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Lehrstuhl für Allgemeine Elektrotechnik und Theoretische Nachrichtentechnik

Prof. Dr.- Ing. Anton Kummert


Alle :: 1986, ... , 2017, 2018, 2019, 2020
J. Cao; H. Dai; B. Lei; C. Yin; H. Zeng; A. Kummert
Maximum Correntropy Criterion-Based Hierarchical One-Class Classification
IEEE Transactions on Neural Networks and Learning Systems, :1-7
ISSN: 2162-2388

Schlüsselwörter: Hierarchical structure;maximum correntropy criterion (MCC);one-class classification;outlier/anomaly detection.

Zusammenfassung: Due to the effectiveness of anomaly/outlier detection, one-class algorithms have been extensively studied in the past. The representatives include the shallow-structure methods and deep networks, such as the one-class support vector machine (OC-SVM), one-class extreme learning machine (OC-ELM), deep support vector data description (Deep SVDD), and multilayer OC-ELM (ML-OCELM/MK-OCELM). However, existing algorithms are generally built on the minimum mean-square-error (mse) criterion, which is robust to the Gaussian noises but less effective in dealing with large outliers. To alleviate this deficiency, a robust maximum correntropy criterion (MCC)-based OC-ELM (MC-OCELM) is first proposed and then further extended to a hierarchical network to enhance its capability in characterizing complex and large data (named HC-OCELM). The gradient derivation combining with a fixed-point iterative updation scheme is adopted for the output weight optimization. Experiments on many benchmark data sets are conducted for effectiveness validation. Comparisons to many state-of-the-art approaches are provided for the superiority demonstration.

Jessica Malerczyk; Sabine Lerch; Bernd Tibken; Anton Kummert
Impact of intelligent agents on the avoidance of spontaneous traffic jams on two-lane motorways
MATEC Web of Conferences Band 308 , Seite 05003.
EDP Sciences
Jan-Christoph Schmitz; Stephan Tilgner; Kathrin Kalischewski; Daniel Wagner; Anton Kummert
Hands on Wheel Classification Based on Depth Images and Neural Networks
MATEC Web of Conferences Band 308 , Seite 06003.
EDP Sciences
L. Kolonko; J. Velten; A. Kummert
Optimization of Artificial Port Reflectances for Wave Digital Filters with Topology-Related Delay-Free Loops
2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS) , Seite 170-173.

Schlüsselwörter: Wave Digital Filter;Automatic Differentiation;Delay-Free Loop;Multi-Dimensional;Bridged-T Model

Zusammenfassung: In this paper, a generic method for fast determination of all involved optimal artificial port resistances is presented for the realization of Wave Digital Filters (WDFs) containing noncomputable, delay-free loops. Therefore, the concept of Automatic Differentiating WDFs (ADWDFs) is applied to obtain said resistances by minimizing the associated artificial reflectances, which performs significantly faster than empirical approaches, as will be shown in an example. This way, the resulting Wave Digital structure remains completely modular under fixed point iteration schemes achieving optimal convergence speeds. Additionally, contractivity of WDFs is exploited to obtain an optimal operating point from a different perspective.