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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Wang, Juan Guo, Maozu School of Computer Science Inner Mongolia University Hohhot010021 China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

ISBN: (纸本)9783030325909

Phylogenetic networks are used in biology to describe reticulate or non-treelike evolution events. Thus, constructing phylogenetic networks is essential for the research on the evolution of species. IGNet is a web-based server for on-line construction of rooted phylogenetic networks from rooted phylogenetic trees based on an incompatible graph. Two methods, and BIMLR, were introduced and implemented in IGNet to help users construct rooted phylogenetic networks rapidly and efficiently. Besides the on-line computation service, the IGNet also provides downloadable Java programs of and BIMLR for off-line construction of rooted phylogenetic networks. IGNet is availability from http://***. © 2020, Springer Nature Switzerland AG.

关键词： Biology

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Sub-Picosecond-Level Laser-Based Underwater Transfer of Radio Frequency with Optical Phase Compensation 20

Sub-Picosecond-Level Laser-Based Underwater Transfer of Radi...

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20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Pan, Bisong Li, Haopengyu Hou, Dong Zhao, Jianye Univ. of Electronic Science and Technology of China School of Automation Engineering Chengdu China Peking University College of Elec. Engineering and Computer Science Beijing China

ISBN: (纸本)9781728181417

We demonstrated a sub-picosecond laser-based underwater frequency transfer with an optical phase compensation. With this transfer technique, a highly-stable 500 MHz radio-frequency (RF) signal was disseminated over a 5-m underwater link for 5000 s, and the characteristic of the timing fluctuation and instability for the transfer was analyzed and measured. The experimental results show the total root-mean-square (RMS) timing fluctuation of the transferred RF signal with compensation is about 162 fs with a fractional frequency instability on the order of 2.8 × 10-13 at 1 s and 2.7 × 10-16 at 1000 s. The laser-based underwater frequency transfer proposed in this paper has a potential application of transferring atomic clock in water environment as its instability is less than the currently-used commercial Cs or H-master clocks. © 2020 IEEE.

关键词： Stability

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Windfix: Harnessing the Power of Self-Supervised Learning for Versatile Imputation of Offshore Wind Speed Time Series

SSRN

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SSRN 2023年

作者： Chen, Yaoran Zhang, Dan Cao, Leilei Cai, Candong Kuang, Limin Peng, Yan Pu, Huayan Wu, Chuhan Zhou, Dai Cao, Yong Institute of Artificial Intelligence Collaborative Innovation Center for the Marine Artificial Intelligence Shanghai University Shanghai200444 China School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Engineering Research Center of Unmanned Intelligent Marine Equipment Ministry of Education Shanghai200444 China School of Software Northwestern Polytechnical University Shannxi Xi’an710072 China School of Computer Engineering and Science Shanghai University 200444 China Department of Electronic Engineering Tsinghua University Beijing100084 China School of Naval Architecture Ocean & Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

The accurate prediction of offshore wind speed is crucial for effective wind energy management. Deep learning models, utilizing large-scale wind speed time series, have emerged as a prominent approach for this task. However, the availability of wind history and other oceanic data are often capriciously incomplete, with intermittent gaps in the time series due to transmission difficulties or measurement device malfunctions. This presents a significant challenge for both data preparation and model training. In this paper, a self-supervised model, WindFix, is proposed for versatile imputation of offshore wind time series using meteorological features from both the self-spot and neighboring spots. The numerical results validated on wind dataset from Pacific Ocean show that the model can serve for various missing types with a mean squared error around 0.002 for the simplest continue-missing case. The comprehensive analysis also provided deep explanations on the different patterns occurring in various imputation types, attributing to the allocation of attention weights on features. © 2023, The Authors. All rights reserved.

关键词： Time series

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Pyramid deconvolution net: Breast cancer detection using tissue and cell encoding information 4

Pyramid deconvolution net: Breast cancer detection using tis...

