Optimizing Outdoor Micro-Space Design for Prolonged Activity Duration: A Study Integrating Rough Set Theory and the PSO-SVR Algorithm DOI Creative Commons
Jingwen Tian, Zimo Chen,

Lingling Yuan

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(12), P. 3950 - 3950

Published: Dec. 12, 2024

This study proposes an optimization method based on Rough Set Theory (RST) and Particle Swarm Optimization–Support Vector Regression (PSO-SVR), aimed at enhancing the emotional dimension of outdoor micro-space (OMS) design, thereby improving users’ activity duration preferences experiences. OMS, as a key element in modern urban significantly enhances residents’ quality life promotes public health. Accurately understanding predicting needs is core challenge optimizing OMS. In this study, Kansei Engineering (KE) framework applied, using fuzzy clustering to reduce dimensionality descriptors, while RST employed for attribute reduction select five design features that influence emotions. Subsequently, PSO-SVR model applied establish nonlinear mapping relationship between these emotions, optimal configuration OMS design. The results indicate optimized intention stay space, reflected by higher ratings descriptors increased longer duration, all exceeding median score scale. Additionally, comparative analysis shows outperforms traditional methods (e.g., BPNN, RF, SVR) terms accuracy generalization predictions. These findings demonstrate proposed effectively improves performance offers solid along with practical guidance future space innovative contribution lies data-driven integrates machine learning KE. not only new theoretical perspective but also establishes scientific accurately incorporate into process. contributes knowledge field health well-being, provides foundation applications different environments.

Language: Английский

PSR-GAN: a product concept sketch rendering method based on generative adversarial network and colour tags DOI
Wen-Yu Tang, Ze‐Rui Xiang, Shulan Yu

et al.

Journal of Engineering Design, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: Jan. 22, 2025

Language: Английский

Citations

0
IXAI: generative design of automotive styling based on inception convolution with explainable AI DOI
Zhenyu Wang, Zongyang Lv, Jianmin Wang

et al.

Journal of Engineering Design, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 29

Published: March 24, 2025

Language: Английский

Citations

0
An optimization method based on improved ant colony algorithm for complex product change propagation path DOI Creative Commons
Ruizhao Zheng,

Mingqun Liu,

Zhang Yon

et al.

Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 23, P. 200412 - 200412

Published: July 2, 2024

Due to factors such as changing customer demands and supply disruptions, product design changes are inevitable during the development process. Selecting an appropriate change propagation path not only maintains performance but also reduces generation time cost. This paper investigates intelligent optimization method for complex paths. Firstly, considering duration, cost, impact degree on performance, a parameter network model of is constructed based linkage relationship between parts. Secondly, solve this improved ant colony algorithm proposed. Finally, effectiveness proposed validated problem TV products at Skyworth RGB Co., Ltd. Experimental results demonstrate that can generate highly competitive optimal paths products.

Language: Английский

Citations

2
Optimizing Outdoor Micro-Space Design for Prolonged Activity Duration: A Study Integrating Rough Set Theory and the PSO-SVR Algorithm DOI Creative Commons
Jingwen Tian, Zimo Chen,

Lingling Yuan

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(12), P. 3950 - 3950

Published: Dec. 12, 2024

This study proposes an optimization method based on Rough Set Theory (RST) and Particle Swarm Optimization–Support Vector Regression (PSO-SVR), aimed at enhancing the emotional dimension of outdoor micro-space (OMS) design, thereby improving users’ activity duration preferences experiences. OMS, as a key element in modern urban significantly enhances residents’ quality life promotes public health. Accurately understanding predicting needs is core challenge optimizing OMS. In this study, Kansei Engineering (KE) framework applied, using fuzzy clustering to reduce dimensionality descriptors, while RST employed for attribute reduction select five design features that influence emotions. Subsequently, PSO-SVR model applied establish nonlinear mapping relationship between these emotions, optimal configuration OMS design. The results indicate optimized intention stay space, reflected by higher ratings descriptors increased longer duration, all exceeding median score scale. Additionally, comparative analysis shows outperforms traditional methods (e.g., BPNN, RF, SVR) terms accuracy generalization predictions. These findings demonstrate proposed effectively improves performance offers solid along with practical guidance future space innovative contribution lies data-driven integrates machine learning KE. not only new theoretical perspective but also establishes scientific accurately incorporate into process. contributes knowledge field health well-being, provides foundation applications different environments.

Language: Английский

Citations

1