“回收感动”——博物馆功能的再思考与博物馆信息交互平台之构想

博物馆因人的需要而设,也因人的发展而显得生机勃勃。博物馆的发展应该以展品为主到以观众为主,从"请勿触摸"到"欢迎参与"的转变。博物馆传播给观众的知识信息量越来越大,知识的传播不再是教育者向受教育者的单向传递,而是双向交流,互动影响。要在观众和博物馆之间建立一种联系,让观众参与到博物馆的展览中,从单方的接收信息到主动的去回收信息,创造一种新的服务方式,提供一种新的鉴赏体验,从而真正地满足观众的需求。     

浅谈卫浴产品造型设计与体验

本文以体验设计为出发点,从卫浴产品造型与用户体验论述了产品与生活形态的关联关系,指出在现代社会,设计师需要研究产品造型语言含义,创造人与卫浴产品之间一种更愉悦的使用体验。     

手机产品交互设计与用户体验探析

运用交互设计和用户体验的基本概念、产品交互系统组成图解、用户体验四元素,结合手机产品设计的用户调查和列表分析,获得了手机产品交互设计和用户体验的最终评价,通过这一比较和探析的过程证明,交互设计是未来设计尤其是工业产品设计的努力的方向和目标,用户体验是设计的终极参照。     

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.