创造运动品牌店面独特的消费体验

在体验经济时代,消费者更渴望在选购服装时能获得一种美好而难忘的体验。基于此,本文提出联想式符号陈列法,主要研究运动品牌店面各区域陈列如何以运动场所或运动项目为灵感,营造出一种运动品牌特有的购物氛围,通过该陈列法的展示来引发消费者对产品的注意,以及感受到相应的运动场景,引起消费者共鸣,使消费者获得特殊的运动品牌符号信息和消费体验。     

设计艺术创造过程的研究

人类从远古走到今天,对设计艺术创造过程的认识一步步深入,随着社会经济、科技、文化的发展人们对设计艺术的创造也在不断的深化和完善。生活水平的提高使得人们对设计艺术也提出了更高的要求,本文就设计艺术创造过程的几个方面阐述自己的看法。从文中你可以觉察到设计艺术创造过程在我们设计艺术中的重要性。     

QoE enhancement of the industrial metaverse based on Mixed Reality application optimization

As a comprehensive integration of many new-generation information technologies, the metaverse has become a research hotspot that has attracted much attention. As a part of the metaverse, the industrial metaverse is expected to break through the constraints of space and time and promote high-quality industrial development. The industrial metaverse is human-centric, so its quality of experience (QoE) is a key topic. As one of the enabling technologies of the industrial metaverse, Mixed Reality (MR) can seamlessly integrate virtual information with the physical world and is widely regarded as an important window to the industrial metaverse. In close integration with other enabling technologies, industrial MR applications can be seen as a path toward the realization of the industrial metaverse; thus, the optimization of industrial MR applications can effectively achieve the QoE enhancement of the industrial metaverse. Based on the analysis of existing research and the characteristics of industrial scenarios, consistency, authenticity, smoothness, and comfort are identified as the factors that influence the user experience (UX) of industrial MR applications. Specific optimization methods for industrial MR applications are proposed to improve the UX with regard to these four factors. To verify the effectiveness of the proposed methods, a QoE evaluation model of the industrial metaverse based on the fuzzy analytic hierarchy process (FAHP) is established. Moreover, an industrial metaverse prototype for longwall mining that incorporates the proposed methods is developed and its QoE is evaluated. The results show that the proposed optimization methods for industrial MR applications significantly enhance the QoE in the industrial metaverse, and can provide better services for users in industrial systems, thus better serving these systems.     

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.     

Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.     

Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Continual learning (CL) studies the problem of learning to accumulate knowledge over time from a stream of data. A crucial challenge is that neural networks suffer from performance degradation on previously seen data, known as catastrophic forgetting, due to allowing parameter sharing. In this work, we consider a more practical online class-incremental CL setting, where the model learns new samples in an online manner and may continuously experience new classes. Moreover, prior knowledge is unavailable during training and evaluation. Existing works usually explore sample usages from a single dimension, which ignores a lot of valuable supervisory information. To better tackle the setting, we propose a novel replay-based CL method, which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge. Specifically, besides the previous raw samples, we store the corresponding logits and features in the memory. Furthermore, to imitate the prediction of the past model, we construct extra constraints by leveraging multi-level information stored in the memory. With the same number of samples for replay, our method can use more past knowledge to prevent interference. We conduct extensive evaluations on several popular CL datasets, and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory. We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.      

Predicting Mobile Cross-Platform Adaptation Using a Hybrid Sem–ANN Approach

Owing to constant changes in user needs, new technologies have been introduced to keep pace by building sustainable applications. Researchers and practitioners are keen to understand the factors that create an attractive user interface. Although the use of cross-platform applications and services is increasing, limited research has examined and evaluated cross-platforms for developing mobile applications for different operating systems. This study evaluates cross-platform features, identifying the main factors that help to create an attractive user adaptation when building sustainable applications for both Android and iOS. Flutter and React Native were selected so end-users could test their features using the cross-platform usability assessment model. Usability, satisfaction, and navigation were tested to measure the cross-platform adaptation and end-user experience. The data were analyzed using hybrid structural equation modeling (SEM) and artificial neural network (ANN) approaches. The study results show that usability and navigation both have a positive effect on adaptation on Flutter and React Native, while satisfaction only has an effect on Flutter. The navigation variable was also the most significant predictor of adaptation for both models. This study has several implications and makes contributions to the research field, to developers, and to end-users.     

以体验为中心的产品设计价值研究

目的研究"体验"对定义产品功能、形态,挖掘创新机遇点,提升产品价值等方面的作用与影响。方法从关注人的心理、情感出发,探究人使用产品的具体交互行为,和人使用产品前、中、后的全流程体验需求;将产品、服务、体验作为整体的设计对象进行研究。结论体验经济时代,人们喜欢的不单是产品本身,还有产品所处的场景,以及场景中自己存在的情感;体验的好坏在人们对产品的使用认知、购买决策和品牌忠诚度方面具有重要影响;从体验的角度出发设计产品,对挖掘产品差异化、产品创新机遇点和创新商业模式具有指导意义。     

体验消费与染织艺术的传承与发展

目的在现代商业环境及文化创意产业发展背景下,为传统染织艺术文化的传承和发展探寻可行之路。方法通过体验消费的方法和模式研究,分析消费者在体验消费过程的行为和心理活动,并结合传统手工染织艺术的产品和生产过程的特点,探讨现代体验消费的商业模式对染织艺术传承和发展的作用与影响。结论传统染织艺术文化可以通过艺术沙龙的形式,在高雅的商业艺术环境中展现手工染织的艺术魅力,从而让传统染织手工艺重新找到生存的土壤,使染织艺术得以传承和发展。     

数字化背景下汽车仪表盘界面设计研究

目的研究汽车数字化仪表盘界面因素对驾驶员驾驶体验的作用和影响。方法分析现有汽车仪表盘的研究现状和问题,选取5个具有代表性的样本,从仪表盘样式、整体布局、信息架构、基本信息列表4个方面,同时搭配色彩比例、风格类型进行纵向与横向分析研究。结论通过对选取的5个样本的研究和分析,提出在数字化汽车仪表盘的设计过程中,遵循布局简洁、色彩准确、体验丰富的设计规律,具有重要的实践意义和价值。