Volume 71, Issue 10 - Special Issue: Selected Contributions from the Workshop “Computational Intelligence”; Guest Editors: Ralf Mikut, Andreas Kroll and Horst Schulte

The quality of data used for data-driven modeling affects the model performance significantly. Thus, design of experiments (DoE) is an important part during model development. The design space is constrained in many applications. In this work, the constrained case is investigated. An Latin hypercube based approach is applied and analyzed for strongly constrained design spaces. Contrary to commonly used optimization techniques, an incremental procedure is proposed. In every step, new data are added to the design. Each new point is selected by a distance-based criterion. The performance of the created designs is evaluated by the quality of the trained models. For different constraints, artificial data sets are created with a function generator. The performance of local model networks and Gaussian process regression models trained with those designs is evaluated and compared to models trained on data sets based on Sobol’ sequences.

System identification with finite impulse response (FIR) models has been recently revised by introducing specific forms of regularization in the estimation. By utilizing prior knowledge of the dynamic process under investigation different kernels for regularization can be derived. However, the dynamical processes considered are mainly restricted to time-delay free systems. Therefore, in this paper we propose two novel methods to handle the time-delay case. Both methods incorporate the time-delay estimation in the hyperparameter optimization of the regularized FIR models. The first method is built on a single kernel (SK) and the second utilizes a multiple kernel (MK) approach. Simulations on different dynamical systems show the superior behavior of both methods in comparison to standard regularization methods, whereupon the MK outperforms the SK approach.

A touch-evoked response of zebrafish larvae provides information on the mechanism of the gene functional expressions. Recently, an automated system has been developed for precise and repeated touch-response experimentation with minor human intervention. To quantify the collected data, we propose a fully automated multi-larvae touch-response behavior inspection pipeline based on larva tracking and segmentation. Experimental data with different treatments is analyzed by using the proposed inspection platform for demonstration, and the result proves that this platform can generate comparable touch-response behavior inspection readouts efficiently and automatically. The initial results were published in 31. Workshop Computational Intelligence, and this paper summarizes and extends the main work of the respective article.

Zusammenfassung LLC-Resonanzwandler spielen eine wichtige Rolle bei der Energieversorgung batteriebetriebener Elektrokleinfahrzeuge. Die Modellierung des nichtlinearen, dynamischen Verhaltens dieser Schaltungstopologie ist seit einiger Zeit Gegenstand von Forschungs- und Entwicklungsarbeiten. Häufig werden hierzu möglichst einfache lineare Kleinsignalmodelle abgeleitet, welche allerdings nur einen Reglerentwurf in einem Arbeitspunkt zulassen. Die vorliegende Arbeit greift eine neue Methode zur Modellierung von LLC-Resonanzwandlern auf und erweitert diese im Rahmen eines Takagi-Sugeno Fuzzy-Modells, um das nichtlineare Verhalten über einen großen Arbeitsbereich abzudecken. Die vorgestellte Methode wird mit einem datengetriebenen Takagi-Sugeno-Modell verglichen.

The online classification of grid disturbances in power transmission systems has been investigated since many years and shows promising results on measured and simulated PMU signals. Nonetheless, a practical deployment of machine learning techniques is still challenging due to robustness problems, which may lead to severe misclassifications in the model application. This paper formulates an advanced evaluation procedure for disturbance classification methods by introducing additional measurement noise, unknown operational points, and unknown disturbance events in the test dataset. Based on preliminary work, Siamese Sigmoid Networks are used as classification approach and are compared against several benchmark models for a simulated power transmission system at 400 kV. Different test scenarios are proposed to evaluate the disturbance classification models assuming a limited and full observability of the grid with PMUs.

In production, quality monitoring is essential to detect defective elements. State-of-the-art approaches are single-sensor systems (SSS) and multi-sensor systems (MSS). Yet, these approaches might not be suitable: Nowadays, one component may comprise several hundred meters of the weld seam, necessitating high-speed welding to produce enough components. To detect as many defects as possible in time, fast yet precise monitoring is required. However, information captured by SSS might not be sufficient and MSS suffer from long inference times. Therefore, we present a confidence-based cascaded system (CS). The key idea of the CS is that not all data are analyzed to obtain the quality weld, but only selected ones. As evidenced by our results, all CS outperform SSS in terms of accuracy and inference time. Further, compared to MSS, the CS has hardware advantages.

A power tracking controller for frequency support by photovoltaic power plants without battery storage is presented. Due to the decreasing inertia, regenerative systems such as wind turbines and photovoltaic power plants must provide an instantaneous reserve for fast frequency support in power systems. To provide grid support by PV power plants, a control scheme, and a design procedure are introduced to ensure power reserve by leaving the optimal operating point. A model-based generalized design procedure in the Takagi-Sugeno fuzzy framework for PV system with switched-mode DC-DC converter is presented to achieve the specified control objectives. Simulation studies show the applicability of the control scheme.