检索结果-内蒙古大学图书馆

Identification of driving factors of algal growth in the South-to-North Water Diversion Project by Transformer-based deep learning

引用

Water Biology and Security 2023年第3期2卷 47-56页

作者： Jing Qian Nan Pu Li Qian Xiaobai Xue Yonghong Bi Stefan Norra Institute of Applied Geosciences Karlsruhe Institute of TechnologyKarlsruhe76131Germany Institute of Advanced Computer Science Leiden UniversityLeiden2333CANetherlands Institute of Informatics Ludwig Maximilian University of MunichMunich80538Germany MioTech Research Yingtou Information Technology(Shanghai)LimitedShanghai200120China State Key Laboratory of Freshwater Ecology and Biotechnology Institute of HydrobiologyChinese Academy of SciencesWuhan430072China Institute of Environmental Sciences and Geography Soil Sciences and GeoecologyPotsdam University Potsdam-Golm14476Germany

Accurate and credible identification of the drivers of algal growth is essential for sustainable utilization and scientific management of *** this study,we developed a deep learning-based Transformer model,named Bloomformer-1,for end-to-end identification of the drivers of algal growth without the needing extensive a priori knowledge or prior *** Middle Route of the South-to-North Water Diversion Project(MRP)was used as the study site to demonstrate that Bloomformer-1 exhibited more robust performance(with the highest R^(2),0.80 to 0.94,and the lowest RMSE,0.22–0.43μg/L)compared to four widely used traditional machine learning models,namely extra trees regression(ETR),gradient boosting regression tree(GBRT),support vector regression(SVR),and multiple linear regression(MLR).In addition,Bloomformer-1 had higher interpretability(including higher transferability and understandability)than the four traditional machine learning models,which meant that it was trustworthy and the results could be directly applied to real ***,it was determined that total phosphorus(TP)was the most important driver for the MRP,especially in Henan section of the canal,although total nitrogen(TN)had the highest effect on algal growth in the Hebei *** on these results,phosphorus loading controlling in the whole MRP was proposed as an algal control strategy.

关键词： Algal growth Deep learning Driving factor determination Model interpretability Transformer

来源：评论

学校读者我要写书评

暂无评论

Survey and Design Concept for Control Centers of Automated Vehicle Fleets

Survey and Design Concept for Control Centers of Automated V...

引用

International Conference on Intelligent Transportation

作者： Martin Gontscharow Albert Schotschneider Stefan Orf Tobias Fleck Marc Zofka J. Marius Zöllner FZI Research Center for Information Technology Karlsruhe Germany Institute for Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology (KIT) Germany

Control centers for automated vehicle fleets (CC-AVFs) are pivotal in enabling autonomous driving at a large scale. This work conducts a holistic survey of CC-AVFs, focusing on European studies. It discusses operational concepts, requirements, architectures, transmission technologies, monitoring, and remote operation methods. Additionally, we present a design concept for CC-AVFs that structures the outlined topics and addresses dependencies. This paper connects the fragmented literature regarding CC-AVFs and gives design considerations to guide future research for efficient and safe CC-AVF implementation in contexts such as public transport.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ON AUTOREGRESSIVE DEEP LEARNING MODELS FOR DAY-AHEAD WIND POWER FORECASTING WITH IRREGULAR SHUTDOWNS DUE TO REDISPATCHING

arXiv

引用

arXiv 2024年

作者： Meisenbacher, Stefan Selzer, Silas Aaron Dado, Mehdi Beichter, Maximilian Martin, Tim Zdrallek, Markus Bretschneider, Peter Hagenmeyer, Veit Mikut, Ralf Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany University of Wuppertal Wuppertal42119 Germany Technische Universität Ilmenau Ilmenau98693 Germany Fraunhofer IOSB Ilmenau98693 Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

Renewable energies and their operation are becoming increasingly vital for the stability of electrical power grids since conventional power plants are progressively being displaced, and their contribution to redispatch interventions is thereby diminishing. In order to consider renewable energies like Wind Power (WP) for such interventions as a substitute, day-ahead forecasts are necessary to communicate their availability for redispatch planning. In this context, automated and scalable forecasting models are required for the deployment to thousands of locally-distributed onshore WP turbines. Furthermore, the irregular interventions into the WP generation capabilities due to redispatch shutdowns pose challenges in the design and operation of WP forecasting models. Since state-of-the-art forecasting methods consider past WP generation values alongside day-ahead weather forecasts, redispatch shutdowns may impact the forecast. Therefore, the present paper highlights these challenges and analyzes state-of-the-art forecasting methods on data sets with both regular and irregular shutdowns. Specifically, we compare the forecasting accuracy of three autoregressive Deep Learning (DL) methods to methods based on WP curve modeling. Interestingly, the latter achieve lower forecasting errors, have fewer requirements for data cleaning during modeling and operation while being computationally more efficient, suggesting their advantages in practical applications. © 2024, CC BY.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Ciscnet - a Single-Branch Cell Nucleus Instance Segmentation and Classification Network

Ciscnet - a Single-Branch Cell Nucleus Instance Segmentation...

