体育旅游业中“户外重厨房项目”应用价值研究

"户外重厨房项目"是一种源于美国科拉多大峡谷大河漂流旅游的营地厨房服务模式,其特点是以物资为基础有效降低户外运动旅行的不舒适性,从而提高参与者评价度,促进旅游者消费。该项目需要在专业建设中囊括体育、旅游相关产业的旅游院校参加,提供一条"旅游+体育+餐饮+酒管"专业人才培养模式,培养复合型旅游人才,以有利于产业的全面、可持续发展以及体育旅游服务质量的提升。     

Understanding structure ignition vulnerabilities using mock‐up sections of attached wood fencing assemblies

Firebrand production from structure combustion becomes a key factor in the magnitude of how quickly a large outdoor fire may spread. Post‐fire disaster investigations suggest that attached building components, such as wood fencing assemblies are known to be prone to ignition in these fires, and may provide pathways to structure ignition. Here, a comparison of ignition results from full‐scale fencing assembly experiments conducted using a full‐scale wind tunnel facility, to mock‐ups of full‐scale fencing assemblies using the recently developed experimental capability at the National Research Institute of Fire and Disaster (NRIFD) are discussed. In both experimental facilities, the fencing assemblies were exposed to firebrand showers using custom built continuous feed firebrand generators with size and mass distributions similar to those generated from structure combustion. Similar ignition behaviors were observed between the full‐scale fencing assemblies and the mock‐up of full‐scale fencing assemblies. Additional experiments are required for other fencing assembly types to further verify these important findings.     

基于深度残差学习的自动驾驶道路场景理解

随着道路场景理解技术的快速发展，自主驾驶领域取得了长足的进步。在相关任务中，包括道路分割、分类和车辆检测的实时性和准确性是安全性的一个关键问题。为此，提出了一个具有编/解码器网络结构的基于深度残差学习的方法。一方面，编码器网络结构使用不同层次的残差网络来提取高维中的抽象特征，这些特征在接下来的三个任务中共享使用；另一方面，解码器网络结构采用一种子任务的并行计算机制，即道路分割、车辆检测和道路分类任务同时执行。此外，全卷积神经网络用于对提取的图像特征进行上采样以解决道路分割问题。最终，实验结果表明在保证高精度的前提下处理帧率可达到15 fps以上。     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

一种无线网络优化中挖掘LTE小区场景突变的方法研究

研究了一种检测无线网络优化中挖掘LTE小区场景突变的方法，通过筛选过滤出场景突变的LTE小区数据集，分析确认是否由于某些性能指标或者参数原因而导致的小区场景突变，最终挖掘得到发生场景突变的LTE小区。     

起重机械移动综合检测平台的研制开发

起重机械作为工业发展的基础性产业，其安全使用一直备受关注，有关的检测技术和手段日新月异。文中结合起重机械检验检测工作特点和流程，基于目前日趋成熟的移动实验室技术，提出了起重机械移动综合检测平台的设计方案。对现有检测手段、业务流程、办公软件、网络系统等进行整合，将检测平台结构分为车载基础和业务测试2个功能模块，实现了起重机械一站式的业务服务、检验检测、远程联检等功能，推进检验检测工作提质增效，并具有一定的扩展性，为智慧特检提供了一种思路。     

羊毛混纺户外运动服用针织面料开发与性能评价

为满足人们对春秋季户外休闲运动服装的需求,采用藏青羊毛纱、白色羊毛纱、涤纶丝以及锦纶丝为原料,在双面电脑提花机上开发4款羊毛针织面料,并选择2款规格相近的高密度面料作为对比样。对6种面料的耐磨性、顶破强力、抗起毛起球性、透气量、悬垂系数、克罗值、液态水分管理能力进行测试分析,采用模糊综合评价方法对面料的服用舒适性能进行评价,最后选出服用性能最好的羊毛针织面料,为羊毛户外休闲运动针织面料的开发提供参考。     

Real-time characterization of aerosol particle composition,sources and influences of increased ventilation and humidity in an office

Most of human exposure to atmospheric pollutants occurs indoors, and the components of outdoor aerosols may have been changed in the way before reaching indoor spaces. Here we conducted real-time online measurements of mass concentrations and chemical composition of black carbon and the non-refractory species in PM_2.5 in an occupied office for approximately one month. The open-close windows and controlled dampness experiments were also performed. Our results show that indoor aerosol species primarily originate from outdoors with indoor/outdoor ratio of these species typically less than unity except for certain organic aerosol (OA) factors. All aerosol species went through filtration upon transport indoors. Ammonium nitrate and fossil fuel OA underwent evaporation or particle-to-gas partitioning, while less oxidized secondary OA (SOA) underwent secondary formation and cooking OA might have indoor sources. With higher particulate matter (PM) mass concentration outdoors than in the office, elevated natural ventilation increased PM exposure indoors and this increased exposure was prolonged when outdoor PM was scavenged. We found that increasing humidity in the office led to higher indoor PM mass concentration particularly more oxidized SOA. Overall, our results highlight that indoor exposure of occupants is substantially different from outdoor in terms of mass concentrations and chemical species.     

Correlating indoor and outdoor temperature and humidity in a sample of buildings in tropical climates

The incidence of several respiratory viral infections has been shown to be related to climate. Because humans spend most of their time indoors, measures of indoor climate, rather than outdoor climate, may be better predictors of disease incidence and transmission. Therefore, understanding the relationship between indoor and outdoor climate will help illuminate their influence on the seasonality of diseases caused by respiratory viruses. Indoor-outdoor relationships between temperature and humidity have been documented in temperate regions, but little information is available for tropical regions, where seasonal patterns of respiratory viral diseases differ. We have examined indoor-outdoor correlations of temperature, relative humidity (RH), and absolute humidity (AH) over a 1-year period in each of seven tropical cities. Across all cities, the average monthly indoor temperature was 25 ± 3°C (mean ± standard deviation) with a range of 20–30°C. The average monthly indoor RH was 66 ± 9% with a range of 50–78%, and the average monthly indoor AH was 15 ± 3 g/m³ with a range of 10–23 g/m³. Indoor AH and RH were linearly correlated with outdoor AH when the air conditioning (AC) was off, suggesting that outdoor AH may be a good proxy of indoor humidity in the absence of AC. All indoor measurements were more strongly correlated with outdoor measurements as distance from the equator increased. Such correlations were weaker during the wet season, especially when AC was in operation. These correlations will provide insight for assessing the seasonality of respiratory viral infections using outdoor climate data, which is more widely available than indoor data, even though transmission of these diseases mainly occurs indoors.