Efficient Solution to Electromagnetic Scattering Problems of Bodies of Revolution by Compressive Sensing

Under the theory structure of compressive sensing （CS）, an underdetermined equation is deduced for describing the discrete solution of the electromagnetic integral equation of body of revolution （BOR）, which will result in a small-scale impedance matrix. In the new linear equation system, the small-scale impedance matrix can be regarded as the measurement matrix in CS, while the excited vector is the measurement of unknown currents. Instead of solving dense full rank matrix equations by the iterative method, with suitable sparse representation, for unknown currents on the surface of BOR, the entire current can be accurately obtained by reconstructed algorithms in CS for small-scale undetermined equations. Numerical results show that the proposed method can greatly improve the computgtional efficiency and can decrease memory consumed.

Aerial Extent Analysis and Environmental Problems Identification of Matasagar and Sukhsagar Wetlands in Bangladesh Using GIS and Remote Sensing Tools

Bangladesh is a land of wetlands. Basically, most of them are freshwater wetlands and have great influence on the primary economic activities such as agriculture and fisheries of the country. Due to its important role in the harmonizing ecosystem, wetlands demand much attention as a significant part of our environment. Matasagar and Sukhsagar are very important historical wetlands of Bangladesh. But those are endangered today due to lack of public awareness of the dangers of their activities to the environment and unbridled profit making activities of the commercial users of the wetlands. Comparisons of maps and Google images from 1933 to present have shown that the forest areas of the wetlands have been progressively destroyed, and these have greatly affected the biodiversity of these areas mentioned. GIS (Geographic Information System) and remote sensing techniques are used to identify the changes in the aerial extent of those wetlands. This study also tried to explore present environmental conditions by in-situ observation. This is high time;some serious steps should be taken to ensure the conservation and preservation of these areas mentioned.

铸牢中华民族共同体意识融入民族地区语言教育对策研究

民族地区语言教育发展是推动各族群众交往交流交融,促进民族团结的重要举措。民族地区只有不断加强国家通用语言文字教育才能更好地适应当前阶段的客观需要,加强民族间的交往交流交融,实现书同文、语同音、心相通、情相融。文章基于民族地区通用语言教育对于铸牢中华民族共同体意识的重要意义,通过梳理既有文献及研究成果,了解当前民族语言教育发展现状,就部分民族地区语言教育存在的突出问题,提出了加强语言教育师资队伍、加大语言教育投入力度、推广普及国家通用语言文字、增强课程思政在语言教育课堂的融合程度四条策略,并认为在民族地区推广普及国家通用语言文字,要开拓创新,多措并举,多管齐下,切实解决当前存在的问题,加快推进民族地区语言教育进程,以语言相通促进各族群众的沟通交流和国家认同,进而铸牢中华民族共同体意识。

基于问题探索的文献导读教学模式探析

依据认知失调理论和认知主义教学理论,提出基于问题探索为核心的文献导读教学模式。该教学模式以与课程教学目标紧密联系的问题意识培养为核心,以文献导读为手段,用“起惑——文献推荐——探索——解惑”的逻辑思路重构教学流程,实现高等教育课程教学完整性、互动性、高深性的教学要求。

中国民族思想史研究述论

民族思想是人类对历史和现实中民族和民族问题的基本认识,中国民族思想史就是在多民族中国国家发展历史过程中形成并且不断发展的民族思想及其实践过程,中国民族思想史最显著的特征是鲜明的时代性,是在解决民族问题实践过程中的智慧表达。在以铸牢中华民族共同体意识为主线的新时代,研究中国民族思想史对于中华民族共同体的建设和铸牢中华民族共同体意识的实践都具有重大的学术价值和实践意义。

基于Elastic-net正则化的神经网络方法求解反问题

神经网络已经成为求解反问题的热点方法之一,引入elastic-net正则项作为神经网络中损失函数的惩罚项防止求解过程的过度拟合,并通过交叉训练实现基于elastic-net正则项的神经网络的算法.通过压缩感知和图像去模糊2个数值实验,验证elastic-net正则项防止过度拟合的可行性和有效性.此外,当变换矩阵条件数较大时,在较低的训练轮次下可以达到较好的训练效果.