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4th International Conference on Big Data Research, ICBDR 2020

作者： Sui, Dong Guo, Maozu Zhang, Yue Zhang, Lei School of Electrical and Information Engineering Beijing China University of Civil Engineering and Architecture Beijing China College of Computer Science and Technology Harbin Engineering University Harbin China Diagnostic Radiology and Nuclear Medicine University of Maryland BaltimoreMD United States

ISBN: (纸本)9781450387750

Accurate diagnosis of breast cancer lesions from whole slide images (WSIs) is crucial for pathologist, since the results are associated with a certain status of breast cancer development. Until now, it is not yet possible to detect all of the cancerous areas in a WSIs. This limitation leads to mistakes or low detection precision in most of the approaches based on deep learning. In this paper, we propose an automatic cancer lesions detection approach using pyramid deconvolution network (PDN) for multi-level and multi-scale H&E stained breast pathological WSIs. Our workflow integrates tissue and cell level information for the cancerous region detection, and this is neglected by state-of-the-art methods. The high-level cancerous regions (macro) are obtained by deconvolution neuron network feature map from level 3 to level 9 in WSIs. The low-level cancer cells and region analysis pipeline is designed for detecting smaller region micro an ITCs from level 0. The results demonstrated that our workflow greatly improved the performance compared with those only using tissue level information. © 2020 ACM.

关键词： Deep learning

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A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges

arXiv

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arXiv 2025年

作者： Ding, Lei Hong, Danfeng Zhao, Maofan Chen, Hongruixuan Li, Chenyu Deng, Jie Yokoya, Naoto Bruzzone, Lorenzo Chanussot, Jocelyn Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Information Engineering University Zhengzhou China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Southeast University Nanjing211189 China Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Information Engineering and Computer Science University of Trento Trento38123 Italy Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France

In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sensing Images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited due to the diverse input data and the applicational context. For example, the collected RSIs can be time-series observations, and more informative results are required to indicate the time of change or the specific change category. Moreover, training a Deep Neural Network (DNN) requires a massive amount of training samples, whereas in many cases these samples are difficult to collect. To address these challenges, various specific CD methods have been developed considering different application scenarios and training resources. Additionally, recent advancements in image generation, self-supervision, and visual foundation models (VFMs) have opened up new approaches to address the’data-hungry’ issue of DL-based CD. The development of these methods in broader application scenarios requires further investigation and discussion. Therefore, this article summarizes the literature methods for different CD tasks and the available strategies and techniques to train and deploy DL-based CD methods in sample-limited scenarios. We expect that this survey can provide new insights and inspiration for researchers in this field to develop more effective CD methods that can be applied in a wider range of contexts. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Frecast of civil Aviation Unsafe Events Rate Using Grey-Buffer Operator-Markov Chain Method

Frecast of Civil Aviation Unsafe Events Rate Using Grey-Buff...

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International Conference on Transportation and Development 2020: Transportation Safety, ICTD 2020

作者： Guo, Jiuxia Gao, Ziliang Wang, Yinhai Computer Science and Engineering College Univ. of Electronic Science and Technology of China Chengdu China Associate of Air Traffic Management Bureau Caac Beijing China Federal Region 10 Civil and Environmental Engineering Univ. of Washington SeattleWA United States

ISBN: (纸本)9780784483145

civil aviation safety is one of the crucial problems of the aviation industry. Air traffic management as a core part of civil aviation is facing massive pressure of safety operation. Accurately evaluating unsafe events rate is essential to improving air traffic safety. This paper proposed a grey-buffer operator-Markov chain method to forecast the civil aviation unsafe events rate. It combined the grey system and Markov chain. When predicting the civil aviation unsafe events rate, the actual data samples are few, discrete, and random fluctuation, which often causes more significant relative errors and worse accuracy using the traditional grey model. However, the average weakening buffer operator is an excellent method to process the raw data. According to the distribution of relative value, we can build the prediction model, combining with the characteristics of Markov forecast volatile data sequence. Finally, this method is applied to predict the unsafe events rate of civil aviation. The experimental result shows that this method not only reduces the relative error of predicted value but also improves the prediction precision of the model. © 2020 American Society of civil Engineers.

