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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Dynamic Task Allocation for Heterogeneous Multi-Robot System under Communication Constraints

Dynamic Task Allocation for Heterogeneous Multi-Robot System...

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IEEE Information technology, Networking, Electronic and Automation Control Conference

作者： Jun Li Zhongxuan Cai Minglong Li Wanrong Huang Yongjun Zhang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology China

Multi-robot system has important application potential in disaster rescue and other dangerous scenarios, in which task allocation is the basis for multi-robot cooperation to complete tasks. Due to the limited ability of individual robots, many complex scenarios requires coordination of different types of robot, e.g. robots for search, communication, rescue and so on. At the same time, communication is often limited in a wide range of scenarios. This brings two challenges in the realistic environment: heterogeneous multi-robot cooperation problem and communication constraint problem. This paper proposes a task allocation method based on hierarchical auction and dynamic grouping. By introducing a dynamic grouping algorithm to the general allocation procedure, we simultaneously optimize the number of robots in each group and the communication distance between robots. Then the communication loss rate can be reduced to improve task execution efficiency. Simulations of a communication constrained search and rescue scenario are conducted to verify the proposed algorithm, which are compared with the vanilla auction algorithm and a hierarchical approach on static grouping. The results show that our method surpasses the baseline on both task achievement and communication performance.

关键词： Uncertainty Automation Adaptive systems Robot kinematics Heuristic algorithms Dynamic scheduling Resource management

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Thermal transport in lithium-ion battery: A micro perspective for thermal management

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Frontiers of physics 2022年第1期17卷 143-153页

作者： Changqing Xiang Cheng-Wei Wu Wu-Xing Zhou Guofeng Xie Gang Zhang College of Information Science and Engineering Jishou UniversityJishou416000China School of Materials Science and Engineering&Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and TechnologyXiangtan411201China Institute of High Performance Computing A*STAR Singapore138632Singapore

In recent years, lithium ion (Li-ion) batteries have served as significant power sources in portable electronic devices and electric vehicles because of their high energy density and rate capability. There are growing concerns towards the safety of Li-ion batteries, in which thermal conductivities of anodes, cathodes, electrolytes and separator play key roles for determining the thermal energy transport in Li-ion battery. In this review, we summarize the state-of-the-art studies on the thermal conductivities of commonly used anodes, cathodes, electrolytes and separator in Li-ion batteries, including both theoretical and experimental reports. First, the thermal conductivities of anodes and cathodes are discussed, and the effects of delithiation degree and temperature of materials are also discussed. Then, we review the thermal conductivities of commonly used electrolytes, especially on solid electrolytes. Finally, the basic concept of interfacial thermal conductance and simulation methods are presented, as well as the interfacial thermal conductance between separator and cathodes. This perspective review would provide atomic perspective knowledge to understand thermal transport in Li-ion battery, which will be beneficial to the thermal management and temperature control in electrochemical energy storage devices.

关键词： lithium ion batteries thermal management phonon transport interfacial thermal transport amorphous materials

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Radio Modulation Recognition Based on Attention-based Convolutional Neural Network and Time-frequency Analysis 2

Radio Modulation Recognition Based on Attention-based Convol...

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2nd International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2021

作者： Li, Xinlong Huang, Da Yang, Wenjing Li, Xueqiong Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781665412964

Radio modulation recognition is the key link of modern electronic warfare. This paper applies the idea of deep learning to radio modulation recognition. Since the modulation type is the most important information about the subtle features of the radio signal, we use short time Fourier transform (STFT) to convert the radio signal into a time-frequency distribution map to form a joint distribution of frequency and time. The attention-based convolutional neural network is used to classify and recognize the modulation types of time-frequency distribution maps. The simulation results show that time-frequency analysis can improve the accuracy of recognition, and adding attention can effectively improve the performance of the network. © 2021 IEEE.

关键词： Convolutional neural networks

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Fast linear prediction hyperspectral image visualization algorithm 8

Fast linear prediction hyperspectral image visualization alg...

