检索结果-内蒙古大学图书馆

Gossip protocol approach for a decentralized energy market with OPC UA client-server communication 50

Gossip protocol approach for a decentralized energy market w...

50. Jahrestagung der Gesellschaft fur Informatik, INFORMATIK 2020 - 50th Annual Conference of the German Informatics Society, INFORMATIK 2020

作者： Schindler, Josef Tellabi, Asmaa Waedt, Karl Friedrich-Alexander-Universitčt Erlangen-Nürnberg Faculty of Engineering Institute of Electrical Energy Systems Cauerstr. 4 Erlangen91058 Germany University of Siegen Faculty of Science and Engineering Chair for Data Communication Systems Hölderlinstraße 3 Siegen57068 Germany Framatome GmbH Erlangen Germany

ISBN: (纸本)9783885797012

Gossiping is a well-researched protocol that enables decentralized information sharing. Being comparable to viruses spreading in a biological population, such concepts of data sharing are also called epidemic protocol. Without wanting to be impious with respect to recent pandemics, we propose its usage to facilitate a peer-to-peer (P2P) market for sharing energy between flexible loads or generation units, respectively. Gossip algorithms have been proposed several times in the context of power sharing in transmission grids. Our main contribution is the integration of such scenario with OPC UA. Comprising security by design, good interoperability attributes, several, well-maintained stack implementations and a widespread usage in automation, it reveals to be an outstanding framework for the proposed use case that will be explained in the first sections. After describing underlying physical models and the setup scenario, we will compare the results of the scenario that was conducted on non-OPC UA modules and an OPC UA implementation. Mostly, the performance is questioned at the comparison, still some beneficial concepts of OPC UA can be highlighted in the conclusion: Security controls can be added to the system at the Application Layer where Attribute Based Access Control (ABAC) can be performed, which allows a fine granularity of privileges expressed for subjects (agents in the gossiping algorithms) and objects (energy related assets) via semi-formal security policies. Additionally, UA Discovery service allows for plug and play availability. Concluding, a framework for a very efficient large-area algorithm is presented here to be researched in further work. © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Access control

来源：评论

学校读者我要写书评

暂无评论

Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework

arXiv

引用

arXiv 2021年

作者： Khalil, Elias B. Vaezipoor, Pashootan Dilkina, Bistra Department of Mechanical & Industrial Engineering University of Toronto Canada Scale AI Research Chair in Data-Driven Algorithms for Modern Supply Chains Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Canada Department of Computer Science University of Southern California United States

In Mixed Integer Linear Programming (MIP), a (strong) backdoor is a "small" subset of an instance's integer variables with the following property: in a branch-and-bound procedure, the instance can be solved to global optimality by branching only on the variables in the backdoor. Constructing datasets of pre-computed backdoors for widely used MIP benchmark sets or particular problem families can enable new questions around novel structural properties of a MIP, or explain why a problem that is hard in theory can be solved efficiently in practice. Existing algorithms for finding backdoors rely on sampling candidate variable subsets in various ways, an approach which has demonstrated the existence of backdoors for some instances from MIPLIB2003 and MIPLIB2010. However, these algorithms fall short of consistently succeeding at the task due to an imbalance between exploration and exploitation. We propose BaMCTS, a Monte Carlo Tree Search framework for finding backdoors to MIPs. Extensive algorithmic engineering, hybridization with traditional MIP concepts, and close integration with the CPLEX solver have enabled our method to outperform baselines on MIPLIB2017 instances, finding backdoors more frequently and more efficiently. © 2021, CC BY.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

Quantitative comparison of the total focusing method, reverse time migration, and full waveform inversion for ultrasonic imaging

引用

Ultrasonics 2025年 155卷 107705页

作者： Tim Bürchner Simon Schmid Lukas Bergbreiter Ernst Rank Stefan Kollmannsberger Christian U. Grosse Technical University of Munich TUM School of Engineering and Design Chair of Computing in Civil and Building Engineering Arcisstrasse 21 Munich 80333 Germany Technical University of Munich TUM School of Engineering and Design Department of Materials Engineering Chair of Non-destructive Testing Franz-Langinger-Str. 10 Munich 81245 Germany Technical University of Munich Institute for Advanced Study Lichtenbergstrasse 2a Garching 85748 Germany Bauhaus-Universität Weimar Chair of Data Science in Civil Engineering Coudraystraße 13 Weimar 99423 Germany

