大型强激光系统风险评价方法

对于大型强激光系统 ,要求进行安全性分析。给出了大型强激光系统风险评价的定性定量评估方法     

2016年冬季北京地区一次重污染天气过程边界层特征

利用中国气象局地面常规观测资料、微脉冲激光雷达（MINI-MPL）、风廓线雷达资料、生态环境部大气成分等资料,对2016年12月16~21日京津冀多地污染过程的生消特征、与气象条件的关系以及边界层的结构特点进行了分析.结果表明：大气处于静稳状态,低层大气盛行偏南气流,大气湿度持续增加,加之北京三面环山不利于污染物扩散的特殊地形是造成北京此次严重空气污染的重要因素.重污染期间,污染物主要聚集在800m高度以下,严重污染时,污染物高度甚至仅有400m左右.风廓线雷达反演风场显示：2次PM_2.5浓度快速上升阶段低层伴随持续偏南风或偏东风.污染过程期间,逆温结构明显,两次污染快速发展阶段恰好出现在两次逆温最强时段.此次污染天气过程,激光雷达退偏振比总体小于0.25,反映污染主要是人类活动产生气溶胶,前期以一次排放颗粒物为主,后期以二次转化颗粒物为主.退偏振比污染过程前期呈明显日变化特征,且白天退偏比比夜间高.     

全新的模具制造快速成形技术

快速成形技术 (RPM)是一门新兴的制造技术 ,给模具制造企业带来了巨大的经济效益 ,被称为制造领域的又一重大突破。对RPM技术原理、方法及特点进行了分析与探讨。     

激光雷达相干成像原理及其研究的若干问题

总结了激光雷达相干成像技术研究现状和进展,根据激光相干成像一般原理,针对2种最新激光雷达即距离高分辨多普勒和合成孔径相干成像技术,给出其系统技术框架和成像效果。分析了激光雷达相干成像特点及系统设计所必须解决的技术难点,指出解决问题的方案和注意问题,为我国进一步开展空间、空中和地基平台激光雷达相干成像试验研究和理论仿真奠定预先技术支持。     

国外武器装备激光隐身评价方法研究

世界军事发达国家在激光隐身技术的测试和评价手段中,大多数是利用三维激光雷达成像原理,对自然条件下大型复杂目标的激光散射特性进行了研究。根据激光散射特性的研究结果,对激光隐身效果、性能进行了有效评估。预测了激光隐身测试与评价技术的发展趋势。     

Correction model for CO detection in the coal combustion loss process in mines based on GWO-SVM

Accurate detection of CO gas is crucial to the prevention of coal combustion. Tuneable diode laser absorption spectroscopy (TDLAS) is a reliable method for CO detection during coal combustion. The influences of temperature and pressure cause changes in the line strength and linewidth of the index gases’ absorption spectra, leading to sizable measurement errors. To correct the distortion of the CO absorption spectrum caused by temperature and pressure fluctuation, a compensation model based on the grey wolf optimizer–support vector machine (GWO–SVM) was proposed. The results were compared with those of the single SVM, the back propagation neural network (BPNN), and multiple regression analysis (MRA). MRA was revealed to result in the lowest accuracy, which indicated that MRA is not ideal for compensation in TDLAS. The hyperparameter selection of the SVM had the disadvantages of randomness and blindness, which led to instability and large errors. The BPNN achieved better correction in the training stage, but severe overfitting occurred in the testing stage. The modified results revealed that the GWO–SVM model had higher accuracy and stability than the other models. It effectively inhibited the effects of temperature and pressure on the measured concentration and greatly improved the measurement accuracy. The equipment is thus suitable for CO gas detection with the aim to preventing coal combustion loss, and it can be further applied to loss prevention in other process fields.     

Factors affecting the adoption of multiple climate‐smart agricultural practices in the Indo‐Gangetic Plains of India

Climate change poses a major threat to agricultural production and food security in India, and climate‐smart agriculture (CSA) is crucial in addressing the potential impacts. Using survey data from 1,267 farm households in 25 villages from Bihar and Haryana in the Indo‐Gangetic Plains, this study analyzes the factors that determine the probability and level of adoption of multiple CSA practices, including seeds of stress‐tolerant varieties, minimum tillage, laser land leveling, site‐specific nutrient management and crop diversification. We applied a multivariate probit model for the simultaneous multiple adoption decisions, and ordered probit models for assessing the factors affecting the level of adoption. The adoption of the various CSA practices is interrelated, whereas several factors, including household characteristics, plot characteristics, market access and major climate risks are found to affect the probability and level of CSA adoption. Climate‐smart agriculture (CSA) adoption and its intensity also vary significantly between eastern Bihar, which is relatively poor and densely populated, and north‐western Haryana. Engaging multiple stakeholders such as farmers, agricultural institutions, agricultural service providers and concerned government departments at the local level is crucial for the large‐scale uptake of CSA. The study, therefore, calls for agricultural policy reforms so that most of the issues related to the uptake of CSA can be adequately addressed.     

A Cost‐Effective Laser Scanning Method for Mapping Stream Channel Geometry and Roughness

This brief pilot study implements a camera‐based laser scanning system that potentially offers a viable, cost‐effective alternative to traditional terrestrial laser scanning (TLS) and LiDAR equipment. We adapted a low‐cost laser ranging system (SICK LSM111) to acquire area scans of the channel and bed for a temporarily diverted stream. The 5 m × 2 m study area was scanned at a 4 mm point spacing which resulted in a point cloud density of 5,600 points/m². A local maxima search algorithm was applied to the point cloud and a grain size distribution of the stream bed was extracted. The 84th and 90th percentiles of this distribution, which are commonly used to characterize channel roughness, were 90 mm and 109 mm, respectively. Our example shows the system can resolve both large‐scale geometry (e.g., bed slope and channel width) and small‐scale roughness elements (e.g., grain sizes between about 30 and 255 mm) in an exposed stream channel thereby providing a resolution adequate for the estimation of ecohydraulic roughness parameters such as Manning's n. While more work is necessary to refine our specific field‐deployable system's design, these initial results are promising in particular for those working on a limited or fixed budget. This opens up a realm of laser scanning applications and monitoring strategies for water resources that may not have been possible previously due to cost limitations associated with traditional TLS systems.