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Knowledge System,Ontology,and Knowledge Graph of the Deep-Time Digital Earth(DDE): Progress and Perspective

Journal of Earth science 2023年第5期34卷 1323-1327页

作者： Xiumian Hu Yiwei Xu Xiaogang Ma Yunqiang Zhu Chao Ma Chao Li Hairong Lü Xinbing Wang Chenghu Zhou Chengshan Wang School of Earth Sciences and Engineering Nanjing UniversityNanjing 210023China State Key Laboratory of Palaeobiology and Stratigraphy Nanjing Institute of Geology and Palaeontology and Center for Excellence in Life and PaleoenvironmentChinese Academy of SciencesNanjing 210008China Department of Computer Science University of IdahoMoscow ID 83844USA Institute of Geographical Science and Natural Resources Chinese Academy of SciencesBeijing 100101China State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation&Institute of Sedimentary Geology&College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu 610059China IUGS Deep-Time Digital Earth Research Center of Excellence Suzhou 215101China Department of Automation Tsinghua UniversityBeijing 100084China School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesBeijing 100083China

THE USE OF KNOWLEDGE GRAPH IN NATURAL science Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).

关键词： DDE Google Web Semantic

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Audio-Visual Target Speaker Extraction With Selective Auditory Attention

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 797-811页

作者： Ruijie Tao Xinyuan Qian Yidi Jiang Junjie Li Jiadong Wang Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Technology University of Science and Technology Beijing Beijing China Chinese University of Hong Kong Shenzhen China Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China University of Bremen Bremen Germany

Audio-visual target speaker extraction (AV-TSE) aims to extract the specific person's speech from the audio mixture given auxiliary visual cues. Previous methods usually search for the target voice through speech-lip synchronization. However, this strategy mainly focuseson the existence of target speech, while ignoring the variations of the noise characteristics, i.e., interference speaker and the background noise. That may result in extracting noisy signals from the incorrect sound source in challenging acoustic situations. To this end, we propose a novel selective auditory attention mechanism, which can suppress interference speakers and non-speech signals to avoid incorrect speaker extraction. By estimating and utilizing the undesired noisy signal through this mechanism, we design an AV-TSE framework named Subtraction-and-ExtrAction network (SEANet) to suppress the noisy signals. We conduct abundant experiments by re-implementing three popular AV-TSE methods as the baselines and involving nine metrics for evaluation. The experimental results show that our proposed SEANet achieves state-of-the-art results and performs well for all five datasets.

关键词： Noise measurement Lips Visualization Synchronization Noise Correlation Data mining Speech processing Interference Speech recognition

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Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian systems:Tackling Multiplicative Noises and Missing Measurements

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1127-1138页

作者： Shaoying Wang Zidong Wang Hongli Dong Yun Chen Guoping Lu College of Science Shandong University of AeronauticsBinzhou 256603China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590 Department of Computer Science Brunel University LondonUxbridge UB83PHUK Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318China School of Automation Hangzhou Dianzi UniversityHangzhou 310018China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission *** multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary *** attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance *** this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented ***,we analyze statistical properties of augmented noises as well as high-order moments of certain random *** the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain ***,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

关键词： filtering quadratic bounds

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Use Coupled LSTM Networks to Solve Constrained Optimization Problems

Use Coupled LSTM Networks to Solve Constrained Optimization ...

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作者： Chen, Zheyu Leung, Kin K. Wang, Shiqiang Tassiulas, Leandros Chan, Kevin Towsley, Don Imperial College London Department of Computing LondonSW7 2AZ United Kingdom Ibm T.J. Watson Research Center Yorktown HeightsNY10598 United States Yale University Institute for Network Science Electrical Engineering Department New HavenCT06520 United States Devcom Army Research Laboratory Army Research Directorate AdelphiMD20783 United States University of Massachusetts Department of Computer Science AmherstMA01003 United States

Gradient-based iterative algorithms have been widely used to solve optimization problems, including resource sharing and network management. When system parameters change, it requires a new solution independent of the previous parameter settings from the iterative methods. Therefore, we propose a learning approach that can quickly produce optimal solutions over a range of system parameters for constrained optimization problems. Two Coupled Long Short-Term Memory networks (CLSTMs) are proposed to find the optimal solution. The advantages of this framework include: (1) near-optimal solution for a given problem instance can be obtained in few iterations during the inference, (2) enhanced robustness as the CLSTMs can be trained using system parameters with distributions different from those used during inference to generate solutions. In this work, we analyze the relationship between minimizing the loss functions and solving the original constrained optimization problem for certain parameter settings. Extensive numerical experiments using datasets from Alibaba reveal that the solutions to a set of nonconvex optimization problems obtained by the CLSTMs reach within 90% or better of the corresponding optimum after 11 iterations, where the number of iterations and CPU time consumption are reduced by 81% and 33%, respectively, when compared with the gradient descent with momentum. © 2015 IEEE.

