How Can Machine Learning Models Be Used for Subjective Assessment of Safety and Comfort: Application on Free Lane Change Maneuver

Durukal, Batuhan; Eren, Mert; Çetin, Orkun; Karabiyik, Egemen; Zengin, Namik; Yetkin, Sarp Kaya

doi:10.1109/scisisis55246.2022.10001888

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…This decision ensures the efficiency and intelligence of the comprehensive evaluation system based on separate rating and ML theory …”

Section: Resultsmentioning

confidence: 99%

“…This decision ensures the efficiency and intelligence of the comprehensive evaluation system based on separate rating and ML theory. 39 Weights obtained through the XGBoost algorithm were then utilized to aid in calculating the overall device performance score. This is because the device's performance is influenced collectively by its transmittance modulation amplitude, coloring time, and bleaching time.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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Machine Learning-Guided Performance Evaluation of an All-Liquid Electrochromic Device

Lai,

Cai,

et al. 2024

ACS Appl. Mater. Interfaces

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Electrochromic devices, capable of modulating light transmittance under the influence of an electric field, have garnered significant interest in the field of smart windows and car rearview mirrors. However, the development of high-performance electrochromic devices via large-scale explorations under miscellaneous experimental settings remains challenging and is still an urgent problem to be solved. In this study, we employed a two-step machine learning approach, combining machine learning algorithms such as KNN and XGBoost with the reality of electrochromic devices, to construct a comprehensive evaluation system for electrochromic materials. Utilizing our predictive evaluation system, we successfully screened the preparation conditions for the best-performing device, which was experimentally verified to have a high transmittance modulation amplitude (62.6%) and fast response time (5.7 s/7.1 s) at 70 A/m2. To test its stability, experiments over a long cycle time (1000 cycles) are performed. In this study, we develop an innovative framework for assessing the performance of electrochromic material devices. Our approach effectively filters experimental samples based on their distinct properties, substantially minimizing the expenditure of human and material resources in electrochromic research. Our approach to a mathematical machine learning evaluation framework for device performance has effectively propelled and informed research in electrochromic devices.

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“…This decision ensures the efficiency and intelligence of the comprehensive evaluation system based on separate rating and ML theory …”

Section: Resultsmentioning

confidence: 99%

Section: ■ Results and Discussionmentioning

confidence: 99%

Machine Learning-Guided Performance Evaluation of an All-Liquid Electrochromic Device

Lai,

Cai,

et al. 2024

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

How Can Machine Learning Models Be Used for Subjective Assessment of Safety and Comfort: Application on Free Lane Change Maneuver

Cited by 1 publication

References 14 publications

Machine Learning-Guided Performance Evaluation of an All-Liquid Electrochromic Device

Machine Learning-Guided Performance Evaluation of an All-Liquid Electrochromic Device

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