The MATLAB Toolbox SciXMiner: User's Manual and Programmer's Guide

Mikut, Ralf; Bartschat, Andreas; Doneit, Wolfgang; Ordiano, Jorge Ángel González; Schott, Benjamin; Stegmaier, Johannes; Waczowicz, Simon; Reischl, Markus

doi:10.48550/arxiv.1704.03298

Cited by 12 publications

(15 citation statements)

References 38 publications

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“…In this section, we propose a scheduling formulation alternative to Problem (24), aiming to minimize the total expected operating cost, including the cost of the DiS and the expected cost of imbalances. This cost can be expressed as…”

Section: Minimizing the Cost Of Deviationsmentioning

confidence: 99%

“…Notice that we employ for {p l (k)} k∈K the hourly-averaged profile of the real production/consumption, considering that the storage compensates for the zero-mean intra-hour variability 5. www.tesla.com/powerwall6 The forecasting models are generated with the open-source MATLAB toolbox SciXMiner[24]. The data from 01/07/10 to 01/12/12 is utilized for training the model.…”

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confidence: 99%

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Storage Scheduling with Stochastic Uncertainties: Feasibility and Cost of Imbalances

Appino

Ordiano

Mikut

et al. 2018

2018 Power Systems Computation Conference (PSCC)

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Dispatchability of renewable energy sources and inflexible loads can be achieved using a volatility-compensating energy storage. However, as the future power outputs of the inflexible devices are uncertain, the computation of a dispatch schedule for such aggregated systems is non-trivial. In the present paper, we propose a novel scheduling method that enforces the feasibility of the dispatch schedule with a pre-determined probability based on a description of the operation of the system as a two-stage decision process. Thereby, a crucial point is the use of probabilistic forecasts, in terms of cumulative density function, of the inflexible energy consumption/production profile. Then, for the sake of comparison, we introduce a second scheduling method based on state-of-the-art scenario optimization, where, unlike the proposed method, the focus is on the minimization of the expected final cost. We draw upon simulations based on real consumption and production data to compare the methods and illustrate our findings.

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Section: Minimizing the Cost Of Deviationsmentioning

confidence: 99%

mentioning

confidence: 99%

Storage Scheduling with Stochastic Uncertainties: Feasibility and Cost of Imbalances

Appino

Ordiano

Mikut

et al. 2018

2018 Power Systems Computation Conference (PSCC)

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“…Moreover, to improve the forecast accuracy, the night values are removed during training and automatically set to zero during testing. Note that all polynomial and FCANN models are trained using the MATLAB open-source toolbox SciXMiner [20].…”

Section: Experiments Descriptionmentioning

confidence: 99%

Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches

Sharma,

Ordiano,

Mikut

et al. 2021

Preprint

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The high penetration of volatile renewable energy sources such as solar, make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14).

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“…We implemented an automatic tracking and quantification tool in MATLAB to postprocess 2D segmentations or manually segmented consecutive 3D image snippets. For the 2D projections, we incorporated the nearest neighbor-based tracking algorithm of the SciXMiner toolbox [19]. For each tracked cell we extract quantitative features like area, eccentricity and speed.…”

Section: Automatic Tracking and Measurementmentioning

confidence: 99%

Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples

Bhide¹,

Mikut²,

Leptin³

et al. 2020

Preprint

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Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t) of complete organisms and offers insights into their development on the cellular level. Even though the imaging speed and quality is steadily improving, fullyautomated segmentation and analysis methods are often not accurate enough. This is particularly true while imaging large samples (100 µm −1 mm) and deep inside the specimen. Drosophila embryogenesis, widely used as a developmental paradigm, presents an example for such a challenge, especially where cell outlines need to imaged a general challenge in other systems as well. To deal with the current bottleneck in analyzing quantitatively the 3D+t light-sheet microscopy images of Drosophila embryos, we developed a collection of semi-automatic open-source tools. The presented methods include a semi-automatic masking procedure, automatic projection of non-convex 3D isosurfaces to 2D representations as well as cell segmentation and tracking.

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The MATLAB Toolbox SciXMiner: User's Manual and Programmer's Guide

Cited by 12 publications

References 38 publications

Storage Scheduling with Stochastic Uncertainties: Feasibility and Cost of Imbalances

Storage Scheduling with Stochastic Uncertainties: Feasibility and Cost of Imbalances

Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches

Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples

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