关键词： Forecasting

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Software Defect Prediction via Deep Belief Network

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Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

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Rockburst prediction using artificial intelligence techniques:A review

岩石力学通报（英文）

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岩石力学通报（英文） 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Lunar Resources

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Reviews in Mineralogy and Geochemistry 2024年 89卷 829-868页

作者： Crawford, Ian A. Anand, Mahesh Barber, Simeon Cowley, Aidan Crites, Sarah Fa, Wenzhe Flahaut, Jessica Gaddis, Lisa R. Greenhagen, Ben Haruyama, Junichi Hurley, Dana Mcleod, Claire L. Morse, Andrew Neal, Clive R. Sargeant, Hannah Sefton-Nash, Elliot Tartèse, Romain Dept. of Earth and Planetary Sciences Birkbeck College University of London United Kingdom Centre for Planetary Sciences at UCL/Birkbeck London United Kingdom Planetary and Space Sciences School of Physical Sciences The Open University United Kingdom Dept. of Earth Science The Natural History Museum London United Kingdom European Space Agency European Astronaut Centre Linder Höhe Cologne Germany Dept. of Solar System Science Institute of Space and Astronautical Science Aerospace Exploration Agency Sagamihara Japan Institute of Remote Sensing and Geographical Information System School of Earth and Space Sciences Peking University Beijing China Centre de Recherches Pétrographiques et Géochimiques CNRS Université de Lorraine France Lunar and Planetary Institute HoustonTX United States Johns Hopkins Applied Physics Laboratory LaurelMD United States Dept. of Geology and Environmental Earth Science Miami University OxfordOH United States Dept of Civil & Environmental Engineering and Earth Sciences Univ. of Notre Dame Notre DameIN United States European Space Agency ESTEC Noordwijk Netherlands Dept. of Earth and Environmental Sciences University of Manchester Manchester United Kingdom

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Effect of Different Coarse Aggregate Sizes on the Propagation Characteristics of Acoustic Emission Waves in Concrete

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Journal of Performance of Constructed Facilities 2025年第4期39卷

作者： Yu, Aiping Li, Xiuxin Cheng, Zichen Liu, Liyuan Shi, Jinxu Fu, Feng College of Civil Engineering Guilin Univ. of Technology Guilin541004 China Dept. of Civil Engineering School of Mathematics Computer Science and Engineering City Univ. of London Northampton Square LondonEC1V 0HB United Kingdom

In order to develop a new technology to effectively detect the sizes of the aggregate in existing concrete structure, this study on the influence of coarse aggregate sizes on acoustic emission (AE) signals was performed. The specific surface areas of five coarse aggregate sizes (with the same mass) of 4.75-9.5 mm, 9.5-16 mm, 16-19 mm, 19-26.5 mm, and 26.5-31.5 mm were measured by the slurry wrapping method to calculate their effects on the three-phase microscopic components of concrete. AE monitoring and parametric analysis were conducted on concrete samples with different coarse aggregate sizes by adopting the method of active-passive joint AE monitoring. This study also explores the influence of excitation frequencies and coarse aggregate sizes on the parameter variation of elastic waves in concrete. The results show that AE signals in the concrete will display high-frequency and low-frequency interconversion characteristics, and the degree of diffraction, refraction, scattering, and energy attenuation of AE signals in concrete samples are highly correlated to coarse aggregate sizes. The energy difference index (EDI) values are most affected by elastic waves with AE signals of 50 kHz, followed by 100 kHz, and the least affected is 150 kHz. Based on the proportion of the wavelet packet energy, a relationship model can be established between the specific surface area of coarse aggregate and its evaluation index, which can quantitatively evaluate the influence of coarse aggregate size on AE elastic waves at three excitation frequencies, and provide a theoretical basis and data support for optimizing the algorithm of the AE system to locate the damage source. © 2025 American Society of civil Engineers.

关键词： Acoustic emissions

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