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8th International Conference on Systems and Informatics, ICSAI 2022

作者： Liu, Jin Kang, Yanqin Liu, Tao Zhang, TingYu Zhang, Yikun Anhui Polytechnic University School of Computer and Information Wuhu China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665474481

clinical low X-ray dose computed tomography (LDCT) scanner often induce high intensity strip artifact and spot nosie, compromising diagnoses and intervention plans. Recently, sparsely constrained and network learning-based frameworks have been shown to be efficient in mitigating such issue. In this work, we propose a deep iterative reconstruction network (DIRNet) model with a residual constraint to synergize the advantages of feature learning and image reconstruction to address the LDCT imaging problem. DIR-Net compose by few iteration units, and all iteration units include three different network modules: projection restoration, residual constraint and image update block. DIR-Net is a promising approach for building an end-to-reconstruction mapping strategy and directly obtaining high-quality CT images. Furthermore, LISTA is used to conFigure the network, and the whole network architecture yields improved interpretability. Qualitative and quantitative analysis in test data shown the promising imaging effects of DIR-Net in quantum noise reduction, block artifact removal and tissue detail texture mantian. © 2022 IEEE.

关键词： Image reconstruction

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Gradient compared p-LMS algorithms for sparse system identification 27

Gradient compared p-LMS algorithms for sparse system identif...

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27th Chinese Control and Decision Conference, CCDC 2015

作者： Feng, Yong Wu, Jiasong Zeng, Rui Luo, Limin Shu, Huazhong School of Biological Science and Medical Engineering Southeast University Nanjing China Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781479970179

In this paper, we propose two novel p-norm penalty least mean square (p-LMS) algorithms as supplements of the conventional p-LMS algorithm established for sparse adaptive filtering recently. A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error, which leads to the new proposed gradient compared p-norm constraint LMS algorithm (pGC-LMS). We explain that the pGC-LMS can achieve lower mean square error than the standard p-LMS algorithm theoretically and experimentally. To further improve the performance of the filter, the pNGC-LMS algorithm is derived using a new gradient comparator which takes the sign-smoothed version of the previous one. The performance of the pNGC-LMS is superior to that of the pGC-LMS in theory and in simulations. Moreover, these two comparators can be easily applied to other norm constraint LMS algorithms to derive some new approaches for sparse adaptive filtering. The numerical simulation results show that the two proposed algorithms achieve better performance than the standard LMS algorithm and p-LMS algorithm in terms of convergence rate and steady-state behavior in sparse system identification settings. © 2015 IEEE.

关键词： Least Mean Square Algorithm New Gradient Comparator Sparse System Identification p Norm Constraint

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Analysis and Improvement of an Efficient Controlled Quantum Secure Direct Communication and Authentication Protocol

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computers, Materials & Continua 2018年第12期57卷 621-633页

作者： Jifeng Zhong Zhihao Liu Juan Xu Navigation College Jimei UniversityXiamen361021China School of Computer Science and Engineering Southeast UniversityNanjing211189China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjing211189China Institute for Quantum Computing University of WaterlooWaterlooONCanadaN2L3G1

The controlled quantum secure direct communication(CQSDC)with authentication protocol based on four particle cluster states via quantum one-time pad and local unitary operations is *** is found that there are some serious security issues in this *** eavesdropper(Eve)can eavesdrop on some information of the identity strings of the receiver and the controller without being detected by the selective-CNOT-operation(SCNO)*** the same attack,Eve can also steal some information of the secret message that the sender *** addition,the receiver can take the same kind of attack to eavesdrop on some information of the secret message out of the control of the *** means that the requirements of CQSDC are not *** last,we improve the original CQSDC protocol to a secure one.