遥感监测技术在江西省重点河湖库“清四乱”中的应用研究

为及时排查与整改处置影响河湖库面貌及功能发挥的乱占、乱采、乱堆、乱建问题,探索利用遥感监测手段开展定期监测,充分发挥卫星遥感影像高时间分辨率和高空间分辨率的优势,为河湖库“四乱”问题高频度遥感监测提供数据保障。结合江西省重点河湖库遥感监测实施案例,通过构建遥感解译标志、生成“四乱”问题疑似图斑数据库、采用无人机辅助实地核实等技术手段开展遥感监测,对监测成果进行科学分析,为河湖管理及行政执法提供科学依据和决策支持。

卫星遥感在河湖岸线保护与利用监测中的应用

主要结合当前河湖长制与推进“四乱”问题治理常态化、规范化管理需求,通过套合河湖岸线规划数据,利用高分辨率卫星遥感影像数据,在制定河湖“乱占、乱堆、乱采和乱建”遥感解译分类标准基础上,以人机交互的方式提取疑似河湖“四乱”问题,并辅助外业核查,研制了一套利用卫星遥感技术开展河湖岸线保护与开发利用的技术路线与方法。研究结果表明:借助卫星遥感技术,可实现对河湖“四乱”问题的大范围、多频次、精准化监测,为河湖管护与河湖“四乱”问题治理提供一套完整、科学、可靠的解决方案。

以航天遥感为主题的问题驱动式研究生教学模式探索——以图像处理与成像技术课程为例

产教融合创新能力培养是我国新时代研究生教育的研究方向之一。该文结合航天遥感的行业特点,把研究生教育跟航天需求方案论证、问题驱动创新结合起来,探索新时代研究生更贴近产业需求的培养模式。从生活中常见的手机拍照入手,将专业遥感相机与“遥感手机”作对比,抛出问题并引导研究生拆解问题,查阅文献资料获取手机摄像头的主要成像参数,逐步分析计算手机作为遥感载荷对地成像的分辨力指标,引导学生对比专业遥感相机的成像指标参数,激发讨论热情,适当归纳结论,最终回归到图像处理与成像技术这门研究生课程的深层知识讲解。在提高课堂教学效率的同时,将目前航天遥感领域的产业状况与当下研究生教学紧密结合在一起,探索产教融合的研究生培养新模式。

“双碳”目标下广西居民环保素质对绿色消费的影响研究

本文基于广西7个重点城市的实地调研数据,结合环保素质理论和计划行为理论,从居民的环保素质角度出发,通过环保价值观、环保责任感和环保问题感知三个变量与绿色消费建立影响机制模型,运用结构方程模型进行数据分析。研究结果表明,环保价值观、环保责任感、环保问题感知分别对绿色消费具有正向的显著性影响作用,其中环保价值观对于绿色消费的影响相较其他变量更大。基于此,为进一步促进广西绿色消费发展,政府应积极引导居民强化绿色消费动因,实现绿色消费常规化;推动企业完善绿色消费产业链,提升绿色发展水平;加快绿色消费体系建设,助力绿色转型发展。政府、企业、居民三者应各尽其责,群策群力,推动广西居民形成绿色低碳的生产方式和生活方式。

Review of the Impact of Grassland Degradation on Ecosystem Service Value

In recent years, grassland degradation has become one of China’s most critical environmental problems due to the interaction of natural environmental factors and human causes. Based on the systematic analysis of the spatial characteristics of grassland degradation and the current research status of environmental drivers, this paper summarizes and summarizes the research methods on the impact of grassland degradation on natural ecological service function and social and economic value to understand further the natural ecological service function of grassland degradation and its impact on social and economic benefits. The results show that since the function of grassland ecosystem service is much larger than the biomass value it provides, we should focus on the effective management of grassland from the design concept of ecological service function to achieve the sustainable development of grassland. We should do an excellent job in the comprehensive application of various ecosystems and service value evaluation methods in the future.

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.

