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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Gou, Pengfei Zheng, Daiwei Tang, Lei Liu, Mengjuan School of Information and Software Engineering University of Electronic Science and Technology of China UESTC Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China NPIC Chengdu China

ISBN: (纸本)9798350354010

Prognostics and Health Management (PHM) is currently the mainstream solution for the condition monitoring and maintenance of industrial systems. In PHM, the system condition is monitored by monitoring the operational status parameters of the system to diagnose equipment faults, predict the remaining useful life, etc. Utilizing sequential operational status parameters to complete prediction and classification tasks is usually modeled as a time series processing task. Therefore, a high-performance time series prediction model is crucial for PHM. This paper focuses on the use of time series prediction models to achieve the prediction of key state parameters of the nuclear power system. However, there are still some challenges when directly applying existing time series prediction models to industrial systems. First, obtaining operational data on the nuclear power system is relatively difficult, resulting in a small amount of training data, which causes many models to face the risk of insufficient training or overfitting. Second, most of the existing time series prediction models are composed of high- and low-frequency decoupling modules, while the operational data of nuclear power systems often have fewer high-frequency components, making it difficult for many existing SOTA models to achieve good results. In order to address these challenges, we have developed a Transformer-based time series prediction model with contrastive learning for monitoring the status of nuclear power systems. We evaluated the new model on three nuclear power simulation datasets and four power transformer datasets. The experimental results show that the new model's performance is superior to baseline models. We also proved the effectiveness of our improvement through ablation experiments. © 2024 IEEE.

关键词： Prediction models

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Adaptive 3D Routing Protocol for Flying Ad Hoc Networks Based on Prediction-Driven Q-Learning

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China Communications 2022年第5期19卷 302-317页

作者： Min Zhang Chao Dong Simeng Feng Xin Guan Huichao Chen Qihui Wu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjing 210016China School of Software Engineering Tongji UniversityShanghai 201804China

The routing protocols are paramount to guarantee the Quality of Service(QoS)for Flying Ad Hoc Networks(FANETs).However,they still face several challenges owing to high mobility and dynamic *** paper mainly focuses on the adaptive routing protocol and proposes a Three Dimensional Q-Learning(3DQ)based routing protocol to guarantee the packet delivery ratio and improve the *** 3DQ routing,we propose a Q-Learning based routing decision scheme,which contains a link-state prediction module and routing decision *** link-state prediction module allows each Unmanned Aerial Vehicle(UAV)to predict the link-state of Neighboring UAVs(NUs),considering their Three Dimensional mobility and packet ***,UAV can produce routing decisions with the help of the routing decision module considering the *** evaluate the various performance of 3DQ routing,and simulation results demonstrate that 3DQ can improve packet delivery ratio,goodput and delay of baseline protocol at most 71.36%,89.32%and 83.54%in FANETs over a variety of communication scenarios.

关键词： routing unmanned aerial vehicles(UAVs) flying ad hoc networks(FANETs) prediction Q-Learning

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Multipath matching pursuit using a cross-validation technique for sparse direction-of-arrival estimation with an acoustic vector array

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Chinese Journal of Acoustics 2023年第2期42卷 153-170页

作者： TAN Peng HU Bo ZHANG Youwen WU Yuquan YANG Desen Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001 Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001 College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001 School of Electronics and Information Jiangsu University of Science and TechnologyZhenjiang 212100 Science and Technology on Integrated Information System Laboratory Institute of SoftwareChinese Academy of SciencesBeijing 100190

High-resolution direction-of-arrival(DOA)estimations and the starboard ambiguity of moving underwater targets have always been key issues in underwater acoustic array signal *** with sound pressure arrays,vector arrays have natural advantages with respect to solving the starboard ambiguity problem and obtaining higher processing *** high-resolution DOA estimation methods such as Capon have disadvantages such as being unable to resolve coherent sources,requiring multiple snapshot processing,and being sensitive to array manifold ***-resolution DOA estimation and the starboard ambiguity of moving underwater targets have always been challenging research *** one hand,maneuvering underwater targets reduce the coherence time of the received signals,which ultimately leads to poor performance when using high-resolution DOA estimation technologies based on the covariance matrix of the received *** the other hand,traditional DOA estimation technologies based on sound pressure arrays have the problem of port and starboard ambiguity,which can be solved by maneuvering the sonar ***,maneuvering the sonar platform can impair the coherence of the received signal,on which some algorithms *** approach greatly limits the combat effectiveness and performance of the *** the aforementioned problems and taking advantage of the target sparsity,a cross-validation multipath matching pursuit technique based on the sparse DOA estimation of an acoustic vector array is proposed in this article for sonar *** proposed algorithm uses cross-validation technology to achieve a sparse DOA estimation with an unknown number of targets in a sonar observation *** with the conventional acoustic vector array-based Capon algorithm,the proposed algorithm can achieve a sparse DOA estimation and high-resolution capability with small numbers of snapshots or even single *** effectiveness of the proposed

关键词： multipath estimation underwater

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology SydneyNSW2007 Australia Institute of Software Chinese Academy of Sciences Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 1982-2012 IEEE.

