Tactile colour pictogram to improve artwork appreciation of people with visual impairments

A recent development in tactile technology enables an improvement in the appreciation of the visual arts for people with visual impairment (PVI). The tactile sense, in conjunction with, or a possibly as an alternative to, the auditory sense, would allow PVIs to approach artwork in a more self‐driven and engaging way that would be difficult to achieve with just an auditory stimulus. Tactile colour pictograms (TCPs), which are raised geometric patterns, are ideographic characters that are designed to enable PVIs to identify colours and interpret information by touch. In this article, three TCPs are introduced to code colours in the Munsell colour system. Each colour pattern consists of a basic cell size of 10 mm × 10 mm to represent the patterns consistently in terms of regular shape. Each TCP consists of basic geometric patterns that are combined to create primary, secondary, and tertiary colour pictograms of shapes indicating colour hue, intensity and lightness. Each TCP represents 29 colours including six hues; they were then further expanded to represent 53 colours. Two of them did not increase the cell size, the other increased the cell size 1.5 times for some colours, such as yellow‐orange, yellow, blue, and blue‐purple. Our proposed TCPs use a slightly larger cell size compared to most tactile patterns currently used to indicate colour, but code for more colours. With user experience and identification tests, conducted with 23 visually impaired adults, the effectiveness of the TCPs suggests that they were helpful for the participants.     

Neuroscience and architecture: What does the brain tell to an emotional experience of architecture via a functional MR study?

Environment psychologically affects individuals. According to the base of cognitive psychology, there is a direct relationship between human behavior, environment, and emotional process. Assuming that pleasantness and unpleasantness are associated with peripheral nervous system activation, the current study aims to explore if the pleasant or unpleasant architectural places can stimulate the brain regions engaged in emotions or not. As the main contribution, we used functional magnetic resonance imaging (fMRI) measuring blood oxygenation level-dependent (BOLD) changes to effectively detect the brain's region that mainly responds to the emotional-perceptual processes. Based on the results of examining the emotional assessment model of “Pleasure-Arousal” applied to 140 students, 30 most-rated images representing 15 pleasant and 15 unpleasant places were shown to 32 participants in a 1.5-T MRI scanner. After applying standard preprocessing steps (re-alignment, slice-timing, co-registration, segmentation, normalization, and smoothing) to functional MR images, first-level analysis was applied to each subject. The results were evaluated using statistical corrections at different levels for female and male participants with the second-level analysis. In conclusion, it has been shown that there is a significant linkage between environmental experience and brain activation so that the architectural qualities can change blood flow in specific brain regions.     

一次野外重大活动饮食卫生保障的经验与借鉴

总结一次野外重大活动饮食卫生保障的针对性做法和经验，为以后遂行多样化任务提供参考。从食谱审查、从业人员卫生管理及培训、采购保管、加工制作、餐饮具消毒以及留样备检等环节严格把关，强化常态食品卫生监督；科学分析餐饮环节容易导致疫情传播的风险点，从人、物、环境和预案4个方面开展督导；针对野外食品加工及人员大规模长时间野外就餐，指导餐饮单元选址开设、做好环境卫生管理、保障加工用水及食材安全、保障运输前送环节的食品安全及人员就餐过程卫生、指导智能化餐厨设备使用。提高餐饮单位负责人及从业人员的食品安全意识；提高保障人员的卫生法律素质和专业技术能力，关注新的风险点。加强保障人员自身管理及从业人员的卫生管理，树立良好形象。     

基于用户体验的压路机操纵手柄交互品质研究

目的以压路机操纵手柄为对象,探讨交互品质研究方法,改进用户体验。方法对交互品质进行解析,由用户体验与交互品质的关联性,提出基于用户体验的交互品质研究方法;面向压路机操纵手柄典型任务进行用户体验研究,梳理压路机手柄的操作流程,并对交互行为进行聚类分析,结合语义分析构建用户体验词库,运用半结构式访谈与问卷调查,以定量与定性方法相结合对驾驶员的操纵体验进行调研;以专家研讨的形式进行驾驶员的操纵体验向交互特征的映射,构建交互特征集,进而对操纵手柄的交互品质进行分析与阐释。结果得出了压路机操纵手柄的交互特征与交互品质。结论通过用户体验可以映射交互特征,得到了交互品质,对于从交互品质的角度指导交互设计提供了借鉴。     

