TY - JOUR A1 - Dobrovolskij, Dascha A1 - Stark, Hans-Georg T1 - Synthetic demand data generation for individual electricity consumers: Inpainting JF - Energy and AI N2 - In this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier. KW - Energieverbrauch KW - Energieversorgungsunternehmen Y1 - 2024 VL - 15 IS - Januar 2024 SP - 100312 EP - 100320 ER - TY - JOUR A1 - Lieb, Florian A1 - Stark, Hans-Georg A1 - Thielemann, Christiane T1 - A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data JF - Journal of Neural Engineering N2 - Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms. KW - spike detection KW - wavelet TEO KW - extracellular recording KW - Neuronales Netz Y1 - 2017 U6 - https://doi.org/10.1088/1741-2552/aa654b VL - 2017 IS - 14 SP - 1 EP - 13 ER -