An Artificial Motion and Tactile Receptor Constructed by Hyperelastic Double Physically Cross-Linked Silk Fibroin Ionoelastomer

Ionotronic artificial motion and tactile receptor (i-AMTR) is essential to realize an interactive human-machine interface. However, an i-AMTR that effectively mimics the composition, structure, mechanics, and multi-functionality of human skin, called humanoid i-AMTR, is yet to be developed. To bridge this technological gap, this study proposes a strategy that combines molecular structure design and function integration to construct a humanoid i-AMTR. Herein, a silk fibroin ionoelastomer (SFIE) with double cross-linked molecular structure is designed to mimic the composition and structure of human skin, thereby resolving the conflict of stretchability, softness, and resilience, suffered by many previously reported ionotronics. Functionally, electromechanical sensing and triboelectricity-based tactile perception are integrated into SFIE, to enable simultaneous perception of both motion and tactile inputs. By further leveraging the machine learning and Internet of Things (IoT) techniques, the proposed SFIE-based humanoid i-AMTR precisely senses the movement of human body and accurately sortball objects made of different materials. Notably, the success rate for 610 sorting tests reaches as high as 92.3%. These promising results essentially demonstrate a massive potential of humanoid i-AMTR in the fields of sorting robots, rehabilitation medicine, and augmented reality.     

Dual geometric perception for cross-domain road segmentation

Road segmentation plays an important role in navigation systems and autonomous driving. However, many methods in road segmentation are based on supervised learning and suffer from performance degradation in the real world. There is a certain domain gap (distribution shift problem) between the source domain (training data) and the target domain (testing data). In this paper, we propose a Dual-Geometric Perception (DGP) approach for cross-domain road segmentation, which jointly uses semantic and dual-geometric information to learn the domain-invariant feature for road segmentation. First, we propose an RGB-N dual stream network structure, which effectively fuses normal vector information and RGB information to reduce domain gap. Moreover, a dual geometric adversarial learning strategy is proposed to utilize depth-aware and normal vector features to perform better domain alignment. Furthermore, a self-training learning strategy is used to further improve the model’s generalizability in the target domain. Extensive experiments demonstrate that our proposed DGP achieves superior performance on lane-to-lane and lane-to-sidewalk road domain adaptation tasks.     

以图像为主的多模态感知与多源融合技术发展及应用综述

不同模态的表现方式不同,描述目标的角度也会不同。多模态感知与多源融合技术是将两种或两种以上的模态组合起来,融合不同传感器、不同平台收集到的数据、特征信息,兼顾不同图像的优势,在遥感监测、军事安防、自动驾驶等领域有着广泛的运用。介绍了热成像、高光谱成像、偏振成像、合成孔径雷达(SAR)、激光雷达(LiDAR)等多模态感知技术,总结了不同成像方式的特点与联系,简述了多源融合相关概念及其技术发展历程,重点分析了不同模态下图像融合案例,在此基础上归纳多模态感知和多源融合技术的发展趋势,最后基于融合算法、系统整体性、评价指标总结出进一步发展方向。     

基于边权表示学习的乌克兰互联网变化感知

2022年2月至7月期间,网络攻防频繁发生,乌克兰部分地区间接受到断网影响,其境内和边界网络连通情况发生了改变,通过对时间变化拐点的感知和变化区域的分析可敏锐捕捉网络异常变化的细节,为网络威胁预警、网络关键基础设施风险评估等提供重要的信息支持。本文针对2022年2月至7月期间乌克兰自治系统的路由变化进行研究,构建乌克兰自治系统动态网络拓扑,介绍动态网络分析方法,监测乌克兰网络变化,分析断网的时间、地点,并感知断网后网络发生异常的地区。通过与现实事件的对比分析,映射出互联网结构变化感知结果与实体军事冲突的相关性,为从互联网空间观测复杂战场态势提供了信息支撑。     

融合深度学习与成像模型的水下图像增强算法

水下机器人的视觉感知功能因受到水下环境因素的影响,面临着图像质量降低的挑战,如图像颜色畸变、整体色调偏绿、偏蓝、对比度较低、细节较为模糊等。提出一种结合深度学习方法与物理成像模型的新型水下图像增强算法,通过构建包含扩张卷积和带参数激活函数的神经网络,进行背景散射光和直接传输映射的估计,并结合成像模型的数学表达进行重建运算得到增强后图像。实验结果表明,与UDCP、IBLA、GLNet等典型图像增强算法相比,该算法具有更快的运算速度,且能够消除水下环境因素带来的影响,丰富图像色彩的同时能增强各类细节,在峰值信噪比指标和结构相似度指标上取得了较大值。此外,增强后的图像在特征点匹配实验中获得了更好的匹配效果。     

