检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Weber, Moritz Turowski, Marian Çakmak, Hüseyin K. Mikut, Ralf Kühnapfel, Uwe Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms by far the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Cyber-Physical Event Reasoning for Distributed Energy Resources on the Case of a Pv-Battery System

SSRN

引用

SSRN 2023年

作者： Müller, Nils Bao, Kaibin Heussen, Kai Wind and Energy Systems Department Technical University of Denmark Building 330 Risø campus Roskilde4000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Campus North Building 445 Eggenstein-Leopoldshafen76344 Germany

The widespread adoption of internet-connected and remotely controllable solar plants, battery storages, and other electric devices renders coordinated cyber-physical attacks against such distributed energy resources (DERs) an emerging risk for power systems. Effective incident response can be facilitated by online DER monitoring providing real-time information on event root causes and physical impacts. Such online event identification is challenged by the lack of historical attack observations, and emergence of new attack strategies. The cyber-physical event reasoning system CyPhERS provides real-time information on both known and unknown attack types in form of informative and interpretable event signatures, without need for historical attack samples. However, CyPhERS has been demonstrated on a generic cyber-physical laboratory testbed, considering human evaluation of event signatures. This work extends applicability of CyPhERS to DER monitoring on the case of a real PV-battery system affected by several cyber and cyber-physical attack types. In this context, an automated signature evaluation system is realized, and the concept adapted to specificities of DERs, such as weather and consumer-induced volatility. The results demonstrate that CyPhERS can be applied for online DER monitoring, providing information on root causes and physical impacts of both known attack strategies and unknown event types in an automated fashion. © 2023, The Authors. All rights reserved.

关键词： Energy resources

来源：评论

学校读者我要写书评

暂无评论

Data Fusion and State Estimation Using Belief Propagation in Gas Distribution Networks

Data Fusion and State Estimation Using Belief Propagation in...

引用

International Universities Power Engineering Conference (UPEC)

作者： Goekhan Demirel Steven de Jongh Felicitas Mueller Thomas Leibfried Institute of Electrical Energy Systems and High Voltage Technology (IEH) Karlsruhe Institute of Technology Karlsruhe Germany Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

ISBN: (数字)9781665455053

ISBN: (纸本)9781665455060

This paper proposes a solution to the state estimation problem in gas networks using the distributed belief propagation (BP) algorithm. Power system identification applications require precise and robust state estimatiors as well as various sensor information. Compared to augmenting the power system with a very large number of sensors, a limited number of sensors and probabilistic graphical models can be used to infer the system state and reduce hardware investments. A novel BP algorithm propagates the pressure quantities at nodes in the gas network based on pressure manometer signals and applies a correction based on the information of neighboring nodes in the fusion step by using additional supporting sensors. Finally, the data fusion algorithm is demonstrated for a 14-node gas distribution network based on real data. This paper presents a novel algorithm aimed at tackling the traditional weighted least squares method to validate the developed novel approach in order to highlight the advantage of the distributed inference algorithm over traditional methods.

关键词： Power engineering Graphical models Data integration Distribution networks Probabilistic logic Inference algorithms Power systems

来源：评论

学校读者我要写书评

暂无评论

Design-for-manufacture of a dual-chamber nozzle for hydrodynamic focusing of the Aerosol-on-Demand jet-printhead

Design-for-manufacture of a dual-chamber nozzle for hydrodyn...

引用

Design, Test, Integration and Packaging of MEMS/MOEMS

作者： Martin Ungerer Ulrich Gengenbach Karanvir Singh Grewal Achim Wenka Ingo Sieber Institute for Automation and Applied Informatics (IAI) Karlsruhe Institiute of Technology (KIT) Karlsruhe Germany Department of Mechanical Engineering Punjab Engineering College Chandigarh India Institute for Micro Process Engineering (IMVT) Karlsruhe Institiute of Technology (KIT) Karlsruhe Germany

In this paper, we present the design of a dual-chamber nozzle (DCN) for a newly developed concept of a printhead for Aerosol-on-Demand (AoD) jet-printing using fluid dynamic modelling and simulation. In this newly developed concept, the ink is atomized inside of the nozzle chamber by ultrasonic excitation. This enables on-demand operation of the printhead. Focusing is achieved by means of a sheath gas and the nozzle geometry. To achieve a collimated aerosol jet, a DCN is advantageous. Computational fluid dynamics (CFD) is used to perform the design layout of a DCN for AoD jet-printing, with the goal of finding a design-for-manufacture.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluation of semi-supervised learning using sparse labeling to segment cell nuclei

Evaluation of semi-supervised learning using sparse labeling...

引用

作者： Bruch, Roman Rudolf, Rüdiger Mikut, Ralf Reischl, Markus Institute of Molecular and Cell Biology Mannheim University of Applied Sciences Mannheim Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

The analysis of microscopic images from cell cultures plays an important role in the development of drugs. The segmentation of such images is a basic step to extract the viable information on which further evaluation steps are build. Classical image processing pipelines often fail under heterogeneous conditions. In the recent years deep neuronal networks gained attention due to their great potentials in image segmentation. One main pitfall of deep learning is often seen in the amount of labeled data required for training such models. Especially for 3D images the process to generate such data is tedious and time consuming and thus seen as a possible reason for the lack of establishment of deep learning models for 3D data. Efforts have been made to minimize the time needed to create labeled training data or to reduce the amount of labels needed for training. In this paper we present a new semisupervised training method for image segmentation of microscopic cell recordings based on an iterative approach utilizing unlabeled data during training. This method helps to further reduce the amount of labels required to effectively train deep learning models for image segmentation. By labeling less than one percent of the training data, a performance of 90% compared to a full annotation with 342 nuclei can be achieved. © 2020 by Walter de Gruyter Berlin/Boston 2020.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Identifying drivers and mitigators for congestion and redispatch in the German electric power system with explainable AI

arXiv

引用

arXiv 2023年

作者： Titz, Maurizio Pütz, Sebastian Witthaut, Dirk Jülich52428 Germany University of Cologne Institute for Theoretical Physics Zülpicher Str. 77 Cologne50937 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

