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Found 43 papers, 14 papers with code
AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability
AutoPQ also automates the selection of the underlying point forecasting method and the optimization of hyperparameters, ensuring that the best model and configuration is chosen for each application.
On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching
Furthermore, the irregular interventions into the WP generation capabilities due to redispatch shutdowns pose challenges in the design and operation of WP forecasting models.
EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail.
Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators
In order to overcome this problem, we present an approach to estimate pseudo-measurements for non-measured LV feeders based on the metadata of the respective feeder using regression models.
Coverage Path Planning for Thermal Interface Materials
They are applied along a dispense path onto the semiconductor and spread over its entire surface once the heatsink is joined.
Iterative Cluster Harvesting for Wafer Map Defect Patterns
Unsupervised clustering of wafer map defect patterns is challenging because the appearance of certain defect patterns varies significantly.
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy
In the biomedical environment, experiments assessing dynamic processes are primarily performed by a human acquisition supervisor.
MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis
The operationalization of ML models is governed by a set of concepts and methods referred to as Machine Learning Operations (MLOps).
Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent Ensembles
Since the environments can be stochastic and complex in terms of the number of states and feasible actions, activities are usually modelled in a simplified way by Markov decision processes so that, e. g., agents with reinforcement learning are able to learn policies, that help to capture the context and act accordingly to optimally perform activities.
Transformer Training Strategies for Forecasting Multiple Load Time Series
We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information
Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly.
Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks
More specifically, we apply a cINN to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast to generate accurate probabilistic forecasts.
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
To tackle the problem of missing information about the PV mounting configuration, we use new data that become available during operation to adapt the ensemble weights to minimize the forecasting error.
EasyMLServe: Easy Deployment of REST Machine Learning Services
We contribute an EasyMLServe software framework to deploy machine learning services in the cloud using REST interfaces and generic local or web-based GUIs.
Predicting the power grid frequency of European islands
In the present paper, we utilize measurements of the power grid frequency obtained in European islands: the Faroe Islands, Ireland, the Balearic Islands and Iceland and investigate how their frequency can be predicted, compared to the Nordic power system, acting as a reference.
Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks
For more complex geometries, Computational Fluid Dynamics (CFD) simulations are used in combination with manual experiments.
EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking.
Exploiting Multiple EEG Data Domains with Adversarial Learning
Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states.
ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network
Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making.
Review of automated time series forecasting pipelines
We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.
Domain-Invariant Representation Learning from EEG with Private Encoders
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution.
Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks
Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems.
Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
In the present paper, we analyze the impact of data representations on the performance of Deep Neural Networks using energy time series forecasting.
Concepts for Automated Machine Learning in Smart Grid Applications
Forecasts are elementary for sector coupling, where energy-consuming sectors are interconnected with the power-generating sector to address electricity storage challenges by adding flexibility to the power system.
On Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned
The virtually error-free segmentation and tracking of densely packed cells and cell nuclei is still a challenging task.
pyWATTS: Python Workflow Automation Tool for Time Series
Time series data are fundamental for a variety of applications, ranging from financial markets to energy systems.
Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches
Currently, there is a trend towards the use of deep learning probabilistic forecasting methods.
Data-Driven Copy-Paste Imputation 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.
CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings
Electronic control units (ECUs) are essential for many automobile components, e. g. engine, anti-lock braking system (ABS), steering and airbags.
Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles
Smart charging of Electric Vehicles (EVs) reduces operating costs, allows more sustainable battery usage, and promotes the rise of electric mobility.
Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking.
Ranked #1 on
Cell Segmentation
on Fluo-N2DL-HeLa
Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples
Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t) of complete organisms and offers insights into their development on the cellular level.
Towards DeepSpray: Using Convolutional Neural Network to post-process Shadowgraphy Images of Liquid Atomization
This technical report investigates the potential of Convolutional Neural Networks to post-process images from primary atomization.
Probabilistic Energy Forecasting using Quantile Regressions based on a new Nearest Neighbors Quantile Filter
Parametric quantile regressions are a useful tool for creating probabilistic energy forecasts.
Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer
Transfer learning is a powerful tool to adapt trained neural networks to new tasks.
Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection
Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images.
The MATLAB Toolbox SciXMiner: User's Manual and Programmer's Guide
The decision to a Matlab-based solution was made to use the wide mathematical functionality of this package provided by The Mathworks Inc. SciXMiner is controlled by a graphical user interface (GUI) with menu items and control elements like popup lists, checkboxes and edit elements.
3D Cell Nuclei Segmentation with Balanced Graph Partitioning
Cell nuclei segmentation is one of the most important tasks in the analysis of biomedical images.
Datenqualität in Regressionsproblemen
Further, the distribution of input data influences the reliability of regression models.
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines.
Generating Semi-Synthetic Validation Benchmarks for Embryomics
Systematic validation is an essential part of algorithm development.
Challenges of Integrating A Priori Information Efficiently in the Discovery of Spatio-Temporal Objects in Large Databases
The described framework can be used to gain knowledge out of object databases in many different fields.
