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Multi-layer network embedding on scc-based network with motif

Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Cooperative differential games guidance laws for multiple attackers against an active defense target

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Chinese Journal of Aeronautics 2022年第5期35卷 374-389页

作者： Fei LIU Xiwang DONG Qingdong LI Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of ChinaChongqing UniversityChongqing 400044China

This paper is concerned with a scenario of multiple attackers trying to intercept a target with active *** types of agents are considered in the guidance:The multiple attackers,the target and the defender,where the attackers aim to pursuit the target from different directions and evade from the defender *** guidance engagement is formulated in the framework of a zero-sum two-person differential game between the two opposing teams,such that the measurements on the maneuver of the target or estimations on the defending strategy of the defender can be *** of the attackers resides in two aspects:redundant interception under the threat of the defender and the relative intercept geometry with the *** miss distances,the relative intercept angle errors and the costs of the agents are combined into a single performance index of the *** formulation enables a unitary approach to the design of guidance laws for the *** minimize the control efforts and miss distances for the attackers,an optimization method is proposed to find the best anticipated miss distances to the defender under the constraint that the defender is endowed with a capture *** simulations with two cases are conducted to illustrate the effectiveness of the proposed cooperative guidance law.

关键词： Active defense Cooperative guidance Differential games Minimum effort optimization Relative intercept angle

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Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse data

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 796-805页

作者： Di Wu Xin Luo the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China

High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor *** they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this *** LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with *** L2-norm is sensitive to outlier ***,outlier data usually exist in such *** example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious *** address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)*** main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS *** results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.

关键词： High-dimensional and sparse matrix L1-norm L2 norm latent factor model recommender system smooth L1-norm

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State-of-Charge Estimation of Lithium Battery Based on Deep Residual Shrinkage Networks and a Variant Long Short Term Memory Neural Network

State-of-Charge Estimation of Lithium Battery Based on Deep ...

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Chinese Control and Decision Conference, CCDC

作者： Penghua Li Yihui Zhang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China

Accurate state-of-charge (SOC) estimation, which is critical to ensuring the safe and reliable operation of battery management systems in electric vehicles, is still a challenging task due to sophisticated battery dynamics and ever-changing ambient conditions. This paper proposes a method combining DRSN and ASTLSTM neural networks to estimate lithium batteries' state of charge (SOC). The proposed method can not only utilize the historical information of the data but also eliminate unimportant features by inserting soft thresholding into the deep architectures. To reveal the performance of the proposed method, the results are compared with several other methods from the literature. The comparative experimental results show that the proposed method achieves a lower root mean square error (RMSE) of 0.83% with normal data and RMSE of 0.98% with noisy data, respectively.

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State of Health Estimation of Lithium-ion Battery Based On Improved Transfer Learning

State of Health Estimation of Lithium-ion Battery Based On I...

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Chinese Control and Decision Conference, CCDC

作者： Penghua Li Kangheng Shan Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China

Many existing methods of forecasting the state- of-health(SOH) assume that training and testing data follow the same distribution. The model based on dataset under one working condition may be ineffective for the dataset under another working condition due to the distribution discrepancy. In order to meet this challenge, this paper proposes an improved method Mutual Information Domain-Adversial Neural Networks (DANN) based on domain adaptation, which improves the domain discriminator to better extract domain invariant features. In addition, to avoid the loss of target information, the mutual information among target features, source features, and original target data is calculated to fix the features on the target site during the migration process. Different from the traditional methods, we only use 40% of the data sets for training, and the rest are used for prediction, so we can complete the prediction of more scenes. Experimental results show that this method can accurately and stably predict SOH.

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Improved RUL prediction method of wind turbine gearbox bearings under strong noises

Improved RUL prediction method of wind turbine gearbox beari...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Sheng Xiang Penghua Li Jie Hou Mamadsho Ilolov Jiwei Wang Deqi Huang Lirong Xie School of Automation& Key Laboratory of Intelligent Computing for Big Data Chongqing University of Posts and Telecommunications Chongqing China Center for Innovative Development of Science and New Technologies National Academy of Sciences of Tajikistan Dushanbe Tajikistan School of Electrical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

As essential components within the wind turbine gearbox, bearings are critical, and forecasting their Remaining Useful Life (RUL) is paramount. To enhance the integration of current inputs with historical data in Recurrent Neural Network (RNN) models, the Mogrifier Long Short-Term Memory (LSTM) approach has been devised. It interacts with inputs and recurrent data in a predetermined fashion, marking its initial application in wind turbine gearbox bearing RUL prediction. Furthermore, the Relative Standard Deviation of Envelope Spectrum (RSDES), a reliable health indicator (HI) under high noise conditions, is introduced for precise evaluation of the gearbox bearing health. Consequently, a novel RUL prediction technique is introduced. Initially, RSDES from vibration data serves as HIs. Subsequently, utilizing these HIs, the Mogrifier LSTM predicts future HIs, and the bearing’s RUL is determined once the predicted HI surpasses the failure threshold. Empirical outcomes underscore the proposed method’s superiority over existing techniques in bearing RUL prediction.

关键词： Vibrations Recurrent neural networks Accuracy Noise data models Wind turbines Reliability

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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LAGIM: A Label-Aware Graph Interaction Model for Joint Multiple Intent Detection and Slot Filling

LAGIM: A Label-Aware Graph Interaction Model for Joint Multi...

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Chinese Control and Decision Conference, CCDC

作者： Penghua Li Ziheng Huang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China

Multi-intent spoken language understanding joint model can handle multiple intents in an utterance and is closer to complicated real-world scenarios, attracting increasing attention. However, existing research (1) usually focuses on identifying implicit correlations between utterances and one-hot encoding while ignoring intuitive and explicit original label characteristics; (2) only considers the token-level intent-slot interaction, which results in the limitation of the performance. In this paper, we propose a Label-Aware Graph Interaction Model (LAGIM), which captures the correlation between utterances and explicit labels’ semantics to deliver enriched priors. Then, a global graph interaction module is constructed to model the sentence-level interaction between intents and slots. Specifically, we propose a novel framework to model the global interactive graph based on the injection of the original label semantics, which can fuse explicit original label features and provide global optimization. Experimental results show that our model outperforms existing approaches, achieving a relative improvement of 11.9% and 2.1% overall accuracy over the previous state-of-the-art model on the MixATIS and MixSnips datasets, respectively.

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Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang Transform and Feature Fusion Dimensionality Reduction 5

Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang T...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Fan, Lin Yao, Ruiling Wang, Zhongmin School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi 'An Key Laboratory of Big Data and Intelligent Computing China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the development of artificial intelligence, pulse diagnosis has been standardized and objectified. However, there is a lack of research on the extraction and dimensionality reduction of hypertensive pulse features. We propose two effective features for distinguishing pulses of disease samples and a fusion dimensionality reduction method that combines linear and nonlinear dimensionality reduction. The results show that the proposed features and dimensionality reduction method make the classification accuracy of hypertension pulse feature reach 94.23%, and the training time of the classifier is reduced by 47 seconds, which improves the performance in terms of both accuracy and time. © 2022 ACM.

关键词： Hilbert-Huang transform

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