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Multi-sensor data fusion and target location in pipeline monitoring and a pre-warning system based on multi-seismic sensors

Multi-sensor data fusion and target location in pipeline mon...

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International Conference on Pipelines and Trenchless technology 2012: Better Pipeline Infrastructure for a Better Life, ICPTT 2012

作者： Sun, Jiedi Zhang, Jinquan Wang, Xiaojun College of Information Science and Engineering Yanshan University No. 438 Hebei Avenue Qinhuangdao City Hebei Province China Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province No. 438 Hebei Avenue Qinhuangdao City 066004 China Information Technology Unit of China Petrolium Communication Company No. 49 Jin'guang Road Langfang Hebei Province 065000 China

ISBN: (纸本)9780784412619

As a safe and economical method the pipeline is widely used to transport the oil and gas. At present, most of serious pipeline accidents are the artificial damages. The traditional leakage detection methods only infer the leakages by analyzing the parameters. The sensitivities are low and the leakage warning exclusively arrives after the leakage. The urgent problem is to detect the intrusions and locate the threat position. For this reason, we studied and developed a novel pipeline security monitoring and pre-warning system based on multi-seismic sensors. The system consists of many seismic geophones and data acquisition modules which are laid along the pipeline. The events such as footstep, car moving and the digging generate the seismic waves. These sensors and modules acquire the seismic signals. The empirical mode decomposition is used to acquire the IMFs (intrinsic mode functions). Then the IMF kurtosis features are normalized and used to recognize the different targets. In order to improve the individual target recognition accuracy, it proposes to fuse the identification results by using the Dempster-Shafer evidence theory and obtain the final judgment. If the target belongs to threat, the location will be finished. The time-frequency analysis method is used to process the seismic signals. The EMD outcomes are transformed by Hilbert-Huang Transform and then it can acquire the signal's instantaneous properties such as instantaneous frequency, phase and amplitude. In this paper we also discuss the localization procedures and put forward to a new passive localization method based on HHT and TDOA. The filed trails show the proposed methods work well. © 2013 American Society of Civil Engineers.

关键词： Hilbert-Huang transform

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Reinforced Heterogeneous Graphlet Design for Knowledge Graph Representation Learning

SSRN

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SSRN 2023年

作者： Gong, Jibing Lin, Yuting Zhao, Yi Lin, Tianyu Fang, Xiaohan Feng, Xinchao Peng, Jiquan School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province Yanshan University Qinhuangdao066004 China

Knowledge Graphs have been practical tools to represent and integrate plentiful structural and semantic information in mainstream industrial scenarios. While promising, the heterogeneity and complexity of KGs pose a formidable obstacle, especially for graph representation learning. Most existing KG embedding models omit the dynamic high-order connectivity patterns, and heavily rely on the handcrafted patterns to handle the complex semantic relationships, thereby limiting their ability to adaptively capture nuanced and intricate relationships of knowledge graphs in different tasks. To fill this gap, we present ReHGD — a model designed for KGs that focuses on adaptively designing typed graphlets through a cooperative Multi-agent Reinforcement Learning algorithm. This approach can learn more discriminative graph representations tailored to specific downstream tasks. Specifically, ReHGD engages in the creation of typed graphlets through a two-stage process: it (1) establishes a reinforced chain design module to generate chains without predefined rules, and (2) employs a buffer-aware sampling technique to derive episodic chains from prior experiences. Subsequently, motifs are deduced by applying commute count and Hadamard product operations to the episodic chain-based subgraphs. Finally, ReHGD utilize chain and motif aggregations to learn graph representations. Experimental results on three real-world KGs show that ReHGD outperforms baselines on entity classification. © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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SYVR virtual Equipment Manufacturing Simulation

SYVR Virtual Equipment Manufacturing Simulation

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International Conference on virtual Reality and Visualization (ICVRV)

作者： Yuhan Liu Yong Tang Qian Sun Shiny VR Yanshan University College of Information Science and Engineering Qinhuangdao China Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9781538684986;9781538684979

SYVR is a real-time simulation graphic engine focusing on equipment manufacturing. It is based on decades of research works of physics simulations, is able to provide services like real-time simulation for key parts of devices, large-scale com-plicated situation simulation, virtual experiment, operational training, visualization of concept design and etc., which con-sist of the whole process of virtual manufacturing and simulation. SYVR can provide comprehensive technology support for industrial 3-dimensional data, accelerate applications of VR to all aspects of industry, cut down development costs of equipment manufacturing significantly, highly increase effi-ciency of equipment design, and promote development of equipment manufacturing.

