引航员登离船安全防护装置研究

引航员登离软梯过程中,使用手动方式上下移动安全带实现引航员安全防护较为繁琐,且会出现二次伤害风险。为此,提出一种新型登离船安全防护装置,包括姿态监控装置和攀爬防护装置。姿态监控装置通过姿态传感器MPU(Multi-chipUnit)-9250获取人体姿态信息,并实现人体步数的判断;攀爬防护装置以直流电机作为装置驱动源,配合爬绳同步轮组,设计攀爬防护装置机械结构、锁紧结构和电控装置,实现了引航员登离船过程中装置跟随人体的自动跟随功能。实验结果表明:该装置相对于传统手动攀爬方式具有更好的智能性、便携性、安全性。     

Generation of Time History Compatible with Multi-Damping Response Spectra

Based on the method developed by M. Hirasawa and M. Watabe (1992), the authors deduced the related mathematical formulas. Taking an artificial ground motion compatible with single-damping as the original time history, a small corrected time history was calculated by solving linear equations using a singular value decomposition method. We performed iteration to obtain the simulated earthquake motion compatible with multi-damping within a certain accuracy. The influences of selective matching frequencies are discussed preliminarily. Although the current criterion still has no explicit demand on different numbers of matching frequencies for various damping factors, it should be taken into account in practice.     

额济纳旗人工绿洲生态建设试验研究

根据额济纳旗弱水下游近河戈壁区的立地条件和气候特点进行合理布局,利用春季河水浇灌补充地下水位,采取开沟积沙保墒,选用乡土树种,乔灌结合,封造结合等严格的管理技术措施,１５ａ造林１３００ｈｍ２以上,年经济效益１４０多万元。通过边营造边观测取得数据,从防风固沙,改善土壤结构,增加植被覆盖度、生物产量以及社会效益等方面进行分析研究。结果表明,近河戈壁是可以逆转为绿洲的,这为综合整治居延绿洲环境提供了科学依据。     

长沙电力学院3号教学楼人工挖孔灌注桩工程地质问题及其处理措施

长沙电力学院 3号教学楼人工挖孔桩基的施工条件极差。经灵活采用多种经济实用的措施处理 ,成功地解决了流砂、流泥、管涌、坍塌、护壁吊烂与整体失稳等技术难题。     

地震活动增强方式及其复杂性

分析了地震活动增强特征参数W1与相应其它参数的关系以及在一些6级左右地震前W1值的动态图像变化。结果表明中强以上地震前的地震活动“增强”通常表现为在强度上的增高、时间和空间上的丛集以及强度增高与时空丛集同时出现3种方式。一些6级左右地震前W1值动态图像变化表明地震过程往往表现为多应力集中区相互作用和影响的演变过程。     

桶形基础平台安装就位智能化测控装置

近年来,国内外海上平台开始采用桶形基础(几米浅基础)代替一直沿用的桩基础(几十米深基础),其安装就位采用与打桩施工完全不同的负压沉贯的施工方法.介绍了一种用于桶形基础平台在负压沉贯施工中的智能化测控装置.该装置成功地用于我国首座桶形基础平台(胜利油田CB20B平台)的安装就位施工,取得了预期的效果.为我国今后桶形基础平台负压沉贯施工中的智能化测控提供了有益的借鉴.     

高光谱影像特征选择的快速克隆选择算法(英文)

Clonal selection feature selection algorithm (CSFS) based on clonal selection algorithm (CSA), a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm (FCSFS) for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instea...     

Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks

The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods.     

An ANN algorithm for automatic, real-time tsunami detection in deep-sea level measurements

The present paper looks at algorithms to be implemented in the software of bottom pressure recorders (BPRs) for the automatic, real-time detection of a tsunami within recorded signals. The structure of an algorithm based on the use of an artificial neural network (ANN) is presented and compared to the one developed under the Deep-ocean Assessment and Reporting of Tsunamis (DART) program run by the U.S. National Oceanic and Atmospheric Administration (NOAA). The performance and efficiency of the two algorithms are compared using both synthetic and actually measured time series. Results show that an improvement in detection performance can be obtained by using the ANN algorithm.     

A virtual laboratory for stability tests of rubble-mound breakwaters

The prediction of rubble-mound breakwater damage under wave action has usually relied on costly and time-consuming physical model tests. In this work, artificial neural networks (ANNs) are applied to estimate the outcome of a physical model throughout an experimental campaign comprising of 127 stability tests. In order to choose the network best suited to the problem data, five different activation function options and 38 network architectures are compared. The good agreement found between the physical model and the neural network shows that an ANN may well serve as a virtual laboratory, reducing the number of physical model tests necessary for a project.