@article{KahlKrauseHackenbergetal., author = {Kahl, Matthias and Krause, Veronika and Hackenberg, Rudolf and Ul Haq, Anwar and Horn, Anton and Jacobsen, Hans-Arno and Kriechbaumer, Thomas and Petzenhauser, Michael and Shamonin (Chamonine), Mikhail and Udalzow, Anton}, title = {Measurement system and dataset for in-depth analysis of appliance energy consumption in industrial environment}, series = {tm - Technisches Messen}, volume = {86}, journal = {tm - Technisches Messen}, number = {1}, publisher = {De Gruyter}, doi = {10.1515/teme-2018-0038}, pages = {1 -- 13}, abstract = {To support a rational and efficient use of electrical energy in residential and industrial environments, Non-Intrusive Load Monitoring (NILM) provides several techniques to identify state and power consumption profiles of connected appliances. Design requirements for such systems include a low hardware and installations costs for residential, reliability and high-availability for industrial purposes, while keeping invasive interventions into the electrical infrastructure to a minimum. This work introduces a reference hardware setup that allows an in depth analysis of electrical energy consumption in industrial environments. To identify appliances and their consumption profile, appropriate identification algorithms are developed by the NILM community. To enable an evaluation of these algorithms on industrial appliances, we introduce the Laboratory-measured IndustriaL Appliance Characteristics (LILAC) dataset: 1302 measurements from one, two, and three concurrently running appliances of 15 appliance types, measured with the introduced testbed. To allow in-depth appliance consumption analysis, measurements were carried out with a sampling rate of 50 kHz and 16-bit amplitude resolution for voltage and current signals. We show in experiments that signal signatures, contained in the measurement data, allows one to distinguish the single measured electrical appliances with a baseline machine learning approach of nearly 100\% accuracy.}, language = {en} } @inproceedings{HammerMottokKrauseetal., author = {Hammer, Pascal and Mottok, J{\"u}rgen and Krause, Veronika and Probst, Tobias}, title = {Approach for High-Performance Random Number Generators for Critical Systems}, series = {Proceeding of the 12th European Congress on Embedded Real Time Software and Systems (ERTS2024) , Toulouse, 11-12 June 2024}, booktitle = {Proceeding of the 12th European Congress on Embedded Real Time Software and Systems (ERTS2024) , Toulouse, 11-12 June 2024}, doi = {10.5281/zenodo.14848832}, pages = {9}, abstract = {In times of digitalization, the encryption and signing of sensitive data is becoming increasingly important. These cryptographic processes require large quantities of high-quality random numbers. Which is why a high-performance random number generator (RNG) is to be developed. For this purpose, existing concepts of RNGs and application standards are first analyzed. The proposed approach is to design a physical true random number generator (PTRNG) with a high output of random numbers. Based on this, the development begins with the analog part of the RNG, the noise signal source and a suitable amplifier for the analog noise signal. Therefore, a special noise diode from Noisecom and an amplifier from NXP were chosen and analyzed in different measurements. From the results of the measurements, it can be concluded that both components are suitable for use in the RNG.}, language = {en} }