The transition to a sustainable energy supply challenges the operation of electric power systems in manifold ways. Transmission grid loads increase as wind and solar power are often installed far away from the consumers. In extreme cases, system operators must intervene via countertrading or redispatch to ensure grid stability. In this article, we provide a data-driven analysis of congestion in the German transmission grid. We develop an explainable machine learning model to predict the volume of redispatch and countertrade on an hourly basis. The model reveals factors that drive or mitigate grid congestion and quantifies their impact. We show that, as expected, wind power generation is the main driver, but hydropower and cross-border electricity trading also play an essential role. Solar power, on the other hand, has no mitigating effect. Our results suggest that a change to the market design would alleviate congestion. Copyright © 2023, The Authors. All rights reserved.

关键词： Solar energy

来源：评论

学校读者我要写书评

暂无评论

An Industry 4.0-Compliant Digital Product Passport Approach for Realising Dairy Product Traceability 49

An Industry 4.0-Compliant Digital Product Passport Approach ...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Bar, Martin Alejandro Colombo, Armando Walter Torres, Jose Luis Fernandez, Erica Rico, Mariela Caliusco, Maria Laura Institute for Industrial Informatics Automation and Robotics University of Applied Sciences Emden/Leer Emden Germany Ciese Research Center Utn - Fac. Reg. Santa Fe Santa Fe Argentina Industrial Engineering Utn - Fac. Reg. Rafaela - Santa Fe Santa Fe Argentina Cidisi Research Center Utn - Fac. Reg. Santa Fe Santa Fe Argentina

ISBN: (纸本)9798350331820

This paper provides an approach to create the digital thread associated to the value stream along a dairy process value chain. Identifying the different stakeholders within the dairy process, each of them is concretely positioned as provider of assets to be digitalized and networked using the Asset Administration Shell technology and this within an Eco-system formally specified by the 3D-Reference Architecture Model for Industry 4.0 (RAMI 4.0). The different sub-models of the Asset Administration Shells along the digital thread are then an essential part of the so called Digital Passport, which will be associated to different dairy products (milk powders, butter, whey, cheese, etc.). The Digital Passport is the core tool allowing all stakeholders for performing traceability of products and processes. It provides digitalized data and information, to be exposed and/or consumed as services, developed and shared in real-time by all the actors of the dairy supply chain, responding to strict dairy process business requirements. © 2023 IEEE.

关键词： Dairies

来源：评论

学校读者我要写书评

暂无评论

Non-linear, bivariate stochastic modelling of power-grid frequency applied to islands

Non-linear, bivariate stochastic modelling of power-grid fre...

引用

Power Tech Conference

作者： Ulrich Oberhofer Leonardo Rydin Gorjao G. Cigdem Yalcin Oliver Kamps Veit Hagenmeyer Benjamin Schafer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Faculty of Science and Technology Norwegian University of Life Sciences As Norway Department of Physics Istanbul University Istanbul Turkey Center for Nonlinear Sciences (CeNoS) University of Munster Germany

Mitigating climate change requires a transition away from fossil fuels towards renewable energy. As a result, power generation becomes more volatile and options for microgrids and islanded power-grid operation are being broadly discussed. Therefore, studying the power grids of physical islands, as a model for islanded microgrids, is of particular interest when it comes to enhancing our understanding of power-grid stability. In the present paper, we investigate the statistical properties of the power-grid frequency of three island systems: Iceland, Ireland, and the Balearic Islands. We utilise a Fokker-Planck approach to construct stochastic differential equations that describe market activities, control, and noise acting on power-grid dynamics. Using the obtained parameters we create synthetic time series of the frequency dynamics and compare them to empirical data. Our main contribution is to propose two extensions of stochastic power-grid frequency models and showcase the applicability of these new models to non-Gaussian statistics, as encountered in islands.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Non-linear, bivariate stochastic modelling of power-grid frequency applied to islands

arXiv

引用

arXiv 2023年

作者： Oberhofer, Ulrich Gorjão, Leonardo Rydin Cigdem Yalcin, G. Kamps, Oliver Hagenmeyer, Veit Schäfer, Benjamin Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Faculty of Science and Technology Norwegian University of Life Sciences Ås Norway Department of Physics Istanbul University Istanbul Turkey University of Münster Münster Germany

关键词： Fossil fuels

来源：评论

学校读者我要写书评

暂无评论

Technical sound event classification applying recurrent and convolutional neural networks 1

Technical sound event classification applying recurrent and ...

引用

1st International Conference on Deep Learning Theory and Applications, DeLTA 2020

作者： Rieder, Constantin Germann, Markus Mezger, Samuel Scherer, Klaus Peter Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen Germany

ISBN: (纸本)9789897584411

In many intelligent technical assistance systems (especially diagnostics), the sound classification is a significant and useful input for intelligent diagnostics. A high performance classification of the heterogeneous sounds of any mechanical components can support the diagnostic experts with a lot of information. Classical pattern recognition methods fail because of the complex features and the heterogeneous state noise. Because of no explicit human knowledge about the characteristic representation of the classes, classical feature generation is impossible. A new approach by generation of a concept for neural networks and realization by especially convolutional networks shows the power of technical sound classification methods. After the concept finding a parametrized network model is devised and realized. First results show the power of the RNNs and CNNs. Dependent on the parametrized configuration of the net architecture and the training sets an enhancement of the sound event classification is possible. © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

关键词： Recurrent neural networks

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：