Achieving eco-innovative smart glass design with the integration of opinion mining, QFD and TRIZ
Bibliographic record
Abstract
Abstract As technology advances, the release of new smart devices accelerates, and thus the disposal of electronic devices has increased exponentially, worsening the gravity of electronic waste all over the world. It is eager to consider eco-friendly product design in order to sustain global supply chains. When addressing customer requirements in new product design, sustainable design thinking should thus be incorporated to create not only innovative, but also sustainable products. The purpose of this research is to build a systematic approach to achieve sustainable new product development. To examine the system effectiveness, smart glasses which are rapidly developed in the era of the metaverse is considered in a case study. By utilizing opinion mining approaches, including wordcloud and TextRank algorithm, to decipher customer needs, quality function deployment (QFD) is used to investigate the existing state of smart glasses, as well as translate customers’ needs into technical requirements that serve as the basis for sustainable new product design. Subsequently, the theory of inventive problem solving (TRIZ) is applied to formulate the contradiction matrix between improvement and preservation areas in order to reach the eco-innovation in a systematic manner. Beyond the theoretical investigation, this study presents the application of the proposed system for the sustainable design of smart glasses, so as to support the next generation of smart glasses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".