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13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2023

作者： An, Hongyi Qiu, Zhonghui Liu, Xi Wang, Rongqiao Hu, Dianyin School of Energy and Power Engineering Beihang University Beijing China No.703 Research Institute of CSSC Harbin China Ship and Marine Engineering Special Equipment and Power System National Engineering Research Center Ship and Ocean Engineering Gas Turbine R&D and Testing Laboratory Harbin China Research Institute of Aero-Engine Beihang University Beijing China United Research Center of Mid-Small Aero-Engine Beijing China Beijing Key Laboratory of Aero-Engine Structure and Strength Beijing China

ISBN: (纸本)9781839539848

A reliability analysis study was conducted on the shock absorber used in a certain ship. Firstly, a multi-source uncertainty quantification model was established. Different forms of models are used to quantify the uncertainty of loads, geometric shapes, and material properties based on their characteristics. Then, combining Monte-Carlo method with BP neural network surrogate model, the relationship between input variables and output acceleration is established. Finally, based on the surrogate model, the response distribution and reliability of the shock absorber are obtained, which provides a reference for the subsequent structural analysis and optimization. © The Institution of Engineering & Technology 2023.

关键词： Monte Carlo methods

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Convex Risk Bounded Continuous-Time Trajectory Planning and Tube Design in Uncertain Nonconvex Environments

arXiv

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arXiv 2023年

作者： Jasour, Ashkan Han, Weiqiao Williams, Brian MIT Computer Science and Artificial Intelligence Laboratory United States

In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geometry. To address this problem, we provide a risk bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon. Risk is defined as the probability of collision with uncertain obstacles. Existing approaches to address risk bounded trajectory planning problems either are limited to Gaussian uncertainties and convex obstacles or rely on sampling-based methods that need uncertainty samples and time discretization. To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem. Risk contours are the set of all points in the uncertain environment with guaranteed bounded risk. The obtained deterministic optimization is, in general, nonlinear and nonconvex time-varying optimization. We provide convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem and obtain the continuous-time risk bounded trajectories without time discretization. The provided approach deals with arbitrary (and known) probabilistic uncertainties, nonconvex and nonlinear, static and dynamic obstacles, and is suitable for online trajectory planning problems. In addition, we provide convex methods based on sum-of-squares optimization to build the max-sized tube with respect to its parameterization along the trajectory so that any state inside the tube is guaranteed to have bounded risk. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Composition and Sources of Organic Aerosol in Two Megacities in Western China Using Complementary Mass Spectrometric and Statistical Techniques

ACS ES&T air

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ACS ES&T air 2024年第9期1卷 1053-1065页

作者： Tianqu Cui Manousos I Manousakas Qiyuan Wang Gaëlle Uzu Yufang Hao Peeyush Khare Lu Qi Yang Chen Yuemei Han Jay G Slowik Jean-Luc Jaffrezo Junji Cao André S H Prévôt Kaspar R Daellenbach PSI Center for Energy and Environmental Sciences Paul Scherrer Institute 5232 Villigen-PSI Switzerland. Key Laboratory of Aerosol Chemistry and Physics Institute of Earth Environment Chinese Academy of Sciences Xi'an 710061 China. Institut des Géosciences de l'Environnement CNRS UGA IRD Grenoble INP INRAE Grenoble 38000 France. Research Center for Atmospheric Environment Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China.

Over 300 daily PM filter samples were collected in two western Chinese megacities, Xi'an and Chongqing, from October 2019 to May 2020. Their aqueous extracts were nebulized simultaneously to an aerosol mass spectrometer (AMS) and a recently developed extractive electrospray ionization (EESI) mass spectrometer, for bulk and near-molecular organic aerosol (OA) composition, respectively. Carbonate was quantified using EESI and a total organic carbon analyzer to separate inorganic carbon from dust. Via isotopically-labelled internal standards and positive matrix factorization, seven water-soluble sources were quantified separately using the AMS- and EESI-based analyses, with consistent types, concentrations, and correlations. These include dust, solid fuel combustion (SFC)-related, nitrogen- (and sulfur-) containing, summer/winter oxygenated OAs, and a cigarette-related OA only in EESI. When accounting for water-solubility, SFC-related OAs were the largest (53%) sources in Chongqing, while dust (consisting of 77% OA and 23% carbonates) was the largest (30%) source in Xi'an. Overall, this study presents one of the first times that complementary mass spectrometric techniques independently resolved consistent OA sources-with added chemical information-over multiple seasons and locations of complex pollution. The methods and quantified sources are essential for subsequent chemical, modelling, and health studies, and policy making for air pollution mitigation.