Critical factors and paths influencing construction workers’ safety risk tolerances

While workers’ safety risk tolerances have been regarded as a main reason for their unsafe behaviors, little is known about why different people have different risk tolerances even when confronting the same situation. The aim of this research is to identify the critical factors and paths that influence workers’ safety risk tolerance and to explore how they contribute to accident causal model from a system thinking perceptive. A number of methods were carried out to analyze the data collected through interviews and questionnaire surveys. In the first and second steps of the research, factor identification, factor ranking and factor analysis were carried out, and the results show that workers’ safety risk tolerance can be influenced by four groups of factors, namely: (1) personal subjective perception; (2) work knowledge and experiences; (3) work characteristics; and (4) safety management. In the third step of the research, hypothetical influencing path model was developed and tested by using structural equation modeling (SEM). It is found that the effects of external factors (safety management and work characteristics) on risk tolerance are larger than that of internal factors (personal subjective perception and work knowledge & experiences). Specifically, safety management contributes the most to workers’ safety risk tolerance through its direct effect and indirect effect; while personal subjective perception comes the second and can act as an intermedia for work characteristics. This research provides an in-depth insight of workers’ unsafe behaviors by depicting the contributing factors as shown in the accident causal model developed in this research.     

Optimal markdown policy of perishable food under the consumer price fairness perception

Markdown policies are widely used by retailers to sell perishable food. Consumers purchase food at different prices during different sales periods. Some consumers may compare their experience with others who purchase the same items. Price unfairness or inequity is perceived when different prices are quoted without reasonable explanations. This study develops an optimal markdown model for perishable food pricing to optimise the food retailer revenue and enable a maximum aggregated consumer utility considering individuals’ price fairness perception. The model serves as the first step in evaluating trade-offs between food retailer revenue and consumer utility. In addition, it enables consumer utility to be depicted through perceived price fairness by including the effects of food perishability and scarcity. Another innovative feature is the equalisation of the consumer average aggregated utility during different sales periods as a condition of intertemporal price fairness perception. The proposed model is compared with two benchmark models to justify the effectiveness and advantages in the example. Finally, a sensitive analysis based on the food deterioration rate, consumer food desire rate and consumer average reservation price is conducted to justify the manner in which these factors influence the optimal pricing policy, and managerial insights are suggested for food retailers.     

标准有关信息对公众产品质量认知影响研究

标准支撑经济社会健康发展的作用显著提高了公众对标准问题的关注和标准与产品质量相关性的探索意愿.但受标准技术性和专业性的影响,其对一般公众而言知识的可及性相对较差,因此可能导致标准有关信息的引入对公众的产品认知与其他外部信息的影响有不同的发展规律.本文以标准作为兼具技术性和公众相关性话题的代表,通过实验分析的方法,观察不同信息叠加对公众的产品态度的影响,讨论了标准对产品质量认知的影响方式.     

跨界融合视野下的空间认知与视觉意象

目的解析空间认知对视觉意象的影响。方法通过实体论、关系论、属性论的特征与视觉意象的映射关系来阐述空间认知与艺术设计的关联。结论实体论强调了物体本身的空间虚实变化。关系论强调了物与物之间的空间关系协调,形成更加丰富的内涵。属性论强调了物与人共同影响下的空间个性表现,带来个性化的体验。分析空间的虚实、关系及个性对于视觉意象的影响,可以为设计艺术创作提供参考。     

产品细节形态设计探究

目的探究产品细节形态设计中的设计原则和方法。方法结合现有产品的调研汇总、基本模型的建立分析以及问卷调查的结果,对影响产品外观细节形态的两个基本要素——圆角和分割线的属性对产品视觉美感的影响进行研究,并给出相关实例验证。结论分析了产品细节形态中圆角和分割线两个视觉元素对产品视觉感受的影响,提出了在产品细节形态设计中合理设计圆角和分割线的原则和方法,给出了从圆角和分割线两个方面提升整体产品视觉美感的优化方案。