关键词： Quantum cryptography controlled quantum secure direct communication selective-CNOT-operation attack

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Advances and challenges in artificial intelligence text generation

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Frontiers of information Technology & Electronic Engineering 2024年第1期25卷 64-83页

作者： Bing LI Peng YANG Yuankang SUN Zhongjian HU Meng YI School of Computer Science and Engineering Southeast UniversityNanjing 210000China Key Laboratory of Computer Network and Information Integration Ministry of EducationSoutheast UniversityNanjing 210000China

Text generation is an essential research area in artificial intelligence(AI)technology and natural language processing and provides key technical support for the rapid development of AI-generated content(AIGC).It is based on technologies such as natural language processing,machine learning,and deep learning,which enable learning language rules through training models to automatically generate text that meets grammatical and semantic *** this paper,we sort and systematically summarize the main research progress in text generation and review recent text generation papers,focusing on presenting a detailed understanding of the technical *** addition,several typical text generation application systems are ***,we address some challenges and future directions in AI text *** conclude that improving the quality,quantity,interactivity,and adaptability of generated text can help fundamentally advance AI text generation development.

关键词： AI text generation Natural language processing Machine learning Deep learning

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A common gateway for interoperability in multi-architecture LVC simulation 6

A common gateway for interoperability in multi-architecture ...

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6th International Conference on computer information Science and Application Technology, CISAT 2023

作者： Lin, Shichao Wang, Yun School of Computer Science and Engineering Southeast University Nanjing China Key Lab of Computer Network and Information Integration MOE China

ISBN: (纸本)9781510668546

With the development of Live-Virtual-Constructive (LVC) simulation technology, numerous LVC simulation resources have been developed. To construct more large-scale LVC simulation, it is necessary to integrate existing simulation resources. Due to the heterogeneity of LVC resources, the interoperability between heterogeneous simulation resources becomes a major concern. This paper proposes a common gateway in multi-architecture LVC simulation environment that converts messages between different simulation architectures and achieves a level of semantic interoperability. The gateway provides a collection of protocol-independent interfaces for simulation systems to access co-simulation conveniently. To maintain timing consistency in heterogeneous simulation environments, a time synchronization method is employed to coordinate logical time and real time. Experimental results show that the scheme is flexible and can maintain data transmission latency at a millisecond level in LVC simulation environment, which includes High Level Architecture (HLA), Data Distribution Service (DDS), and Distributed Interactive Simulation (DIS). © 2023 SPIE.

关键词： Interoperability

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Intelligent Optimization Method for Mobile NOMA network using IMBO 6

Intelligent Optimization Method for Mobile NOMA Network usin...

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6th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2023

作者： Fu, Xingyue Xu, Lingwei Gao, Zhihe Chen, Zhe Li, Yufang Cao, Shubo Qingdao University of Science & Technology Qingdao China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing211189 China

ISBN: (纸本)9798350399059

Non-orthogonal multiple access (NOMA) technology can significantly increase access volume and spectrum efficiency. The introduction of NOMA technology into the mobile communication system can significantly enhance communication performance. The power allocation technology can greatly improve the performance of NOMA systems and has become a research hotspot. However, many research schemes currently have many problems, such as poor system performance and inability to balance between user fairness and service quality. Therefore, this article studies the intelligent optimization for mobile NOMA network using improved Monarch Butterfly Optimization Algorithm(IMBO). First, an N-Nakagami channel-based mobile cooperative NOMA multi-user network is established. Then, considering the non-ideal communication condition, namely imperfect channel state information (ICSI), the exact expressions of outage probability (OP) for the mobile cooperative NOMA multi-user communication system are derived, and the relation between user power allocation coefficient and the performance of OP is investigated. Finally, an optimization algorithm of power allocation based on the IMBO is put forward, which can get the optimal power allocation coefficient. Simulation results indicate that, compared to the monarch butterfly optimization algorithm(MBO), the artificial fish swarm algorithm(AFSA), and the firefly algorithm(FA), the IMBO is at least 30% more efficient, thereby enhancing the performance of the communication system. © 2023 IEEE.

