iFEED: Interactive Feature Extraction for Engineering Design

Bang, Hyunseung; Selva, Daniel

doi:10.1115/detc2016-60077

Cited by 21 publications

(9 citation statements)

References 26 publications

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“…INFUSE [KPB14] integrates feature selection with prediction models and a glyph-based design to inform users about the feature performance with respect to the prediction performance. iFEED [BS16] and FeatureMiner [CLL16] visualize the feature performance by line charts, bar charts and scatter plots. Prospector [KPN16] applies partial dependence diagnostics to analyze the correlation among features, data values, and prediction results.…”

Section: Interactive Feature Selectionmentioning

confidence: 99%

iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

Yue

Wang

et al. 2021

Computer Graphics Forum

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The model‐based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand‐picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of “good” factors and propose effective visualization designs to illustrate the collective performance of financial factors, stock portfolios, and their interactions. We evaluate iQUANT through a formal user study, two case studies, and expert interviews, using a real stock market dataset consisting of 3000 stocks × 6000 days × 56 factors.

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Section: Interactive Feature Selectionmentioning

confidence: 99%

iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

Yue

Wang

et al. 2021

Computer Graphics Forum

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“…One of the main issues to be faced by humans in the design of complex engineering systems is in fact information overload. When data exceeds a certain size threshold, a designer may become overwhelmed and no longer be able to recognize the underlying meaning of the design problem at hand, which results in premature or poor results (Bang & Selva, 2016). There is a clear need for a methodology to identify and map the data that are relevant in the various stages of ED, in order to unveil how data is used in ED process.…”

Section: The Role Of Data In Engineering Designmentioning

confidence: 99%

Data for Engineering Design: Maps and Gaps

et al. 2021

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Data, information and knowledge are strongly involved in Engineering Design (ED) process. Despite the crucial role played by data in the design process, there is a lack of studies about how different data are used and generated by the various phases of the ED process. This study is a first attempt to fill this gap by mapping which data types are involved in the different ED phases from a research perspective.In order to achieve this objective, we used a methodology based on Text Mining. Firstly, we retrieve a corpus of scientific papers related to ED; then, we build two lexicons to recognize ED phases and data types; finally, we collect these entities within ED papers and map the relations between them.The methodology application allows the building of a network graph for visualizing the relations among data lexicon and ED lexicon. Then, we investigate the specific relations among data types and ED phases by building a heatmap to investigate data types from 3 different perspective.The insight coming from our analysis shows that ED studies have a great potential in the usage of many data sources, but also that there exist some gaps to be solved in order to reach a more effective data usage in the context of ED.

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“…In addition to fully autonomous agents that create designs automatically, there are different types of intelligent tools to support system design, including intelligent computer aided design (CAD) systems [19], [20], knowledge databases [21], [22], design assistants [23]- [26], and design critics [27], [28]. As can be seen, design automation tools include a wide range of behaviors and technologies in artificial intelligence, from bruteforce search, to explanation, machine learning, and human-agent interaction.…”

Section: A Artificial Intelligence In System Designmentioning

confidence: 99%

“…To further reduce the cognitive load of system engineers, other tools combine visualization and data mining algorithms that extract patterns, for instance in the form of if-then rules (e.g. "IF there is an atmospheric chemistry instrument in an AM orbit, THEN the architecture is likely to have low science benefit") [26], [35], [36]. The use of logical rules as data structure for these patterns has been used for decades in artificial intelligence, due to evidence that these rules are easy to understand by humans and that they may resemble how human experts solve problems [37].…”

Section: A Artificial Intelligence In System Designmentioning

confidence: 99%

See 1 more Smart Citation

Daphne: A Virtual Assistant for Designing Earth Observation Distributed Spacecraft Missions

Martin

Selva

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article describes Daphne, a virtual assistant for designing Earth observation distributed spacecraft missions. It is, to the best of our knowledge, the first virtual assistant for such application. The article provides a thorough description of Daphne, including its question answering system and the main features we have implemented to help system engineers design distributed spacecraft missions. In addition, the article describes a study performed at NASA's Jet Propulsion Laboratory (JPL) to assess the usefulness of Daphne in this use case. The study was conducted with N = 9 subjects from JPL, who were asked to work on a mission design task with two versions of Daphne, one that was fully featured implementing the cognitive assistance functions, and one that only had the features one would find in a traditional design space exploration tool. After the task, they filled out a standard user experience survey, completed a test to assess how much they learned about the task, and were asked a number of questions in a semi-structured exit interview. Results of the study suggest that Daphne can help improve performance during system design tasks compared to traditional tools, while keeping the system usable. However, the study also raises some concerns with respect to a potential reduction in human learning due to the use of the cognitive assistant. The article ends with a list of suggestions for future development of virtual assistants for space mission design. Index Terms-Distributed spacecraft missions, earth observation, machine learning, mixed initiative, virtual assistant. I. INTRODUCTION M OTIVATED by the challenges of system architecture in general and architecting distributed satellite missions (DSM) in particular, and inspired by the success of commercial virtual assistants (VA), such as Siri, Google Assistant, Alexa, or Mycroft, we have developed Daphne, the first VA-to the best of our knowledge-to support the high-level design of DSM. This article describes how Daphne can be used to design DSM and includes a quantitative validation study performed at NASA JPL where the test subjects had expertise in mission design. The contribution of this article is two fold. First, we introduce Daphne as a complete, open-source package for DSM tradespace analysis that includes the standard VA functionality, such as Manuscript

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iFEED: Interactive Feature Extraction for Engineering Design

Cited by 21 publications

References 26 publications

iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

Data for Engineering Design: Maps and Gaps

Daphne: A Virtual Assistant for Designing Earth Observation Distributed Spacecraft Missions

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