IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines

Eirich, Joscha; Bonart, J. A. E.; Jäckle, Dominik; Sedlmair, Michael; Schmid, Ute; Fischbach, Kai; Schreck, Tobias; Bernard, Jürgen

doi:10.1109/tvcg.2021.3114797

Cited by 24 publications

(14 citation statements)

References 43 publications

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“…In today's highly automated manufacturing processes, the extraction of relevant and meaningful information from high‐dimensional data remains a challenging problem [BKSS15, EBJ*21]. In this regard, the cooperation between human experts and ML techniques has often proved to be a promising solution by combining the strengths of both worlds [SMF*20, JFSK15].…”

Section: Discussionmentioning

confidence: 99%

RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines

Eirich

Munch

Jäckle

et al. 2022

Computer Graphics Forum

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Random Forests (RFs) are a machine learning (ML) technique widely used across industries. The interpretation of a given RF usually relies on the analysis of statistical values and is often only possible for data analytics experts. To make RFs accessible to experts with no data analytics background, we present RfX, a Visual Analytics (VA) system for the analysis of a RF's decisionmaking process. RfX allows to interactively analyse the properties of a forest and to explore and compare multiple trees in a RF. Thus, its users can identify relationships within a RF's feature subspace and detect hidden patterns in the model's underlying data. We contribute a design study in collaboration with an automotive company. A formative evaluation of RFX was carried out with two domain experts and a summative evaluation in the form of a field study with five domain experts. In this context, new hidden patterns such as increased eccentricities in an engine's rotor by observing secondary excitations of its bearings were detected using analyses made with RfX. Rules derived from analyses with the system led to a change in the company's testing procedures for electrical engines, which resulted in 80% reduced testing time for over 30% of all components.

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Section: Discussionmentioning

confidence: 99%

RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines

Eirich

Munch

Jäckle

et al. 2022

Computer Graphics Forum

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“…However, model refinement can be challenging for users with limited ML expertise. Therefore, other studies take a different approach, allowing users to inspect and label instances as a way to interact with the ML model [14,16,66]. For example, ProtoSteer [41] asks users to edit a list of representative examples from the training data, called prototypes, to steer a deep sequence model [42].…”

Section: Visualization In Interactive Machine Learningmentioning

confidence: 99%

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

Cheng

Keller

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The interface of Polyphony contains three views: the comparison view (A), the anchor set view (B), and the marker view (C).The comparison view provides an overview of the joint embedding space, and offers users interactions to inspect (A1), delete (A2), and add (A3) anchors. The anchor set view orders the anchors in a table, supporting inspection and comparison of different anchors (B1-2). The marker view shows the significant genes (C1) for the query and reference cells from a focal anchor.

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“…Observational studies [37,40,82], think-aloud methods [8], and contextual inquiry [74] are useful methods for identifying tasks while using visualization systems, presuming that a solution for a problem already exists. In particular, domain experts can be a valuable source for task abstractions, through interviews [21,22,39,73], surveys [3,7] or other inquiries. Shortcomings include the limited availability of experts [70] and the risk of skewing the task set because of a low number of experts [42].…”

Section: Building Taxonomic Structures Of Analysis Tasksmentioning

confidence: 99%