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Evaluation-perception of site attributes and plant species selection in the public urban green space of a compact city
Get PDFUnderstanding citizens’ evaluation of public urban green space (UGS) attributes and plant species features can inform greenspace design to meet public expectations. This study evaluated the public’s responses to UGS attributes and plant species in Hong Kong using a questionnaire survey of 827 adult respondents. Principal component analysis followed by cluster analysis were applied to analyze the data. The respondents were differentiated into three groups (ecological, eclectic, and pragmatic users) based on the evaluations of UGS attributes. Additionally, three clusters (conservation supporters, all-round perfectionists, and safety defenders) were classified based on evaluating plant species features. Plant knowledge and gender were the main factors associated with respondents’ evaluation profiles. Respondents with different expectations of UGS attributes harbored different evaluations of plant species features. The respondent groups agreed unanimously that similar plant species composition was deployed across UGS sites in Hong Kong. Respondents attaching importance to the conservation value of plant species (i.e., “conservation supporters”) were more concerned about plant species selection. The conservation supporters were dissatisfied with the current plant selection strategy. A zonation strategy for large UGS could cater to a broad range of user demands and create a socially-inclusive venue for residents. Alternatively, a collection of small UGS in a given district can cover a range of functions. The findings could inform a modified approach to UGS design and plant selection to satisfy the residents’ disparate expectations and needs
Interview - A Prelude to the Future Skills Community Event
Get PDFAn interview with industry expert Leon Yoong Leon Yoong will be speaking at the Panel Discussion session entitled Design Impact - Now and Beyond at the VPET Conference for Design on 9 December 2022. This is part of the Future Skills Community Event, which is held to commemorate the 40th Anniversary of the Vocational Training Council (VTC). Let\u27s find out what Yoong has to share before the big event
Damage Class Prediction using Machine Learning Algorithm
Get PDFThis paper aims to investigate the use of machine learning algorithms in vulnerability assessment of buildings and structures. Traditionally, dynamic performance of buildings under earthquakes is determined by means of non-linear time-history analyses. This method is accurate, but it is known as a time-consuming process and it requires advance knowledge on modelling. As an alternative, responses of building under earthquakes can be obtained using well-trained machine learning models. Nowadays, this is also called a data-driven approach. In the current study, machine learning models for damage class prediction are developed using the building data generated from numerous incremental dynamic analyses. Building models with variety of ground and structural parameters, such as peak ground acceleration, peak ground velocity, aspect ratio of building, member’s size, axial load ratio, etc., are considered in this study as the input parameters. Different past earthquake histories with increasing peak ground acceleration are used in the study. Material non-linearity is modelled using plastic hinge model, while geometric non-linearity is considered using P-delta effects. The effectiveness of typical machine learning models, including ensemble models and deep learning via artificial neural network, is investigated. The results reveal that well-prepared machine learning models are also capable of predicting structural response and damage level with adequate accuracy and minimum computation efforts. The performance of Random Forest and XGBoost is generally better. Other possible applications of machine learning models have been investigated as well in the study
A novel approach in predicting virtual garment fitting sizes with psychographic characteristics and 3D body measurements using artificial neural network and visualizing fitted bodies using generative adversarial network
Get PDFAdvances in technology have brought accessibility to garment product fitting procedures with a virtual fitting environment and, in due course, improved the supply chain socially, economically, and environmentally. 3D body measurements, garment sizes, and ease allowance are the necessary factors to ensure end-user satisfaction in the apparel industry. However, designers find it challenging to recognize customers’ motivations and emotions towards their preferred fit and define ease allowances in the virtual environment. This study investigates the variations of ease preferences for apparel sizes with body dimensions and psychological orientations by developing a virtual garment fitting prediction model. An artificial neural network (ANN) was employed to develop the model. The ANN model was proved to be effective in predicting ease preferences from two major components. A non-linear relationship was modeled among pattern parameters, body dimensions, and psychographic characteristics. Also, to visualize the fitted bodies, a generative adversarial network (GAN) was applied to generate 3D samples with the predicted pattern parameters from the ANN model. This project promotes mass customization using psychographic orientations and provides the perfect fit to the end users. New size-fitting data is generated for improved ease preference charts, and it enhances end-user satisfaction with garment fit
Predicting Virtual Garment Fitting Size With Psychographic Characteristics and 3D Body Measurements Using Artificial Neural Network and Visualizing Fitted Bodies Using Generative Adversarial Network
Get PDF3D virtual garment simulation technology is widely used in apparel industry nowadays with computer-aided manufacturing systems for the earlier stages of apparel design and product development process. The technological advances have brought convenience in garment product fitting procedures with virtual fitting environment, and eventually enhance the supply chain in the aspects of social, economic, and environmental aspects. Many studies have addressed the matters related to non-standardized selection on garment sizing, ease allowance for different selected groups, and use of 3D avatars for virtual fitting in the design and pre-production stages. Nevertheless, the current practice for designers is difficult for them to recognize the customers’ motivation and emotions towards their preferred fit in the virtual environment, leading to a hard time for the designers to determine the appropriate ease allowances for the end users. The present study is to investigate the variations on the ease preferences for the apparel sizes according to the body dimensions and psychological orientation of the subjects by developing a virtual garment fitting prediction model using artificial neural network (ANN). One hundred and twenty adult subjects were recruited to conduct 3D body scans and questionnaire survey for retrieving their body dimensions and psychographic characteristics. Segmentations were performed and each cluster was asked to evaluate the fitting preferences in a co-design interview on virtual garment simulation with a commercial software called Optitex. The results demonstrated that the ANN model is effective in predicting ease preferences from the body measurements and the psychological orientation of the subjects with high correlation coefficients, showing that a non-linear relationship is modelled among pattern parameters, body dimensions and psychographic characteristics. The results were visualized using generative adversarial network (GAN) to generate 3D samples. This new approach is significant to predict the garment sizes and pattern parameters with a highly accurate ANN model. Visualization of the predicted size with the implementation of GAN model is valuable to envision the garment details from 2D to 3D. The project has achieved the conception of mass customization and customer orientation by providing the perfect fit to the end users. Eventually, new size fitting data is generated for improved ease preference charts and augments end-user satisfaction in garment fit
Exhibition - When arts meet technology at Hylozoism
Get PDFHylozoism – the doctrine that all matter has life – is an unlikely head word to be seen on an exhibition poster. It does trigger the curiosity of passers-by to find out what it is all about
Theme - Reinvigorating The Soul Of Traditional Chinese Formal Wear
Get PDFIn Chinese traditions, the clothing system under Confucian rites and etiquettes is a symbol of civilisation, which is best represented by the formal attires of the royals and dignitaries. However, such tradition has faded with the succession of dynasties and the evolution of history. Very often, any residual legacy is only retained verbally by folk artisans. Chinese people do have a few more national costumes. The Zhongshan suit (or Mao suit) came from a Japanese imitation of European military uniforms. The more popular derivative known as Tang suit (or Zhong suit) is somehow restricted to a certain format and rather lacking in depth. Herman Lee is doing what very few people would in this area. He is adopting a scientific approach and applying modern technology to uncover the origins and standards of the clothing civilisation that Chinese people are proud of