Phased array ultrasound is a widely used technique in non-destructive testing. Using piezoelectric elements as both sources and receivers provides a significant gain in information and enables more accurate defect detection. The process is called full matrix capture when all source–receiver combinations are used. The total focusing method (TFM) exploits such datasets and relies on a delay and sum algorithm to sum up the signals on a pixel grid. However, TFM only uses the first arriving p-waves, making it challenging to size complex-shaped defects. By contrast, more advanced methods such as reverse time migration (RTM) and full waveform inversion (FWI) use full waveforms to reconstruct defects. Both methods compare measured signals with ultrasound simulations. While RTM identifies defects by convolving forward and backward wave fields once, FWI iteratively updates material models to reconstruct the actual distributions of material properties. This study compares TFM, RTM, and FWI for six specimens featuring circular defects or Y-shaped notches. The reconstructed results are first evaluated qualitatively by visual assessment, then different defect segmentation approaches are studied, and finally, they are evaluated quantitatively using metrics such as AUPRC, AUROC, and F1-score. The results show that a two-step FWI framework performs best in most of the six samples studied, both qualitatively and quantitatively.

关键词： Ultrasound Reconstruction Phased array Full matrix capture Wave simulation Total focusing method Reverse time migration Full waveform inversion

来源：评论

学校读者我要写书评

暂无评论

MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Guidelines for the computational testing of machine learning approaches to vehicle routing problems

arXiv

引用

arXiv 2021年

作者： Accorsi, Luca Lodi, Andrea Vigo, Daniele Department of Electrical Electronic and Information Engineering "G. Marconi" University of Bologna Italy Canada Excellence Research Chair in Data Science for Decision Making École Polytechnique de Montréal Canada Mila Quebec Artificial Intelligence Institute Canada CIRI ICT University of Bologna Italy

Despite the extensive research efforts and the remarkable results obtained on Vehicle Routing Problems (VRP) by using algorithms proposed by the Machine Learning community that are partially or entirely based on data-driven analysis, most of these approaches are still seldom employed by the Operations Research (OR) community. Among the possible causes, we believe, the different approach to the computational evaluation of the proposed methods may play a major role. With the current work, we want to highlight a number of challenges (and possible ways to handle them) arising during the computational studies of heuristic approaches to VRPs that, if appropriately addressed, may produce a computational study having the characteristics of those presented in OR papers, thus hopefully promoting the collaboration between the two communities. © 2021, CC BY-NC-SA.

关键词： Operations research

来源：评论

学校读者我要写书评

暂无评论

An Exact Method for (Constrained) Assortment Optimization Problems with Product Costs

arXiv

引用

arXiv 2021年

作者： Leitner, Markus Lodi, Andrea Roberti, Roberto Sole, Claudio Department of Operations Analytics Vrije Universiteit Amsterdam Netherlands Jacobs Technion-Cornell Institute Cornell Tech and Technion - IIT United States Department of Information Engineering University of Padova Italy Canada Excellence Research Chair in Data-Science for Real-time Decision-Making Polytechnique Montréal Canada

We study the problem of optimizing assortment decisions in the presence of product-specific costs when customers choose according to a multinomial logit model. This problem is NP-hard and approximate solutions methods have been proposed in the literature to obtain both lower and upper bounds in a tractable manner. We propose the first exact solution method for this problem and show that provably optimal assortments of instances with up to one thousand products can be found, on average, in about two tenths of a second. In particular, we propose a bounding procedure based on the approximation method of Feldman and Topaloglu (2015a) to provide tight lower and upper bounds at a fraction of their computing times. We show how these bounds can be used to effectively identify an optimal assortment. We also describe how to adapt our approach to handle cardinality or space/resource capacity constraints on the assortment as well as assortment optimization under a mixed-multinomial logit model. In both cases, our solution method provides significant computational boosts compared to exact methods from the literature. Copyright © 2021, The Authors. All rights reserved.