关键词： Conferences virtual reality Visualization

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Human Movement Detection and Gait Periodicity Analysis Using Channel State Information

Human Movement Detection and Gait Periodicity Analysis Using...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Zijuan Liu Lin Wang Wenyuan Liu Binbin Li School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province School of Economics and Management Yanshan University Qinhuangdao HeBei China

ISBN: (纸本)9781509056972

Under the influence of multipath effects and small scale fading, the robustness and reliability of the existing human detection methods based on radio frequency signals are easy to be impaired. In this paper, we propose a novel design that using the multi-layer filtering of channel state information (CSI) to identify moving targets in dynamic environments and analyze the gait periodicity of human. We employ an efficient CSI subcarrier feature difference to the multi-layer filtering method leveraging principal component analysis (PCA) and discrete wavelet transform (DWT) to eliminate the noises. Furthermore, we propose a profile matching mechanism for human detection and a periodicity analysis mechanism for human gait taking advantage of the above design. We evaluated it with the commodity Wi-Fi infrastructures in different environments. Experimental results indicate that our approach performs identification of human with an average accuracy of 94%.

关键词： Principal component analysis Wireless sensor networks Wireless communication Discrete wavelet transforms Feature extraction Wireless fidelity Object detection

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A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator

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China Communications 2014年第9期11卷 112-123页

作者： YI Huawei ZHANG Fuzhi LAN Jie School of Information Science and Engineering Yanshan University Qinhuangdao 066004 Hebei Province E R.China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 P. R. China Liaoning University of Technology Jinzhou 121001 Liaoning Province P. R. China

The existing collaborative recommendation algorithms have lower robustness against shilling *** this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey ***,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor *** influence of attack profiles on the recommendation results is reduced through adjusting similarities among ***,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature ***,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization *** results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

关键词： shilling attacks robust collaborative recommendation matrix factori-zation k-distance Tukey M-estimator

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The study to eeg synchronization in alzheimer's disease based on new s estimator method

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Journal of Convergence Information technology 2012年第22期7卷 1-7页

作者： Wen, Dong Chen, Ruihong Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University Qinhuangdao Hebei 066004 China Division of General Internal Medicine Xiantao First People's Hospital Xiantao Hubei 433000 China

Synchronization decay of resting state EEG has shown that cognitive dysfunction in Alzheimer's disease (AD) was relevant to a loss of functional connectivity in intermediate frequency bands. The new S estimator (NSE) proposed recently by us quantifies synchronization between neuronal signals at multiple *** paper meant to explore the behavior of synchronization of multichannel EEG in AD and healthy normal controls at rest, and preliminarily make clear the clinical significance of the NSE in AD *** (EEG) were recorded from 10 AD patients and 12 age-matched healthy normal controls (NC). NSE values were computed both across the all frequency band and separately in the delta, theta, alpha, beta (including beta1, beta2 and beta3), and gamma bands. The Mini-Mental Status Examination (MMSE) was used to assess the symptom severity of AD patients and *** values in the beta1 and beta2 bands were significantly lower in AD patients than in NC. NSE values in the alpha and beta1 bands were positively correlated with the MMSE scores in all participants (AD and NC). In AD patients, NSE values in the alpha, beta1 and beta2 bands were also positively correlated with MMSE *** results suggest that NSE values are a useful correlate of EEG synchronization in AD patients.

