Krešimir Matković

Rainer Splechtna, Majid Behravan, Mario Jelovic et al.

The optimization of cooling systems is important in many cases, for example for cabin and battery cooling in electric cars. Such an optimization is governed by multiple, conflicting objectives and it is performed across a multi-dimensional parameter space. The extent of the parameter space, the complexity of the non-linear model of the system, as well as the time needed per simulation run and factors that are not modeled in the simulation necessitate an iterative, semi-automatic approach. We present an interactive visual optimization approach, where the user works with a p-h diagram to steer an iterative, guided optimization process. A deep learning (DL) model provides estimates for parameters, given a target characterization of the system, while numerical simulation is used to compute system characteristics for an ensemble of parameter sets. Since the DL model only serves as an approximation of the inverse of the cooling system and since target characteristics can be chosen according to different, competing objectives, an iterative optimization process is realized, developing multiple sets of intermediate solutions, which are visually related to each other. The standard p-h diagram, integrated interactively in this approach, is complemented by a dual, also interactive visual representation of additional expressive measures representing the system characteristics. We show how the known four-points semantic of the p-h diagram meaningfully transfers to the dual data representation. When evaluating this approach in the automotive domain, we found that our solution helped with the overall comprehension of the cooling system and that it lead to a faster convergence during optimization.

Manlio Massiris Fernández, Sanjin Radoš, Krešimir Matković et al.

Ergonomic risk assessment is now, due to an increased awareness, carried out more often than in the past. The conventional risk assessment evaluation, based on expert-assisted observation of the workplaces and manually filling in score tables, is still predominant. Data analysis is usually done with a focus on critical moments, although without the support of contextual information and changes over time. In this paper we introduce ErgoExplorer, a system for the interactive visual analysis of risk assessment data. In contrast to the current practice, we focus on data that span across multiple actions and multiple workers while keeping all contextual information. Data is automatically extracted from video streams. Based on carefully investigated analysis tasks, we introduce new views and their corresponding interactions. These views also incorporate domain-specific score tables to guarantee an easy adoption by domain experts. All views are integrated into ErgoExplorer, which relies on coordinated multiple views to facilitate analysis through interaction. ErgoExplorer makes it possible for the first time to examine complex relationships between risk assessments of individual body parts over long sessions that span multiple operations. The newly introduced approach supports analysis and exploration at several levels of detail, ranging from a general overview, down to inspecting individual frames in the video stream, if necessary. We illustrate the usefulness of the newly proposed approach applying it to several datasets.