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Attribute-Sentiment-Guided Summarization of User Opinions From Online Reviews
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Eliciting informative user opinions from online reviews is a key success factor for innovative product design and development. The unstructured, noisy, and verbose nature of user reviews, however, often complicate large-scale ...
DDE-GAN: Integrating a Data-Driven Design Evaluator Into Generative Adversarial Networks for Desirable and Diverse Concept Generation
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Generative adversarial networks (GANs) have shown remarkable success in various generative design tasks, from topology optimization to material design, and shape parametrization. However, most generative design approaches ...
Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: The vision of Industry 4.0 is to materialize the notion of a lot-size of one through enhanced adaptability and resilience of manufacturing and logistics operations to dynamic changes or deviations on the shop floor. This ...
Design Knowledge as Attention Emphasizer in Large Language Model-Based Sentiment Analysis
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Aspect-based sentiment analysis (ABSA) enables a systematic identification of user opinions on particular aspects, thus improving the idea creation process in the initial stages of a product/service design. Large language ...
Eliciting Attribute-Level User Needs From Online Reviews With Deep Language Models and Information Extraction
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Eliciting user needs for individual components and features of a product or a service on a large scale is a key requirement for innovative design. Synthesizing data as an initial discovery phase of a design process is ...