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Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations 29

Reciprocal Learning of Knowledge Retriever and Response Rank...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Feng, Jiazhan Tao, Chongyang Li, Zhen Liu, Chang Shen, Tao Zhao, Dongyan Wangxuan Institute of Computer Technology Peking University China Center for Data Science Peking University China University of Technology Sydney Australia School of Artificial Intelligence Peking University China State Key Laboratory of Media Convergence Production Technology and Systems China

Grounding dialogue agents with knowledge documents has sparked increased attention in both academia and industry. Recently, a growing body of work is trying to build retrieval-based knowledge-grounded dialogue systems. While promising, these approaches require collecting pairs of dialogue context and the corresponding ground-truth knowledge sentences that contain the information regarding the dialogue context. Unfortunately, hand-labeling data to that end is time-consuming, and many datasets and applications lack such knowledge annotations. In this paper, we propose a reciprocal learning approach to jointly optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels. Specifically, the knowledge retriever uses the feedback from the response ranker as pseudo supervised signals of knowledge retrieval for updating its parameters, while the response ranker also receives the top-ranked knowledge sentences from knowledge retriever for optimization. Evaluation results on two public benchmarks show that our model can significantly outperform previous state-of-the-art methods. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Speech processing

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Designing Workflows for Materials Characterization

arXiv

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arXiv 2023年

作者： Kalinin, Sergei V. Ziatdinov, Maxim Ahmadi, Mahshid Ghosh, Ayana Roccapriore, Kevin Liu, Yongtao Vasudevan, Rama K. Institute for Advanced Materials and Manufacturing Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37830 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization. Synthesis of a new material is typically followed by a set of characterization methods aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multi-resolution structural imaging and structural and functional characterization. Some considerations for theory-experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupt the classical progression from ideation, orchestration, and execution stages of workflow development and necessitate development of universal frameworks for workflow design, including universal hyper-languages describing laboratory operation, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization, enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities, and empower distributed research. © 2023, CC BY.

关键词： Knowledge based systems

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Moisture Detection of Stored Rapid-Setting Cementitious Materials

Moisture Detection of Stored Rapid-Setting Cementitious Mate...

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2023 International Conference on Airfield and Highway Pavements

作者： Carruth, William D. Shannon, Jameson D. Carr, Harold T. Geotechnical and Structures Laboratory Us Army Engineer Research and Development Center VicksburgMS United States Information Technology Laboratory Us Army Engineer Research and Development Center VicksburgMS United States

ISBN: (数字)9780784484913

ISBN: (纸本)9780784484913

Storage of cementitious materials onsite is essential for rapid repair operations. However, materials may have limited shelf lives and are prone to degradation in the presence of moisture. Typical industry practice involves storing these materials inside specialized silos before distribution, which is likely impractical for most work environments. This paper investigates methods of storage to reduce moisture damage and to monitor moisture present in cementitious materials. Various techniques were evaluated to reduce moisture in storage containers, and large bulk sacks of rapid-setting materials were installed with sensors to monitor moisture. Two different sensors were tested for monitoring moisture: a standard soil moisture probe and an engineered radio frequency identification reader (RFID) moisture detector. Desiccant poles were found to be the most beneficial of the techniques tested in reducing humidity and removing water from the storage container. The RFID moisture detector was found to be able to detect moisture events better than the soil moisture sensor, which was unable to detect moisture events even when stored outside in rainy conditions. Recommendations for future storage conditions and monitoring are provided. This paper demonstrates the capability of moisture monitoring in cementitious material bulk containers and provides groundwork for further optimization of storage protocols. © 2023 Airfield and Highway Pavements 2023: Design, Construction, Condition Evaluation, and Management of Pavements - Selected Papers from the International Airfield and Highway Pavements Conference 2023. All rights reserved.

关键词： Radio frequency identification (RFID)

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Source-Free Unsupervised Domain Adaptation Fundus Image Segmentation via Entropy optimization and Anatomical Priors

Source-Free Unsupervised Domain Adaptation Fundus Image Segm...