关键词： Approximation theory

来源：评论

学校读者我要写书评

暂无评论

LimeSoDa: A dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

arXiv

引用

arXiv 2025年

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number ( © 2025, CC BY-SA.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

引用

Geoderma 2025年 459卷

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany —Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence ‘Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara S

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Tractably modelling dependence in networks beyond exchangeability

arXiv

引用

arXiv 2020年

作者： Wu, Weichi Olhede, Sofia Wolfe, Patrick Center for Statistical Sciences Department of Industrial Engineering Tsinghua University China Institute of Mathematics Chair of Statistical Data Science EPFL Swaziland Departments of Statistics and Computer Science Purdue University United States

We propose a general framework for modelling network data that is designed to describe aspects of non-exchangeable networks. Conditional on latent (unobserved) variables, the edges of the network are generated by their finite growth history (with latent orders) while the marginal probabilities of the adjacency matrix are modeled by a generalization of a graph limit function (or a graphon). In particular, we study the estimation, clustering and degree behavior of the network in our setting. We determine (i) the minimax estimator of a composite graphon with respect to squared error loss;(ii) that spectral clustering is able to consistently detect the latent membership when the block-wise constant composite graphon is considered under additional conditions;and (iii) we are able to construct models with heavy-tailed empirical degrees under specific scenarios and parameter choices. This explores why and under which general conditions non-exchangeable network data can be described by a stochastic block model. The new modelling framework is able to capture empirically important characteristics of network data such as sparsity combined with heavy tailed degree distribution, and add understanding as to what generative mechanisms will make them *** Codes 62G05, 62R07, 62E20, 62G20, secondary 53C20 Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Livestock behaviour forecasting via generative artificial intelligence

引用

Smart Agricultural Technology 2025年 11卷

作者： Eckhardt, Regina Arablouei, Reza Ingham, Aaron McCosker, Kieren Bernhardt, Heinz Chair of Agricultural Systems Engineering TUM School of Life Sciences Technical University of Munich Freising Germany Data61 CSIRO Pullenvale QLD Australia Agriculture and Food CSIRO St Lucia QLD Australia Centre for Animal Science Queensland Alliance for Agriculture and Food Innovation The University of Queensland Gatton QLD Australia

Recent advancements in sensor technology and generative artificial intelligence (AI) are transforming precision livestock farming by enhancing behaviour monitoring and predictive analytics. This study examines the effectiveness of Transformer-type generative AI models in predicting cattle behaviour profiles and imputing missing data from collar accelerometer readings collected during two trials in Queensland, Australia, in 2022 and 2023, alongside climatic data. Each trial involved 60 cattle equipped with collars that classified six core behaviours: grazing, ruminating, walking, resting, drinking, and other over five-second time windows. Hourly behaviour profiles were constructed for each animal and experiment day by aggregating the behaviour predictions over every calendar hour, representing the time spent on each behaviour within each hour. Subsequently, four Transformer-type models (i.e., standard Transformer, Informer, Reformer, and Autoformer) were trained on the hourly behaviour profile data to predict behaviour profiles of the next 24 hours for each animal. Among the considered models, Autoformer showed the highest predictive accuracy when including climate data, achieving a mean absolute error (MAE) of <5.5 min, while the next best model had an MAE of approximately 6 min. For imputing missing data, the standard Transformer outperformed traditional imputation methods, with an MAE of <30 min over 24 hours, compared to 40 to 70 min for traditional methods (mean, median, and linear interpolation). These results highlight the potential of generative AI, particularly Autoformer and Transformer, to enhance predictive accuracy and data imputation in livestock management, thereby supporting regulatory guidance for data-driven decision-making and improved farming practices. © 2025

关键词： Accelerometer data Cattle behaviour data imputation Generative AI Precision agriculture

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：