基于QoE感知的星地一体化网络公平资源分配策略

星地一体化网络是卫星网络和地面网络互联，实现卫星网络和地面网络优势互补的一体化网络。随着无线通信的快速发展，卫星网络和地面网络的同频复用技术可有效提升资源利用效率，满足日益增长的无线数据业务需求。但由于无线数据业务类型的多样化和频谱资源的稀缺性，如何实现用户数据服务的服务体验质量（Quality of experience， QoE）和公平性提升也成为星地一体化网络亟待解决的关键问题。本文综合考虑信道时变性、服务体验多样性和服务公平性，提出了一种基于QoE感知的星地一体化网络资源分配策略。为此，将目标问题描述为基于时间平均的服务体验质量的网络公平效用最大化，并利用李雅普诺夫优化理论，将复杂的动态原始优化问题进一步转化并分解为3个瞬时优化子问题，每个子问题可以在每个时隙独立求解。同时，由于无线资源分配为典型的非凹混合组合优化问题，推导了一个低复杂度的两步算法来求解第三个资源分配子问题。仿真结果揭示了在不同的公平性参数下用户的长时QoE与公平性之间的折衷情况。     

恒湿文物展柜用吸附式空气取水方法

博物馆恒湿文物展柜的湿度控制至关重要，为实现展柜的自动恒湿功能，本文基于吸附式空气取水原理，提出了一种将环境水汽自动补充至恒湿文物展柜系统水箱的方法，并搭建了相应的小型空气取水装置，证明了该方法的可行性；同时，构建了传热传质耦合的空气取水装置数学模型，在获取环境温湿度对装置取水量的影响规律后，分析加热功率和吸附床结构对装置单位能耗取水量的影响；此外，为便于装置的后期推广应用，提出了两个基于吸附床高度和直径的量纲为1参数。结果表明：进口空气的温度对取水量影响较小，但进口空气的湿度对取水量影响较明显，取水量随着湿度增加而增加；加热功率存在最优值，单位能耗取水量随着加热功率先增大后减小；在当前几何参数组合下，两个量纲为1参数η=2、β=4时，装置的单位能耗取水量最高。     

体验经济背景下家纺设计专业现状调查--以通州中专5年制家纺设计专业为例

体验经济时代,人们的消费理念和消费层次不断提高,企业的经营模式和用人机制也发生着改变。在现实生活中,很多商家打造出以环境、情绪、感受为核心体验的家居体验馆。为适应时代的发展,根据人才培养目标,在调查研究的基础上,针对5年制高职家纺设计专业进行课程改革,逐步打破传统的教学模式,进一步探索产教融合,为社会培养更多优秀的综合型专业人才。     

A comparison of visual ability and its importance awareness between novice and experienced drivers

The visual ability factors of peripheral perception, visual reaction and visual pursuit ability are important for safe driving. Thus, this study explored the influence of driving experience on these visual ability factors. Sixty-five drivers, including novice and experienced drivers, participated in this study. The visual ability levels of the three factors were measured by the Vienna Test System. In addition, driver's awareness of the three factors' importance for safe driving was measured by questionnaire items. Results showed that out of the three factors, drivers' peripheral perception ability was correlated significantly with their driving experience. Experienced drivers had higher peripheral perception ability than that of novice drivers. In addition, compared with novice drivers, experienced drivers believed that peripheral perception was more important whereas visual reaction was less important. It is suggested that higher attention should be paid to peripheral perception in driving training and novice drivers should increase their importance awareness for this factor.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Quantifying co-creative writing experiences

Collaboration with artificial intelligence (AI) is a growing trend even in the field of creativity. This paper examines which quantitative metrics can be used to comparatively analyse human-computer co-creativity with children. To study this question, 24 schoolchildren of age 10–11 wrote a poem with three co-creative poetry writing processes: a human-computer, a human-human, and a human-human-computer process. The computational participant in the processes was an AI-based application called the Poetry Machine. The children were asked to evaluate their user experience with a 5-point Likert-type questionnaire after each writing process and a comparative questionnaire after finishing all processes. The metrics used in the evaluation were immediate fun, long-term enjoyment, creativity, self-expression, outcome satisfaction, ease of starting and finishing writing, quality of ideas and support from others, and ownership.

Significant differences were found in fun, long-term enjoyment, quality of ideas, support, and ownership. The high number of statistically relevant results was enabled by exposing all participants to all writing processes, and the comparative questionnaire. The human-human-computer process was evaluated the best in long-term enjoyment and the human-computer process the weakest in support and idea quality. Creativity and ease of finishing writing turned out to be outlining metrics for the co-creative processes.