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International Conference on Biomimetic Intelligence and Robotics & Medical Robotics Forum, ICBIR+MRF 2024

作者： Chen, Yijia Li, Jiapeng Yu, Haoze Qi, Lin Li, Yongchun College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China Shenyang Contain Electronic Technology Co. Ltd. Shenyang110167 China

This research focuses on fundus image segmentation within a source-free domain adaptation framework, where the availability of source images during the adaptation phase is limited due to privacy concerns. Although Source-Free Unsupervised Domain Adaptation (SFUDA) methods have seen significant innovative developments in recent years, they still face several challenges which include suboptimal performance due to substantial domain discrepancies, reliance on potentially noisy or inaccurate pseudo-labels during the adaptation process, and a lack of integration with domain-specific prior knowledge. To address these issues, this paper proposes a SFUDA framework via Entropy optimization and Anatomical Priors (EOAPNet). To alleviate the influence of the divergence between the source and target domains, EOAPNet primarily evaluates the uncertainty (i.e., entropy) of predictions on target domain data and improves the model by focusing on high-entropy pixels or regions. Additionally, a weak-strong augmentation mean-teacher scheme is introduced in EOAPNet, which can enhance the accuracy of pseudo-labels and reduce error propagation. Thirdly, by integrating an anatomical knowledge-based class ratio prior into the overall loss function in the form of a Kullback-Leibler (KL) divergence, EOAPNet also incorporates expert domain knowledge. EOAPNet yields comparable results to several state-of-the-art adaptation techniques in experiments on two retinal image segmentation datasets involving the RIM-ONE-r3 and Drishti-GS datasets. © 2024 The Authors.

关键词： Domain Knowledge

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Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in complex Networks

arXiv

引用

arXiv 2024年

作者： Liu, Yi Tang, Xiaoan Pedrycz, Witold Zhang, Qiang The Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education The School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey

Various types of promising techniques have come into being for influence maximization whose aim is to identify influential nodes in complex networks. In essence, real-world applications usually have high requirements on the balance between time complexity and accuracy of influential nodes identification. To address the challenges of imperfect node influence measurement and inefficient seed nodes selection mechanism in such class of foregoing techniques, this article proposes a novel approach called Cost-Effective Community-Hierarchy-Based Mutual Voting for influence maximization in complex networks. First, we develop a method for measuring the importance of different nodes in networks based on an original concept of Dual-Scale Community-Hierarchy Information that synthesizes both hierarchy structural information and community structural information of nodes. The community structural information contained in the nodes is measured by a new notion of Hierarchical-Community Entropy. Second, we develop a method named Cost-Effective Mutual-Influence-based Voting for seed nodes selection. Hereinto, a low-computational-cost mutual voting mechanism and an updating strategy called Lazy Score Updating Strategy are newly constructed for optimizing the selecting of seed nodes. Third, we develop a balance index to evaluate the performance of different methods in striking the tradeoff between time complexity and the accuracy of influential nodes identification. Finally, we demonstrate the approach performance over ten public datasets. The extensive experiments show that the proposed approach outperforms 16 state-of-the-art techniques on the balance between time complexity and accuracy of influential nodes identification. Compared with the method with the second highest value of the balance index, our approach can be improved by at most 9.29%. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

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Quantitative Analysis of Regression-Based Temperature Dynamics Models for Households with A/C Units Subject to Unknown Disturbances

Quantitative Analysis of Regression-Based Temperature Dynami...

引用

Human-Computer Interaction, optimization and Robotic Applications (HORA), International Congress on

作者： Nikola Hure Mario Vašak University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Renewable Energy Systems Zagreb Croatia

This paper focuses on the identification of thermodynamic models for temperature prediction in households. The proposed temperature dynamics model falls under the class of Linear Time-Invariant (LTI) models, making it suitable for model predictive control synthesis. However, the presence of significant and variable thermal disturbances in households adds complexity to the identification process. The performance of various prediction error methods, such as ARX, ARARMAX, and BJ models, along with simplified models incorporating persistent disturbance excitation, is analyzed. The findings highlight the substantial impact of unknown disturbances on temperature predictions, emphasizing the crucial need for accurate prediction of these disturbances for effective household heating and cooling planning. The identification and evaluation of model performance measures are conducted using two months of experimental data collected from five households. This study contributes to understanding of the significance of addressing unknown disturbances and variability in thermodynamic model identification for temperature prediction.

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Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment

Towards Efficient Trajectory Generation for Ground Robots be...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jingping Wang Long Xu Haoran Fu Zehui Meng Chao Xu Yanjun Cao Ximin Lyu Fei Gao State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Huzhou Institute of Zhejiang University Huzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Applicaion Innovation Laboratory Huawei Technologies Co. Ltd.

With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in $\mathbb{R}^{3}$ such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm.

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