New Approach for Limited-Angle Problems in Electron Microscope Based on Compressed Sensing

New advances within the recently rediscovered field of Compressed Sensing (CS) have opened for a great variety of new possibilities in the field of image reconstruction and more specifically in medical image reconstruction. In this work, a new approach using a CS-based algorithm is proposed and used in order to solve limited-angle problems (LAPs), like the ones that typically occur in computed tomography or electron microscope. This approach is based on a variant of the Robbins-Monro stochastic approximation procedure, developed by Egaziarian, using regularization by a spatially adaptive filter. This proposal consists on filling the gaps of missing or unobserved data with random noise and enabling a spatially adaptive denoising filter to regularize the data and reveal the underlying topology. This method was tested on different 3D transmission electron microscope datasets that presented different missing data artifacts (e.g, wedge or cone shape). The test results show a great potential for solving LAPs using the proposed technique.

基于多翼型特征的非奇异变形感知方法研究

针对传统变形感知方法在复杂翼型结构中常见的病态、奇异等问题,提出了一种基于多翼型特征的非奇异变形重构模型。依据Timoshenko梁变形理论,采用依存插值技术离散单元位移场,建立理论截面应变与测量应变的最小二乘变分函数,推导单元节点变形与测量应变的积分重构模型。该模型的位置无关性有效消除评估截面选取不当引起的奇异,增强重构模型在复杂翼型结构中的适用性。同时,针对应变传感器服役期间常见的环境扰动,以重构精度与鲁棒性为评估指标,建立自适应多目标粒子群优化模型。实验结果表明,提出的重构模型整体测量精度较高,在机翼变形量小于20 mm范围内最大绝对误差为0.26 mm,最大相对均方根误差为0.42%;当变形量增大时,绝对误差随之增大,但相对均方根误差不超过3.5%。因此基于多翼型特征的非奇异变形重构模型能够满足机翼实时重构需求,有效扩展变形感知方法在复杂结构中的应用价值。

铸牢中华民族共同体意识的问题导向及其应对

铸牢中华民族共同体意识是新时代民族工作运用机遇和应对挑战的必要,也是历经自在、自觉、自为发展历程的中华民族的时代呼唤,又是我国“大一统”历史信仰的时代表达。作为一项重要系统工程,铸牢中华民族共同体意识需要树立问题意识,以问题为导向,廓清是什么、为什么、怎么样等基础性问题,为实践工作的深入推进提供学理支撑。

Mask R-CNN和无人机遥感在黄河“四乱”问题调查中的应用

黄河“四乱”问题排查和整治是保障黄河流域高质量发展的重要环节。为了提高“四乱”问题调查工作的效率和准确性,本文采用一种基于Mask R-CNN和无人机遥感的调查方法,可以有效区分“四乱”目标及其干扰物。首先采用无人机航摄系统获取目标区域的正射影像;然后构建属于“乱建”问题的养殖大棚样本库;最后采用Mask R-CNN模型进行对象检测。试验结果表明,采用方法对养殖大棚目标检测的准确率可以达0.93,且能大幅提高调查效率。

“德法兼修”视域下中国法律史课程思政建设路径探析

中国法律史课程思政是新时代实现“德法兼修”法治人才培养目标的重要举措和必要路径。当前中国法律史课程思政建设主要存在专业教师思想认识不到位、思政理论知识储备不足、思政元素挖掘不够深入、课程思政教学改革与创新不足等问题,与课程思政“三全育人”教育理念存在一定差距。因此,在探索推进中国法律史课程思政建设中,专业教师应将课程思政教育理念内化于心,加强思想政治理论学习,充分挖掘思政元素,深化教学改革与创新等,多维路径培养“德法兼修”的卓越法治人才。

城市社会感知研究与应用进展

随着大数据时代的到来,社会感知研究逐渐成为热门议题。社会感知是指社会公众对自然与社会的实体、过程和现象的感知和响应。文章阐述了国内外社会感知的研究现状,社会感知的主要数据类型,分析了场所环境感知、动态演化感知、社会关系感知3类主要研究内容维度,以及在城市研究中的应用,包括子解析城市特征、预测城市规律、治理城市问题。以期梳理社会感知的研究进展,为更为深入的研究提供参考,实现以人为本的城市规划建设与科学治理决策。