employed to preserve the residuals of high-dimensional dynamic latent variables and the high-dimensional residuals of selected variables, thereby preserving the integrity of the low-dimensional manifold structure. Finally, Bayesian inference is applied to develop comprehensive monitoring indicators. The efficacy of the DLV-NPE algorithm is validated through its application to the Tennessee Eastman (TE) process, where it demonstrates superior fault detection capabilities compared to other algorithms. © 2024 IEEE.

关键词： Embeddings

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Efficient Inference of Markov Logic Network for Link Prediction 12

Efficient Inference of Markov Logic Network for Link Predict...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Li, Zhongbin Duan, Liang Yue, Kun Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9798350340181

Link prediction has achieved great success on ubiquitous graph-based applications, which usually contain multiple types of connections. The heterogeneity of networks introduces complexities in two aspects: representation of multiple types of links, and incorporation of domain knowledge. In this paper, we construct Markov logic network (MLN) to preserve he logical information of graphs for link prediction tasks. By using the graph neural network, we propose a novel inference method in MLNs to guarantee the scalability of link prediction. Experimental results on various datasets show that our proposed method outperforms the state-of-the-art competitors on precision and efficiency. © 2023 IEEE.

关键词： Graph neural networks

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A Reduced Reachability Tree for a Class of Unbounded Petri Nets

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IEEE/CAA Journal of Automatica Sinica 2015年第4期2卷 345-352页

作者： Shouguang Wang Mengdi Gan Mengchu Zhou Dan You School of Information and Electronic Engineering Zhejiang Gongshang University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Ministry of Education(MoE)Key Laboratory of Embedded System and Service Computing Tongji University MoE Key Laboratory of Embedded System and Service Computing Tongji University Department of Electrical and Computer Engineering New Jersey Institute of Technology

As a powerful analysis tool of Petri nets, reachability trees are fundamental for systematically investigating many characteristics such as boundedness, liveness and reversibility. This work proposes a method to generate a reachability tree, called ωRT for short, for a class of unbounded generalized nets called ω-independent nets based on new modified reachability trees (NMRTs). ωRT can effectively decrease the number of nodes by removing duplicate and ω-duplicate nodes in the tree, and verify properties such as reachability, liveness and deadlocks. Two examples are provided to show its superiority over NMRTs in terms of tree size. © 2014 Chinese Association of Automation.

关键词： Petri nets

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Convolutional Neural Network Based Detection of Seasonal Features in Time Series Data 11

Convolutional Neural Network Based Detection of Seasonal Fea...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hu, Kuang Yue, Kun School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9781665492324

Seasonal features of various kinds of time-varying nodes in large-scale complex networks could facilitate the effective network optimization. It is necessary to find the nodes with seasonal features from the limited labeled training set. In this paper, we proposed a sequence convolutional neural network model to detect the seasonal features in time series data, as well as the method of time series upscaling and subsequence embedding as data preprocessing. Experimental results show the effectiveness of our proposed model. © 2022 IEEE.

关键词： Embeddings

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer 39

RNTrajRec: Road Network Enhanced Trajectory Recovery with Sp...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Chen, Yuqi Zhang, Hanyuan Sun, Weiwei Zheng, Baihua Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Shanghai Institute of Intelligent Electronics & Systems China Singapore Management University School of Computing and Information Systems Singapore

ISBN: (纸本)9798350322279

GPS trajectories are the essential foundations for many trajectory-based applications. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and less spatial consistent. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model to a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach. © 2023 IEEE.

关键词： Recovery

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