关键词： Gradient methods

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Competitive Perimeter Defense of Conical Environments

Competitive Perimeter Defense of Conical Environments

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IEEE Conference on Decision and Control

作者： Shivam Bajaj Eric Torng Shaunak D. Bopardikar Alexander Von Moll Isaac Weintraub Eloy Garcia David W. Casbeer Department of Electrical and Computer Engineering Michigan State University Department of Computer Science and Engineering Michigan State University Control Science Center Air Force Research Laboratory

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider a perimeter defense problem in a planar conical environment in which a single vehicle, having a finite capture radius, aims to defend a concentric perimeter from mobile intruders. The intruders are arbitrarily released at the circumference of the environment and they move radially toward the perimeter with fixed speed. We present a competitive analysis approach to this problem by measuring the performance of multiple online algorithms for the vehicle against arbitrary inputs, relative to an optimal offline algorithm that has information about entire input instance in advance. In particular, we establish two necessary conditions on the parameter space to guarantee (i) finite competitiveness of any algorithm and (ii) a competitive ratio of at least 2 for any algorithm. We then design and analyze three online algorithms and characterize parameter regimes in which they have finite competitive ratios. Specifically, our first two algorithms are provably 1, and 2-competitive, respectively, whereas our third algorithm exhibits different competitive ratios in different regimes of problem parameters. Finally, we provide a numerical plot in the parameter space to reveal additional insights into the relative performance of our algorithms.

关键词： Atmospheric measurements Aerospace electronics Particle measurements

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3D direct printing of mechanical and biocompatible hydrogel meta-structures

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Bioactive Materials 2022年第4期7卷 48-55页

作者： Lei Zhang Wenhan Lee Xinhao Li Yanhui Jiang Nicholas Xuanlai Fang Guohao Dai Yongmin Liu Department of Mechanical&Industrial Engineering Northeastern UniversityBostonMA02115United States State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunming650500China Department of Bioengineering Northeastern UniversityBostonMA02115United States Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA02139United States Department of Electrical and Computer Engineering Northeastern UniversityBostonMA02115United States

Direct Ink Writing(DIW)has demonstrated great potential as a versatile method to 3D print multifunctional *** this work,we report the implementation of hydrogel meta-structures using DIW at room temperature,which seamlessly integrate large specific surface areas,interconnected porous characteristics,mechanical toughness,biocompatibility,and water absorption and retention *** but hydrophobic polymers and weakly crosslinked nature-origin hydrogels form a balance in the self-supporting ink,allowing us to directly print complex meta-structures without sacrificial materials and heating ***,the mixed bending or stretching of symmetrical re-entrant cellular lattices and the unique curvature patterns are combined to provide little lateral expansion and large compressive energy absorbance when external forces are applied on the printed *** addition,we have successfully demonstrated ear,aortic valve conduits and hierarchical *** anticipate that the reported 3D meta-structured hydrogel would offer a new strategy to develop functional biomaterials for tissue engineering applications in the future.

关键词： Direct ink writing Gyroid meta-structure Mechanical-functional integration Naturally derived hydrogel

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Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water systems in Smart Grids

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engineering 2021年第8期7卷 1101-1114页

作者： Junjie Hu Huayanran Zhou Yihong Zhou Haijing Zhang Lars Nordströmd Guangya Yang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University) Beijing 102206China Division of Electric Power and Energy Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholm 10044Sweden Center for Electric Power and Energy Department of Electrical Engineering Technical University of DenmarkKgs Lyngby 2800Denmark

With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency *** flexibility of demand response(DR)resources has become a valuable solution to this ***,existing research indicates that problems on flexibility prediction of DR resources have not been *** study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate *** prediction can generate the size and maintenance time of the aggregated *** accuracy of the flexibility prediction results was verified through simulations of case *** simulation results show that under different maintenance times,the size of the flexibility *** proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