关键词： Spectrum efficiency

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KGRN: Knowledge Graph Relational Path network for Target Prediction of TCM Prescriptions 17th

KGRN: Knowledge Graph Relational Path Network for Target Pre...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Gong, Zhuo Zhang, Naixin He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Nanjing210018 China

ISBN: (纸本)9783030845315

Accurately predicting the effect targets of Traditional Chinese Medicine (TCM) prescriptions is essential for exploring the molecular mechanism and clarifying the mechanism of TCM. Although some scholars have used network pharmacology to study the target prediction of TCM prescriptions, all of them are for the analysis of a specific prescription-disease association, and the universal method for predicting effect targets of TCM prescriptions has not discovered yet. Therefore, this paper proposes a Knowledge Graph Relational Path network for Target Prediction of TCM Prescriptions (KGRN), which can integrate prescription’s molecular and target information through the Knowledge Graph (KG). At the same time, the semantic information and high-level structure of the knowledge graph are extracted through Knowledge Graph Embedding and Graph Neural networks, which can effectively capture the correlation between the prescriptions and the targets. Moreover, by emphasizing the KG relations when aggregating neighborhood information, the Relational Path network of the KGRN model has the ability to capture the relationship dependence in the KG path, which enhances the ability to predict the relationship between the prescriptions and the targets. The experiments under multiple configurations demonstrate that KGRN outperforms the related competitive benchmarks. © 2021, Springer Nature Switzerland AG.

关键词： Knowledge graph

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Topgformer: Topological-Based Graph Transformer for Mapping Brain Structural Connectivity to Functional Connectivity 48

Topgformer: Topological-Based Graph Transformer for Mapping ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guo, Dalu Zhang, Ke Li, Jiaxing Kong, Youyong Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Nanjing China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Exploring the mapping between structural connectivity (SC) and functional connectivity (FC) is of essential importance to understanding the working mechanism of the human brain. Traditional methods are difficult to represent the complex relationship of high-order interaction between SC and FC. Recent learning-based methods can not well capture the important long-range interactions and edge information of brain connectivity. To address the issue, we propose a novel Topological-based Graph Transformer (TopGFormer) to generate functional connectivity from the structure connectivity with sufficient consideration of topological properties of brain connectivity. We propose a Topological Multi-Head Attention block that simultaneously uses centrality encoding and adjacency matrix to capture node and edge importance respectively. The centrality encoding reflects the importance of brain regions while the adjacency matrix captures the linking relationship of different regions. Experiments on HCP resting and emotion task datasets demonstrate the performance of the proposed method. © 2023 IEEE.

关键词： functional connectivity structural connectivity Structure-function mapping transformer

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Topology Uncertainty Modeling For Imbalanced Node Classification on Graphs 48

Topology Uncertainty Modeling For Imbalanced Node Classifica...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gao, Jiayi Li, Jiaxing Zhang, Ke Kong, Youyong Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9781728163277

Most existing graph neural networks work under a class-balanced assumption, while ignoring class-imbalanced scenarios that widely exist in real-world graphs. Although there are many methods in other fields that can alleviate this issue, they do not consider the special topology of the non-Euclidean graph. Hence, we propose Graph Topology Uncertainty (GraphTU), a novel probabilistic class-imbalanced solution specifically for graphs. Firstly, an invisible "uncertain gap"between under-represented minorities in training set and authentic minorities in unseen set is modeled by estimating statistical variances in topology. We extend the training distribution for minorities by sampling in this gap through a non-parametric way. Moreover, a gradient-guided mask is introduced to prevent biased statistics. Extensive experiments demonstrate the superior performance of GraphTU. © 2023 IEEE.

关键词： Class Imbalance Node Classification

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Zigbee enabled remote temperature monitor system for high-voltage substations

Zigbee enabled remote temperature monitor system for high-vo...

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2012 Spring World Congress on Engineering and Technology, SCET 2012

作者： Jun, Zhang Wei, Dai Di, Huang Sanfeng, Zhang Yi, Ji College of Software Engineering Southeast University Nanjing China College of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9781457719646

in high-voltage substations, poor contacts at the junctions of contacts can generate heat leading to high temperature, arc and fire disaster in serious condition. In this paper, we present a remote temperature monitor system to monitor and alarm when temperature of the contacts increases. The system measures temperature by sensor node based on low power consuming temperature sensor and Zigbee communication because power consuming becomes a critical fact constrained by long term running and inconvenient maintenance of high-voltage substations. In the system, sensor nodes are deployed on the contacts to be measured, cluster headers in charge of collecting data from sensor nodes are deployed on each distributor and cluster headers are connected with mast monitor computer via RS485 network. This paper presents design, implementation and test of sensor nodes, cluster headers and the master monitor software. © 2012 IEEE.

关键词： Sensor nodes

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