关键词： Qubits

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Time sequential influence maximization algorithm based on neighbor node influence

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High Technology Letters 2022年第2期28卷 153-163页

作者： CHEN Jing QI Ziyi LIU Mingxin School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004P.R.China Hebei Key Laboratory of Virtual Technology and System Integration Qinhuangdao 066004P R.China Hebei Key Laboratory of Software Engineering Qinhuangdao 066004P R.China College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang 524088P.R.China

In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this *** is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information ***,the propagation probability between nodes is calculated by the improved degree estimation ***,the weighted cascade model(WCM) based on static social network is not suitable for temporal social ***,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node *** combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it ***,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.

关键词： neighbor node influence time sequential social network influence maximization(IM) information propagation model

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Research on the Remaining Useful Life Prediction Model of Complex Equipment 4

Research on the Remaining Useful Life Prediction Model of Co...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Tang, Lei Tang, Yifei Zhang, Junhao Liu, Mengjuan Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Glasgow College University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331541729

With the rapid advancement of artificial intelligence technology, the development of intelligent manufacturing has become an inevitable trend. Utilizing AI technology to ensure the safe operation of factories is a crucial aspect of smart factories. In particular, research focused on fault diagnosis and remaining useful life (RUL) prediction models based on machine learning has garnered significant attention from both academia and industry. This paper explores RUL prediction models for complex equipment, introduces four representative models, and analyzes their performance using two benchmark datasets, C-MAPSS and N-CMAPSS. Finally, the paper discusses the future research and application trends of artificial intelligence technology in complex equipment operation and maintenance. © 2024 IEEE.

关键词： Smart manufacturing

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Enhancing building pattern recognition through multi-scale data and knowledge graph:a case study of C-shaped patterns

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International Journal of Digital Earth 2023年第1期16卷 3860-3881页

作者： Zhiwei Wei Wenjia Xu Yi Xiao Mi Shu Lu Cheng Yang Wang Chunbo Liu Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China The Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China School of Software Engineering Shenzhen Institute of Information TechnologyShenzhenPeople’s Republic of China Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China School of Geography Science and Geomatics Suzhou University of Science and TechnologySuzhouPeople’s Republic of China

Building pattern recognition is important for understanding urban forms,automating map generalization,and visualizing 3D city ***,current approaches based on object-independent methods have limitations in capturing all visually aware patterns due to the part-based nature of human ***,these approaches also suffer from inefficiencies when applying proximity graph *** address these limitations,we propose a framework that leverages multi-scale data and a knowledge graph,focusing on recognizing C-shaped building *** first employ a specialized knowledge graph to represent the relationships between buildings within and across various ***,we convert the rules for C-shaped pattern recognition and enhancement into query conditions,where the enhancement refers to using patterns recognized at one scale to enhance pattern recognition at other ***,rule-based reasoning is applied within the constructed knowledge graph to recognize and enrich C-shaped building *** verify the effectiveness of our method using multi-scale data with three levels of detail(LODs)collected from AMap,and our method achieves a higher recall rate of 26.4%for LOD1,20.0%for LOD2,and 9.1%for LOD3 compared to existing methods with similar ***,also achieve recognition efficiency improvements of 0.91,1.37,and 9.35 times,respectively.

关键词： Building pattern recognition urban form knowledge graph rule-based reasoning

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Less Yet Robust: Crucial Region Selection for Scene Recognition

Less Yet Robust: Crucial Region Selection for Scene Recognit...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Jianqi Wang, Mengxuan Wang, Jingyao Si, Lingyu Zheng, Changwen Xu, Fanjiang National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Scene recognition, particularly for aerial and underwater images, often suffers from various types of degradation, such as blurring or overexposure. Previous works that focus on convolutional neural networks have been shown to be able to extract panoramic semantic features and perform well on scene recognition tasks. However, low-quality images still impede model performance due to the inappropriate use of high-level semantic features. To address these challenges, we propose an adaptive selection mechanism to identify the most important and robust regions with high-level features. Thus, the model can perform learning via these regions to avoid interference. implement a learnable mask in the neural network, which can filter high-level features by assigning weights to different regions of the feature matrix. We also introduce a regularization term to further enhance the significance of key high-level feature regions. Different from previous methods, our learnable matrix pays extra attention to regions that are important to multiple categories but may cause misclassification and sets constraints to reduce the influence of such regions. This is a plug-and-play architecture that can be easily extended to other methods. Additionally, we construct an Underwater Geological Scene Classification dataset to assess the effectiveness of our model. Extensive experimental results demonstrate the superiority and robustness of our proposed method over state-of-the-art techniques on two datasets. © 2025 IEEE.

关键词： high-level semantic features remote sensing robust method Scene recognition underwater images

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