关键词： Load flexibility Electric vehicles Domestic hot water system Temporal convolution network-combined transformer Deep learning

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Optimal Power Control Based-Metaheuristic Algorithm Based Variable Speed Wind Energy System of DFIG

Optimal Power Control Based-Metaheuristic Algorithm Based Va...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Boussaid Ibrahim Harrouz Abdelkader Merahi Farid Ibrahim Yaichi Ilhami Colak Korhan Kayisli Virgil Dumbrava Electrical Engineering Department Laboratory of sustainable development and computer science (LDDI) University of Adrar Adrar Algeria Laboratory of sustainable development and computer science (LDDI) University of Adrar Adrar Algeria Electrical Engineering Department Automatic Laboratory of Setif University of Ferhat Abbas Setif1 Setif Algeria Department of Electrical Electronics Eng. Faculty of Engineering Architecture Nisantasi University Istanbul Turkey Department of Electric Power Systems Polytechnic University of Bucharest Bucharest Romania

This article presents a novel method for controlling a doubly fed induction generator (DFIG) wind energy system in the presence of variable wind speeds. In order to achieve decoupling control of the active and reactive power, the rotor-side converter (RSC) of the DFIG is controlled using field-oriented control (FOC). The system is very complicated and nonlinear, making it challenging to adjust the PI gains appropriately. The present research proposes the utilization of the Grey Wolf Optimization (GWO) algorithm as an approach to optimize the gains of a Proportional-Integral (PI) regulator. The simulation results demonstrate that the GWO-PI provides a lower criterion value and an improved response than the conventional PI.

关键词：

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ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning 37

ALIM: Adjusting Label Importance Mechanism for Noisy Partial...

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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Xu, Mingyu Lian, Zheng Feng, Lei Liu, Bin Tao, Jianhua The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Automation Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781713899921

Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows the ground-truth label may not be in the candidate label set. To address this challenging problem, most of the existing works attempt to detect noisy samples and estimate the ground-truth label for each noisy sample. However, detection errors are unavoidable. These errors can accumulate during training and continuously affect model optimization. To this end, we propose a novel framework for noisy PLL with theoretical interpretations, called "Adjusting Label Importance Mechanism (ALIM)". It aims to reduce the negative impact of detection errors by trading off the initial candidate set and model outputs. ALIM is a plug-in strategy that can be integrated with existing PLL approaches. Experimental results on multiple benchmark datasets demonstrate that our method can achieve state-of-the-art performance on noisy PLL. Our code is available at: https://***/zeroQiaoba/ALIM. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Benchmarking

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Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares 34

Algorithmic Stability of Heavy-Tailed Stochastic Gradient De...

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34th International Conference onAlgorithmic Learning Theory, ALT 2023

作者： Raj, Anant Barsbey, Melih Gürbüzbalaban, Mert Zhu, Lingjiong Şimşekli, Umut Coordinated Science Laboratory University of Illinois Urbana-Champaign Inria Ecole Normale Supérieure PSL Research University Paris France Department of Computer Engineering Bogaziçi University Istanbul Turkey Department of Management Science and Information Systems Rutgers University PiscatawayNJ United States Department of Mathematics Florida State University TallahasseeFL United States Inria CNRS Ecole Normale Supérieure PSL Research University Paris France

Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails have links to the generalization error. While these studies have shed light on interesting aspects of the generalization behavior in modern settings, they relied on strong topological and statistical regularity assumptions, which are hard to verify in practice. Furthermore, it has been empirically illustrated that the relation between heavy tails and generalization might not always be monotonic in practice, contrary to the conclusions of existing theory. In this study, we establish novel links between the tail behavior and generalization properties of stochastic gradient descent (SGD), through the lens of algorithmic stability. We consider a quadratic optimization problem and use a heavy-tailed stochastic differential equation (and its Euler discretization) as a proxy for modeling the heavy-tailed behavior emerging in SGD. We then prove uniform stability bounds, which reveal the following outcomes: (i) Without making any exotic assumptions, we show that SGD will not be stable if the stability is measured with the squared-loss x 7→ x2, whereas it in turn becomes stable if the stability is instead measured with a surrogate loss x 7→ |x|p with some p © 2023 A. Raj, M. Barsbey, M. Gürbüzbalaban, L. Zhu & U. Şimşekli.

关键词： Gradient methods

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