The Experts below are selected from a list of 63858 Experts worldwide ranked by ideXlab platform
Junjie Zhang - One of the best experts on this subject based on the ideXlab platform.
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an interactive Knowledge Based recommender system for fashion product Design in the big data environment
Information Sciences, 2020Co-Authors: Min Dong, Xianyi Zeng, Ludovic Koehl, Junjie ZhangAbstract:Abstract In this paper, we originally propose an interactive, Knowledge-Based Design recommender system (IKDRS) for relevant personalised fashion product Design schemes with their virtual demonstrations for a specific consumer. This system enables the iterative interaction between virtual product demonstration and the Designer’s professional Knowledge and perception in order to find the best existing Design solution, i.e. combination of basic garment elements. To develop this system, the anthropometric data and Designer’s perception of body shapes are first acquired by using a 3D body scanning system and a sensory evaluation procedure. Next, an instrumental experiment is realised for measuring the technical parameters of fabrics and five sensory experiments are carried out in order to acquire Design Knowledge. The acquired data are used to classify body shapes and model the relations between human bodies, fashion themes and Design factors by using fuzzy techniques. From these models, we set up an ontology-Based Design Knowledge Base, including key data and relevant relation models. This Knowledge Base can be updated in a big data environment by progressively learning from new Design cases. On this basis, we propose an interactive, personalised Design recommender system. This system works through a newly proposed Design process: consumers’ emotional requirement identification – Design schemes generation – recommender – 3D virtual prototype display and evaluation – Design factors adjustment. This process can be performed repeatedly until the Designer is satisfied. The proposed system has been validated through a number of successful real Design cases.
Xianyi Zeng - One of the best experts on this subject based on the ideXlab platform.
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Development of an Intelligent Data-Driven System to Recommend Personalized Fashion Design Solutions
'MDPI AG', 2021Co-Authors: Shukla Sharma, Pascal Bruniaux, Xianyi Zeng, Ludovic Koehl, Zhujun WangAbstract:In the context of fashion/textile innovations towards Industry 4.0, a variety of digital technologies, such as 3D garment CAD, have been proposed to automate, optimize Design and manufacturing processes in the organizations of involved enterprises and supply chains as well as services such as marketing and sales. However, the current digital solutions rarely deal with key elements used in the fashion industry, including professional Knowledge, as well as fashion and functional requirements of the customer and their relations with product technical parameters. Especially, product Design plays an essential role in the whole fashion supply chain and should be paid more attention to in the process of digitalization and intelligentization of fashion companies. In this context, we originally developed an interactive fashion and garment Design system by systematically integrating a number of data-driven services of garment Design recommendation, 3D virtual garment fitting visualization, Design Knowledge Base, and Design parameters adjustment. This system enables close interactions between the Designer, consumer, and manufacturer around the virtual product corresponding to each Design solution. In this way, the complexity of the product Design process can drastically be reduced by directly integrating the consumer’s perception and professional Designer’s Knowledge into the garment computer-aided Design (CAD) environment. Furthermore, for a specific consumer profile, the related computations (Design solution recommendation and Design parameters adjustment) are performed by using a number of intelligent algorithms (BIRCH, adaptive Random Forest algorithms, and association mining) and matching with a formalized Design Knowledge Base. The proposed interactive Design system has been implemented and then exposed through the REST API, for Designing garments meeting the consumer’s personalized fashion requirements by repeatedly running the cycle of Design recommendation—virtual garment fitting—online evaluation of Designer and consumer—Design parameters adjustment—Design Knowledge Base creation, and updating. The effectiveness of the proposed system has been validated through a business case of personalized men’s shirt Design
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an interactive Knowledge Based recommender system for fashion product Design in the big data environment
Information Sciences, 2020Co-Authors: Min Dong, Xianyi Zeng, Ludovic Koehl, Junjie ZhangAbstract:Abstract In this paper, we originally propose an interactive, Knowledge-Based Design recommender system (IKDRS) for relevant personalised fashion product Design schemes with their virtual demonstrations for a specific consumer. This system enables the iterative interaction between virtual product demonstration and the Designer’s professional Knowledge and perception in order to find the best existing Design solution, i.e. combination of basic garment elements. To develop this system, the anthropometric data and Designer’s perception of body shapes are first acquired by using a 3D body scanning system and a sensory evaluation procedure. Next, an instrumental experiment is realised for measuring the technical parameters of fabrics and five sensory experiments are carried out in order to acquire Design Knowledge. The acquired data are used to classify body shapes and model the relations between human bodies, fashion themes and Design factors by using fuzzy techniques. From these models, we set up an ontology-Based Design Knowledge Base, including key data and relevant relation models. This Knowledge Base can be updated in a big data environment by progressively learning from new Design cases. On this basis, we propose an interactive, personalised Design recommender system. This system works through a newly proposed Design process: consumers’ emotional requirement identification – Design schemes generation – recommender – 3D virtual prototype display and evaluation – Design factors adjustment. This process can be performed repeatedly until the Designer is satisfied. The proposed system has been validated through a number of successful real Design cases.