引用

Biomedical Imaging Challenges (ISBIC), IEEE International Symposium on

作者： Moritz Bö hland Oliver Neumann Marcel P. Schilling Markus Reischl Ralf Mikut Katharina Lö ffler Tim Scherr Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

ISBN: (数字)9781665451727

ISBN: (纸本)9781665451734

Comprehensible and high-quality automated cell nucleus segmentation and classification are required to assist pathol-ogists in their decision making. Commonly, cell nucleus segmentation and classification are either treated as separate tasks or split into different branches of a convolutional neural network. In this contribution, we present our joint cell nu-cleus instance segmentation and classification convolutional neural network ciscNet. In contrast to the state-of-the-art convolutional neural network HoVer-Net that uses multi-ple branches, ciscNet uses a single branch to segment and classify cell nuclei. Our single-branch approach outper-forms HoVer-Net on the histopathological Lizard dataset. In addition, we show that training our approach with the Ranger optimizer yields better results than using the Adam optimizer. Furthermore, we participate as team ciscNet in the Colon Nuclei Identification and Counting Challenge 2022 (CoNIC Challenge 2022). Our code is available at https://***/ciscnet/ciscnet-conic-2022.

关键词： Training Image segmentation Codes Biological system modeling Decision making Colon Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

TOWARDS PRAGMATIC INTEROPERABILITY: A CONCEPTUAL FRAMEWORK FOR AN ENERGY IOT PLATFORM IN THE GERMAN ENERGY SECTOR

TOWARDS PRAGMATIC INTEROPERABILITY: A CONCEPTUAL FRAMEWORK F...

引用

CIRED 2024 Vienna Workshop

作者： Karmann, Alexandra Kilthau, Maximilian Derksen, Christian Strobel, Florian Bittermann, Lukas Korotkiewicz, Kamil Ostmeyer, Jonas Fay, Alexander Institute of Automation Technology Helmut-Schmidt-University Hamburg Germany Research Group for Business Informatics and Software Engineering University Duisburg-Essen Essen Germany Institute of Electrical Power and Energy Technology University of Technology Hamburg Hamburg Germany PSI GridConnect GmbH Karlsruhe Germany PSI FLS Fuzzy Logik & Neuro Systeme GmbH Dortmund Germany Ruhr-University Bochum Germany

ISBN: (纸本)9781837242672

This paper introduces an innovative conceptual framework for an Energy Internet of Things (eIoT) platform designed to address challenges related to grid congestion, voltage limit violations, and the growing demands on low-voltage grids due to increased electrification in the energy and heating sectors. The proposed platform seamlessly integrates cutting-edge technologies including advanced data collection, state estimation algorithms, AI-driven decision-making processes, and grid control functionalities. Its primary objective is to facilitate proactive measures aimed at enhancing energy distribution efficiency and effectively managing consumption devices while ensuring compliance with regulatory standards such as §14a of the Energy Industry Act (EnWG) in Germany. © The Institution of Engineering & technology 2024.

关键词： Internet of Energy

来源：评论

学校读者我要写书评

暂无评论

Optimum settings for PID control of nonlinear systems

arXiv

引用

arXiv 2025年

作者： Vrabel, R. Slovak University of Technology in Bratislava Institute of Applied Informatics Automation and Mechatronics Bottova 25 Trnava917 01 Slovakia

This paper investigates the application of piecewise linear approximation for the control of nonlinear systems, particularly focusing on the effective linearization of systems modeled by the differential equation y(n) + f(y, y', . . . , y(n-1)) = u(t). We explore the use of PID controllers in conjunction with piecewise linearization, tackling the challenges of nonlinear dynamics by dividing the function f into smaller regions or simplices over a predefined compact set D ⊂ Rn. The parameter h determines the number of linear pieces and influences the approximation accuracy. By increasing h, the system's behavior increasingly mirrors that of the original nonlinear system, leading to enhanced performance of the control system. The paper further discusses the optimization of PID controller parameters through the Particle Swarm Optimization method, with a focus on minimizing the ITAE and ISO criteria. Numerical simulations demonstrate the efficacy of the approach, highlighting the trade-off between computational complexity and approximation accuracy in the context of control system *** Codes 93C10 © 2025, CC BY.

