6,455 research outputs found

    GarmentCode: Programming Parametric Sewing Patterns

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    Garment modeling is an essential task of the global apparel industry and a core part of digital human modeling. Realistic representation of garments with valid sewing patterns is key to their accurate digital simulation and eventual fabrication. However, little-to-no computational tools provide support for bridging the gap between high-level construction goals and low-level editing of pattern geometry, e.g., combining or switching garment elements, semantic editing, or design exploration that maintains the validity of a sewing pattern. We suggest the first DSL for garment modeling -- GarmentCode -- that applies principles of object-oriented programming to garment construction and allows designing sewing patterns in a hierarchical, component-oriented manner. The programming-based paradigm naturally provides unique advantages of component abstraction, algorithmic manipulation, and free-form design parametrization. We additionally support the construction process by automating typical low-level tasks like placing a dart at a desired location. In our prototype garment configurator, users can manipulate meaningful design parameters and body measurements, while the construction of pattern geometry is handled by garment programs implemented with GarmentCode. Our configurator enables the free exploration of rich design spaces and the creation of garments using interchangeable, parameterized components. We showcase our approach by producing a variety of garment designs and retargeting them to different body shapes using our configurator.Comment: Supplementary video: https://youtu.be/16Yyr2G9_6E

    Outfit Recommender System

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    The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommendation systems on retail websites generate a lot of this revenue. Thus, improving recommendation systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. We propose an event-based clothing recommendation system which uses object detection. We train a model to identify nine events/scenarios that a user might attend: White Wedding, Indian Wedding, Conference, Funeral, Red Carpet, Pool Party, Birthday, Graduation and Workout. We train another model to detect clothes out of fifty-three categories of clothes worn at the event. Object detection gives a mAP of 84.01. Nearest neighbors of the clothes detected are recommended to the user

    Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

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    Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

    Millennial Consumers’ Purchase Intention for Eco-Fashion Apparel: A Study from Southern China

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    This study attempts to understand the role of value perceptions and environmental attitudes in influencing millennial consumers’ intentions to buy eco-fashion apparel in Southern China. A total of 385 questionnaire responses were collected in Nanning via snowball sampling and convenience sampling techniques. The PLS-SEM method was applied to evaluate the data. The findings show that status value presents the strongest relationship with environmental attitude (β=.308, t=7.209, p<.01), followed by uniqueness value (β=.213, t=3.826, p<.01), materialism (β=.242, t=3.398, p<.01), price-quality perception (β=.089, t=2.209, p<.05), and conspicuous value (β=.150, t=2.171, p<.05). Moreover, environmental attitude exhibits the most significant influence on purchase intentions (β=.765, t=31.730, p<.01). Thus, corresponding implications are discussed.

    Uncovering GPTS with Patent Data

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    This paper asks the question: Can we see evidence of General Purpose Technologies in patent data? Using data on three million US patents granted between 1967 and 1999, and their citations received between 1975 and 2002, we construct a number of measures of GPTs, including generality, number of citations, and patent class growth, for patents themselves and for the patents that cite the patents. A selection of the top twenty patents in the tails of the distribution of several of these measures yields a set of mostly ICT technologies, of which the most important are those underlying transactions on the internet and object-oriented software. We conclude with a brief discussion of the problems we encountered in developing our measures and suggestions for future work in this area.

    Modelling an End to End Supply Chain system Using Simulation

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    Within the current uncertain environment industries are predominantly faced with various challenges resulting in greater need for skilled management and adequate technique as well as tools to manage Supply Chains (SC) efficiently. Derived from this observation is the need to develop a generic/reusable modelling framework that would allow firms to analyse their operational performance over time (Mackulak and Lawrence 1998, Beamon and Chen 2001, Petrovic 2001, Lau et al. 2008, Khilwani et al. 2011, Cigollini et al. 2014). However for this to be effectively managed the simulation modelling efforts should be directed towards identifying the scope of the SC and the key processes performed between players. Purpose: The research attempts to analyse trends in the field of supply chain modelling using simulation and provide directions for future research by reviewing existing Operations Research/Operations Management (OR/OM) literature. Structural and operational complexities as well as different business processes within various industries are often limiting factors during modelling efforts. Successively, this calls for the end to end (E2E) SC modelling framework where the generic processes, related policies and techniques could be captured and supported by the powerful capabilities of simulation. Research Approach: Following Mitroff’s (1974) scientific inquiry model and Sargent (2011) this research will adopt simulation methodology and focus on systematic literature review in order to establish generic OR processes and differentiate them from those which are specific to certain industries. The aim of the research is provide a clear and informed overview of the existing literature in the area of supply chain simulation. Therefore through a profound examination of the selected studies a conceptual model will be design based on the selection of the most commonly used SC Processes and simulation techniques used within those processes. The description of individual elements that make up SC processes (Hermann and Pundoor 2006) will be defined using building blocks, which are also known as Process Categories. Findings and Originality: This paper presents an E2E SC simulation conceptual model realised through means of systematic literature review. Practitioners have adopted the term E2E SC while this is not extensively featured within academic literature. The existing SC studies lack generality in regards to capturing the entire SC within one methodological framework, which this study aims to address. Research Impact: A systematic review of the supply chain and simulation literature takes an integrated and holistic assessment of an E2E SC, from market-demand scenarios through order management and planning processes, and on to manufacturing and physical distribution. Thus by providing significant advances in understanding of the theory, methods used and applicability of supply chain simulation, this paper will further develop a body of knowledge within this subject area. Practical Impact: The paper will empower practitioners’ knowledge and understanding of the supply chain processes characteristics that can be modelled using simulation. Moreover it will facilitate a selection of specific data required for the simulation in accordance to the individual needs of the industry

    The Impact of Experiential Augmented Reality Applications on Fashion Purchase Intention

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    Utilizing the stimulus-organism-response (SOR) model, the purpose of this study is to examine the effects of augmented reality (AR) (specifically augmentation) on consumers’ affective and behavioral response and to assess whether consumers’ hedonic motivation for shopping moderates this relationship. An experiment using the manipulation of AR and no AR was conducted with 162 participants aged between 18 and 35. Participants were recruited through snowball sampling and randomly assigned to the control or stimulus group. The hypothesized associations were analyzed using linear regression with bootstrapping. The paper demonstrates the benefit of using an experiential AR retail application (app) to positively impact purchase intention. The results show this effect is mediated by positive affective response. Furthermore, hedonic shopping motivation moderates the relationship between augmentation and the positive affective response. Because of the chosen research approach, the results may lack generalizability to other forms of augmentation. Therefore, researchers are encouraged to test the proposed model using different types of AR stimuli. Furthermore, replication of the study with other populations would increase the generalizability of the findings. Results of this study provide a valuable reference for retailers of the benefits of using AR when attempting to optimize experiential value in online environments. The study contributes to experiential retail and consumer purchase behavior research by deepening the conceptualization of the impact of experiential technologies, more specifically AR apps, by considering the role of hedonic shopping motivations.Peer reviewe
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