关键词： Synchronization

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Real-time simulation of large-scale water environments with interacting objects

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Journal of Computational Information systems 2015年第9期11卷 3257-3264页

作者： Li, Ying Peng, Jingxuan Fan, Fuding Lv, Mengya Zhang, Lihui Zhao, Jing Tang, Yong The College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Department of Information Engineering Environmental Management College of China Qinhuangdao China

We present a new GPU scheme for real-time simulation in large-scale water environments with physics-based object interaction. To improve the simulation efficiency, we divide surface waves into ambient waves and interactive waves. A fast and stable Verlet integration method is applied to solve the wave equation to simulate the propagation of interactive waves. To solve the problem that the interactive area caused by the moving object is uncertain, we propose a moving grid suited to LOD (Levels of Detail) surface with multiple resolutions, which reduces the computation cost. The extension of multiple moving grids makes the interaction of water and various objects possible. We propose a volume conservation based method to generate interactive waves and apply rigid body dynamics to demonstrate the effect of water reaction against the object. Experimental results show that our approach achieves higher visual realism and performance. ©, 2015, Binary Information Press. All right reserved.

关键词： Wave equations

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Cluster-based superseding nearest neighbor queries on uncertain data

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Journal of Convergence Information technology 2012年第17期7卷 392-401页

作者： Liu, Wenyuan Li, Wei Du, Ying Chen, Zijun He, Yourui College of Information Science and Engineering Yanshan University Qinhuangdao Hebei 066004 China College of Economics and Management Yanshan University Qinhuangdao Hebei 066004 China The Key Lab. for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao Hebei 066004 China

Recently, query processing based on uncertain data is becoming research focuses. Cluster-based superseding nearest neighbor (CSNN) query algorithm on uncertain data is proposed in this paper. We divide the instances of each object into k clusters by applying the clustering algorithm k-means, and then store these clustered objects in R-tree like index structures. Ranked list will be retrieved through traversing R-tree, which could include instances, clusters, or objects. While unfolding the ranked list, the superseding graph G will be constructed gradually based on the superseding relationship. The final result sets will be found through pruning and validating the superseding graph G. Finally, the experimental results show that the algorithm is efficient and effective.

关键词： Clustering algorithms

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VT-SGN:Spiking Graph Neural Network for Neuromorphic Visual-Tactile Fusion

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IEEE Transactions on Haptics 2024年 PP卷 PP-PP页

作者： Wu, Peiliang Zhang, Haozhe Li, Yao Chen, Wenbai Gao, Guowei Yanshan University School of Information Science and Engineering Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Beijing Information Science and Technology University School of Automation Beijing100192 China

Current issues with neuromorphic visual-tactile perception include limited training network representation and inadequate cross-modal fusion. To address these two issues, we proposed a dual network called visual-tactile spiking graph neural network (VT-SGN) that combines graph neural networks and spiking neural networks to jointly utilize the neuromorphic visual and tactile source data. First, the neuromorphic visual-tactile data were expanded spatiotemporally to create a taxel-based tactile graph in the spatial domain, enabling the complete exploitation of the irregular spatial structure properties of tactile information. Subsequently, a method for converting images into graph structures was proposed, allowing the vision to be trained alongside graph neural networks and extracting graph-level features from the vision for fusion with tactile data. Finally, the data were expanded into the time domain using a spiking neural network to train the model and propagate it backwards. This framework effectively utilizes the structural differences between sample instances in the spatial dimension to improve the representational power of spiking neurons, while preserving the biodynamic mechanism of the spiking neural network. Additionally, it effectively solves the morphological variance between the two perceptions and further uses complementary data between visual and tactile. To demonstrate that our approach can improve the learning of neuromorphic perceptual information, we conducted comprehensive comparative experiments on three datasets to validate the benefits of the proposed VT-SGN framework by comparing it with state-of-the-art studies. © 2008-2011 IEEE.

关键词： Graph neural networks

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Learning to Cluster Person in the Video via Graph Convolution Network

Learning to Cluster Person in the Video via Graph Convolutio...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Wei Li Sixin Liang Ang Bian Guodong Du College of Computer Science Sichuan University Chengdu China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao P.R. China

Video person re-identification is receiving academic interest. However, the practical application of the algorithm is hardly supported because of prohibitive annotated data. Hence, the study for unlabeled data will lead to an attractive alternative. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the video through graph convolutional networks. In this paper, we find that the possibility of inter-frame linkage can be inferred from context. Therefore, a pose-guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose-guided representation module; (ii) a pose-guided embedding module; (iii) a link prediction module. Firstly, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross-frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior.

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