关键词： Three term control systems

来源：评论

学校读者我要写书评

暂无评论

Classification of Bundle Branch Blocks with QRS Templates Extracted from 12-lead ECGs

引用

Current Directions in Biomedical Engineering 2021年第2期7卷 582-585页

作者： Welle, Hannes Nagel, Claudia Loewe, Axel Mikut, Ralf Dössel, Olaf Institute of Biomedical Engineering KIT Fritz-Haber-Weg 1 Karlsruhe Germany Institute for Automation and Applied Informatics KIT Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen Germany

Being non-invasive, cheap and widely available, the 12-lead electrocardiogram (ECG) is a standard method to assess cardiac function. Still, its reliable interpretation requires specialized knowledge and experience, rendering a second opinion valuable. We evaluated the performance of machine learning based classification of 11,705 healthy and bundle branch block 12-lead ECGs from 3 open databases. For each lead of the ECG signal, a representative QRS-complex template was extracted automatically. Principal component analysis (PCA) was applied to the concatenated, normalized and rescaled QRS signals to reduce their dimensionality. Multilayer perceptron and support-vector machine classifiers were trained using the principal components of weighted and non-weighted QRS template signals as input data. Classifiers achieved F1 scores between 0.92 and 0.96 on the test set for different input configurations. Anomaly based weighting slightly improved the performance of the classifiers. Neither class-wise PCA for feature extraction nor adding information on sex, gender and electrical heart axis to the input data yielded considerable improvement of the F1 scores. The achieved classification accuracy is similar to deep learning classifier performances and should generalize robustly to other ECG datasets. Our results suggest that this simple and well interpretable approach based on morphological signal characteristics is suitable for automatically and non-invasively identifying bundle branch block pathologies in clinical or smart electronics contexts. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

An Empirical Evaluation of Constrained Feature Selection

引用

SN Computer Science 2022年第6期3卷 1-25页

作者： Bach, Jakob Zoller, Kolja Trittenbach, Holger Schulz, Katrin Böhm, Klemens Department of Informatics Karlsruhe Institute of Technology (KIT) Am Fasanengarten 5 Baden-Württemberg Karlsruhe 76131 Germany Department of Mechanical Engineering Karlsruhe Institute of Technology (KIT) Kaiserstraße 12 Baden-Württemberg Karlsruhe 76131 Germany Faculty of Mechanical Engineering and Mechatronics Karlsruhe University of Applied Sciences Moltkestraße 30 Baden-Württemberg Karlsruhe 76133 Germany

While feature selection helps to get smaller and more understandable prediction models, most existing feature-selection techniques do not consider domain knowledge. One way to use domain knowledge is via constraints on sets of selected features. However, the impact of constraints, e.g., on the predictive quality of selected features, is currently unclear. This article is an empirical study that evaluates the impact of propositional and arithmetic constraints on filter feature selection. First, we systematically generate constraints from various types, using datasets from different domains. As expected, constraints tend to decrease the predictive quality of feature sets, but this effect is non-linear. So we observe feature sets both adhering to constraints and with high predictive quality. Second, we study a concrete setting in materials science. This part of our study sheds light on how one can analyze scientific hypotheses with the help of constraints. © 2022, The Author(s).

关键词： Constraints Domain knowledge Feature selection Theory-guided data science

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

Assessing the combination of differential flatness and deterministic automata for controllable hybrid systems

Assessing the combination of differential flatness and deter...

引用

IEEE Conference on Decision and Control

作者： Frederik Zahn Tobias Kleinert Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen University Aachen Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Hybrid automata are a powerful model class to analyze and control technical systems with nonlinear continuous dynamics and discrete-event dynamics. However, due to their complexity, controllability is generally hard to achieve in hybrid automata, although it is a desirable property of engineered systems. In the present paper, we assess a novel model class of controllable deterministic hybrid automata, which we call Flat Hybrid Automata. This model class is based on the concept of differential flatness for the nonlinear continuous dynamics and on the property of determinism and controllability for the discrete event dynamics. We review the formal definition and we give the respective conditions and a proof for its controllability. An electrical network example illustrates the methodology and shows its potential regarding modelling, control and system design.

关键词： Adaptation models Analytical models Power system dynamics Automata Switches Controllability Trajectory

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：