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virtual reality Based collaborative Design method for Designing customized garment for disabled people with scoliosis
International Journal of Clothing Science and Technology, 2017Co-Authors: Yan Hong, Pascal Bruniaux, Xianyi Zeng, Kaixuan Liu, Yan Chen, Min DongAbstract:Purpose The purpose of this paper is to present a new collaborative Design-Based method for Designing customized garments, aimed at the physically disabled people with scoliosis. Design/methodology/approach The proposed method is Based on the virtual human model created using a 3D body scanner, permitting to simulate the consumer’s morphological shape with atypical physical deformations. Next, customized 2D and 3D virtual garment prototyping tools will be used to create products through interactions between the consumer, Designer and pattern maker. The general principle of the proposed Design method is Based on the following sequence: Design-display-evaluation-adjustment. After running the sequence for a number of times, the final Design solution, which will be approved by both the Designer and consumer, can be easily identified. Findings Design Knowledge, which is already applied to normal body shapes successfully can be applied to 3D garment Design using the concept which is Based on collaborative Design. Through this process, the classical 2D garment Design Knowledge, especially 2D patterns and Design rules, can be modified and applied according to a normalized virtual garment sensory evaluation procedure quantitatively. This evaluation procedure, interactively performed by the Designer and consumer, can permit to adapt the finished product to disabled people afflicted with severe scoliosis. The proposed method is also validated to be more advanced compared to 2D-to-3D virtual CAD Design method, especially for atypical morphologies. Originality/value As a co-Design method, 3D virtual draping and sensory evaluation can fully satisfy the interaction between the garment Design technical space and perceptual space of the finished garments ensuring desired 3D garment fit effect by adjustment of technical parameters. 3D scanning technology is used to generate a complete digitalized 3D human model, permitting to extract the main features of body shapes without accurate measurements. As a Knowledge-Based Design process, both the fashion Design Knowledge and the pattern making Knowledge will be extracted to provide inspirations and references. Successful Design solutions will be incorporated into the fashion Design Knowledge Base in order to generate new Design rules and enhance professional Design Knowledge.
Ludovic Koehl - One of the best experts on this subject based on the ideXlab platform.
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Development of an Intelligent Data-Driven System to Recommend Personalized Fashion Design Solutions
'MDPI AG', 2021Co-Authors: Shukla Sharma, Pascal Bruniaux, Xianyi Zeng, Ludovic Koehl, Zhujun WangAbstract:In the context of fashion/textile innovations towards Industry 4.0, a variety of digital technologies, such as 3D garment CAD, have been proposed to automate, optimize Design and manufacturing processes in the organizations of involved enterprises and supply chains as well as services such as marketing and sales. However, the current digital solutions rarely deal with key elements used in the fashion industry, including professional Knowledge, as well as fashion and functional requirements of the customer and their relations with product technical parameters. Especially, product Design plays an essential role in the whole fashion supply chain and should be paid more attention to in the process of digitalization and intelligentization of fashion companies. In this context, we originally developed an interactive fashion and garment Design system by systematically integrating a number of data-driven services of garment Design recommendation, 3D virtual garment fitting visualization, Design Knowledge Base, and Design parameters adjustment. This system enables close interactions between the Designer, consumer, and manufacturer around the virtual product corresponding to each Design solution. In this way, the complexity of the product Design process can drastically be reduced by directly integrating the consumer’s perception and professional Designer’s Knowledge into the garment computer-aided Design (CAD) environment. Furthermore, for a specific consumer profile, the related computations (Design solution recommendation and Design parameters adjustment) are performed by using a number of intelligent algorithms (BIRCH, adaptive Random Forest algorithms, and association mining) and matching with a formalized Design Knowledge Base. The proposed interactive Design system has been implemented and then exposed through the REST API, for Designing garments meeting the consumer’s personalized fashion requirements by repeatedly running the cycle of Design recommendation—virtual garment fitting—online evaluation of Designer and consumer—Design parameters adjustment—Design Knowledge Base creation, and updating. The effectiveness of the proposed system has been validated through a business case of personalized men’s shirt Design
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an interactive Knowledge Based recommender system for fashion product Design in the big data environment
Information Sciences, 2020Co-Authors: Min Dong, Xianyi Zeng, Ludovic Koehl, Junjie ZhangAbstract:Abstract In this paper, we originally propose an interactive, Knowledge-Based Design recommender system (IKDRS) for relevant personalised fashion product Design schemes with their virtual demonstrations for a specific consumer. This system enables the iterative interaction between virtual product demonstration and the Designer’s professional Knowledge and perception in order to find the best existing Design solution, i.e. combination of basic garment elements. To develop this system, the anthropometric data and Designer’s perception of body shapes are first acquired by using a 3D body scanning system and a sensory evaluation procedure. Next, an instrumental experiment is realised for measuring the technical parameters of fabrics and five sensory experiments are carried out in order to acquire Design Knowledge. The acquired data are used to classify body shapes and model the relations between human bodies, fashion themes and Design factors by using fuzzy techniques. From these models, we set up an ontology-Based Design Knowledge Base, including key data and relevant relation models. This Knowledge Base can be updated in a big data environment by progressively learning from new Design cases. On this basis, we propose an interactive, personalised Design recommender system. This system works through a newly proposed Design process: consumers’ emotional requirement identification – Design schemes generation – recommender – 3D virtual prototype display and evaluation – Design factors adjustment. This process can be performed repeatedly until the Designer is satisfied. The proposed system has been validated through a number of successful real Design cases.