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8th Annual International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2020

作者： Xu, Yunpeng Peng, Yuanxi Long, Jian State Key Laboratory of High-Performance Computing College of Computer National University of Defense Technology Changsha410073 China

How to generate a color image with as much information as possible from hyperspectral images on a trichromatic display is a subject of great concern. In a sense, effective bands containing important information can be extracted from hyperspectral data for image visualization through the method of band selection. In this paper, two improved methods are proposed for the linear prediction (LP) band selection *** to the information entropy, the band with the greatest difference in information domain is selected as initial ***, the max pooling method is used to select the pixel which can better reflect the image features. Finally, the band similarity is used to select the most distinctive band for hyperspectral image visualization. The experimental results are measured by the metrics such as feature separability, standard deviation, operation efficiency and image displaying *** final experimental results show that fast linear prediction (LP) algorithm is an effective method for hyperspectral image visualization. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Data visualization

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Wave-particle duality relation with a quantum N-path beamsplitter

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Chinese Physics B 2021年第5期30卷 220-228页

作者： Dong-Yang Wang Jun-Jie Wu Yi-Zhi Wang Yong Liu An-Qi Huang Chun-Lin Yu Xue-Jun Yang Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Information and Communication National University of Defense TechnologyXi'an 710006China China Greatwall Quantum Laboratory China Greatwall Technology Group CO.LTD.Changsha 410073China

The wave-particle duality relation derived by Englert sets an upper bound of the extractable information from wave and particle properties in a two-path ***,previous studies demonstrated that the introduction of a quantum beamsplitter in the interferometer could break the limitation of this upper bound,due to interference between wave and particle *** the other line,a lot of efforts have been made to generalize this relation from the two-path setup to the N-path ***,it is an interesting question that whether a quantum N-path beamsplitter can break the limitation as *** paper systemically studies the model of a quantum N-path beamsplitter,and finds that the generalized wave-particle duality relation between interference visibility and path distinguishability is also broken in certain *** further study the maximal extractable information's reliance on the interference between wave and particle properties,and derive a quantitative *** then propose an experimental methodology to verify the break of the *** work reflects the effect of quantum superposition on wave-particle duality,and exhibits a new aspect of the relation between visibility and path distinguishability in N-path ***,it implies the observer's influence on wave-particle duality.

关键词： wave-particle duality interference visibility path distinguishability quantum N-path beamsplitter

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Edge-Driven Industrial computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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A FRAMEWORK FOR SAFE PROBABILISTIC INVARIANCE VERIFICATION OF STOCHASTIC DYNAMICAL SYSTEMS

arXiv

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arXiv 2024年

作者： Wu, Taoran Yu, Yiqing Xia, Bican Wang, Ji Xue, Bai Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Information Science School of Mathematical Sciences Peking University China State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology China

Ensuring safety through set invariance has proven to be a valuable method in various robotics and control applications. This paper introduces a comprehensive framework for the safe probabilistic invariance verification of both discrete- and continuous-time stochastic dynamical systems over an infinite time horizon. The objective is to ascertain the lower and upper bounds of liveness probabilities for a given safe set and set of initial states. The liveness probability signifies the likelihood of the system remaining within the safe set indefinitely, starting from a state in the initial set. To address this problem, we propose optimizations for verifying safe probabilistic invariance in discrete-time and continuous-time stochastic dynamical systems. These optimizations are constructed via either using the Doob’s nonnegative supermartingale inequality-based method or relaxing the equations described in [30,32], which can precisely characterize the probability of reaching a target set while avoiding unsafe states. Finally, we demonstrate the effectiveness of these optimizations through several examples using semi-definite programming tools. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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Effect of thickness variations of lithium niobate on insulator waveguide on the frequency spectrum of spontaneous parametric down-conversion

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Chinese Physics B 2021年第11期30卷 268-273页

作者： Guang-Tai Xue Xiao-Hui Tian Chi Zhang Zhenda Xie Ping Xu Yan-Xiao Gong Shi-Ning Zhu National Laboratory of Solid State Microstructures School of PhysicsSchool of Electronic Science and EngineeringCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of ComputingNational University of Defense TechnologyChangsha 410073China

We study the effect of waveguide thickness variations on the frequency spectrum of spontaneous parametric downconversion in the periodically-poled lithium niobate on insulator(LNOI)*** analyze several variation models and our simulation results show that thickness variations in several nanometers can induce distinct effects on the central peak of the spectrum,such as narrowing,broadening,and *** also prove that the effects of positive and negative variations can be canceled and thus lead to a variation-robust feature and an ultra-broad *** study may promote the development of on-chip photon sources in the LNOI platform,as well as opens up a way to engineer photon frequency state.

关键词： lithium niobate on insulator spontaneous parametric down-conversion waveguide thickness variation frequency spectrum

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