Regulation of sensory nerve conduction velocity of human bodies responding to annual temperature variations in natural environments

The extensive research interests in environmental temperature can be linked to human productivity / performance as well as comfort and health; while the mechanisms of physiological indices responding to temperature variations remain incompletely understood. This study adopted a physiological sensory nerve conduction velocity (SCV) as a temperature‐sensitive biomarker to explore the thermoregulatory mechanisms of human responding to annual temperatures. The measurements of subjects’ SCV (over 600 samples) were conducted in a naturally ventilated environment over all four seasons. The results showed a positive correlation between SCV and annual temperatures and a Boltzmann model was adopted to depict the S‐shaped trend of SCV with operative temperatures from 5°C to 40°C. The SCV increased linearly with operative temperatures from 14.28°C to 20.5°C and responded sensitively for 10.19°C‐24.59°C, while tended to be stable beyond that. The subjects’ thermal sensations were linearly related to SCV, elaborating the relation between human physiological regulations and subjective thermal perception variations. The findings reveal the body SCV regulatory characteristics in different operative temperature intervals, thereby giving a deeper insight into human autonomic thermoregulation and benefiting for built environment designs, meantime minimizing the temperature‐invoked risks to human health and well‐being.     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

丙烷脱沥青装置调合前沥青的综合利用

在丙烷脱沥青装置加热炉试烧装置自产调合前沥青,通过观察油枪雾化沥青的能力、火嘴的燃烧情况以及燃烧后的烟尘状况,筛选出适合沥青燃烧的油枪,最终实现调合前沥青直接做加热炉燃料、减少重油出厂量的目的。     

Evaluation model of green supply chain cooperation credit based on BP neural network

Neural Computing and Applications - More and more enterprises hope to achieve cooperation and win–win. However, many companies often have problems such as insufficient partner credit, which...     

Predicting visual similarity between colour palettes

This work is concerned with the prediction of visual colour difference between pairs of palettes. In this study, the palettes contained five colours arranged in a horizontal row. A total of 95 pairs of palettes were rated for visual difference by 20 participants. The colour difference between the palettes was predicted using two algorithms, each based on one of six colour-difference formulae. The best performance (r² = 0.86 and STRESS = 16.9) was obtained using the minimum colour-difference algorithm (MICDM) using the CIEDE2000 equation with a lightness weighing of 2. There was some evidence that the order (or arrangement) of the colours in the palettes was a factor affecting the visual colour differences although the MICDM algorithm does not take order into account. Application of this algorithm is intended for digital design workflows where colour palettes are generated automatically using machine learning and for comparing palettes obtained from psychophysical studies to explore, for example, the effect of culture, age, or gender on colour associations.