Min Dong - One of the best experts on this subject based on the ideXlab platform.
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an interactive Knowledge Based recommender system for fashion product Design in the big data environment
Information Sciences, 2020Co-Authors: Min Dong, Xianyi Zeng, Ludovic Koehl, Junjie ZhangAbstract:Abstract In this paper, we originally propose an interactive, Knowledge-Based Design recommender system (IKDRS) for relevant personalised fashion product Design schemes with their virtual demonstrations for a specific consumer. This system enables the iterative interaction between virtual product demonstration and the Designer’s professional Knowledge and perception in order to find the best existing Design solution, i.e. combination of basic garment elements. To develop this system, the anthropometric data and Designer’s perception of body shapes are first acquired by using a 3D body scanning system and a sensory evaluation procedure. Next, an instrumental experiment is realised for measuring the technical parameters of fabrics and five sensory experiments are carried out in order to acquire Design Knowledge. The acquired data are used to classify body shapes and model the relations between human bodies, fashion themes and Design factors by using fuzzy techniques. From these models, we set up an ontology-Based Design Knowledge Base, including key data and relevant relation models. This Knowledge Base can be updated in a big data environment by progressively learning from new Design cases. On this basis, we propose an interactive, personalised Design recommender system. This system works through a newly proposed Design process: consumers’ emotional requirement identification – Design schemes generation – recommender – 3D virtual prototype display and evaluation – Design factors adjustment. This process can be performed repeatedly until the Designer is satisfied. The proposed system has been validated through a number of successful real Design cases.
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virtual reality Based collaborative Design method for Designing customized garment for disabled people with scoliosis
International Journal of Clothing Science and Technology, 2017Co-Authors: Yan Hong, Pascal Bruniaux, Xianyi Zeng, Kaixuan Liu, Yan Chen, Min DongAbstract:Purpose The purpose of this paper is to present a new collaborative Design-Based method for Designing customized garments, aimed at the physically disabled people with scoliosis. Design/methodology/approach The proposed method is Based on the virtual human model created using a 3D body scanner, permitting to simulate the consumer’s morphological shape with atypical physical deformations. Next, customized 2D and 3D virtual garment prototyping tools will be used to create products through interactions between the consumer, Designer and pattern maker. The general principle of the proposed Design method is Based on the following sequence: Design-display-evaluation-adjustment. After running the sequence for a number of times, the final Design solution, which will be approved by both the Designer and consumer, can be easily identified. Findings Design Knowledge, which is already applied to normal body shapes successfully can be applied to 3D garment Design using the concept which is Based on collaborative Design. Through this process, the classical 2D garment Design Knowledge, especially 2D patterns and Design rules, can be modified and applied according to a normalized virtual garment sensory evaluation procedure quantitatively. This evaluation procedure, interactively performed by the Designer and consumer, can permit to adapt the finished product to disabled people afflicted with severe scoliosis. The proposed method is also validated to be more advanced compared to 2D-to-3D virtual CAD Design method, especially for atypical morphologies. Originality/value As a co-Design method, 3D virtual draping and sensory evaluation can fully satisfy the interaction between the garment Design technical space and perceptual space of the finished garments ensuring desired 3D garment fit effect by adjustment of technical parameters. 3D scanning technology is used to generate a complete digitalized 3D human model, permitting to extract the main features of body shapes without accurate measurements. As a Knowledge-Based Design process, both the fashion Design Knowledge and the pattern making Knowledge will be extracted to provide inspirations and references. Successful Design solutions will be incorporated into the fashion Design Knowledge Base in order to generate new Design rules and enhance professional Design Knowledge.
Y Lam - One of the best experts on this subject based on the ideXlab platform.
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environmentally conscious decision making in engineering Design
International Conference on Robotics and Automation, 2004Co-Authors: Mingxi Tang, Y LamAbstract:In modern Design, environmentally conscious issues such as energy saving, recycling properties and maintenance etc. must be considered in the product development process. Useful environmentally conscious information can be captured, analyzed and organized as Design Knowledge. Early Design intent of a Designer can be propagated to down stream Design activities using this Knowledge as an evaluation index. In this context, a Design intent management (DIM) module associated with a life-cycle assessment (LCA) inventory, a feature coding Design Knowledge Base, and an automatic function-to-form mapping module are developed concurrently and embedded in an integrated environmentally conscious Design (IECD) system being developed in the Design Technology Research Centre of School of Design at the Hong Kong Polytechnic University. A case study involving the Design intent management module, the propagation module to follow-up Design activities and a Design Knowledge Base supporting decision making in engineering Design process are presented In this work. The capabilities of the current system being implemented are highlighted.