Isolation of protein from Chlorella sorokiniana CY1 using liquid biphasic flotation assisted with sonication through sugaring-out effect

Microalgae,a sustainable source of multi beneficial components has been discovered and could be utilised in pharmaceutical,bioenergy and food applications.This study aims to investigate the sugaring-out effect on the recovery of protein from wet green microalga,Chlorella sorokiniana CY1 which was assisted with sonication.A comparison of monosaccharides and disaccharides as one of the phaseforming constituents shows that the monosaccharides,glucose was the most suitable sugar in forming the phases with acetonitrile to enhance the production of protein(52% of protein).The protein productivity of microalgae was found to be significantly influenced by the volume ratio of both phases,as the yield of protein increased to 77%.The interval time between the sonication as well as the sonication modes were influencing the protein productivity as well.The optimum protein productivity was obtained with 10s of resting time in between sonication.Pulse mode of sonication was suitable to break down the cell wall of microalgae compared to continuous mode as a lower protein yield was obtained with the application of continuous mode.The optimum condition for protein extraction were found as followed:200g/L glucose as bottom phase with volume ratio of 1:1.25,10s of resting time for ultrasonication,5s of ultrasonication in pulse mode and 0.25g of biomass weight.The high yield of protein about 81% could be obtained from microalgae which demonstrates the potential of this source and expected to play an important role in the future.     

大面积水深异常检测的条件变化自编码算法

针对大面积海底地形数据缺失或异常的复杂及多变性特点,结合条件变分自编码器(CVAE)与深度卷积生成对抗网络(DCGAN),构建了条件变分自编码生成对抗网络(CVAE-GAN)大面积海底伪地形的检测与剔除方法。本文方法利用条件变分自编码算法改变原有的样本分布,通过对训练样本的学习重新构建样本之间的分布规律,有效提高了高维到低维映射的稳定性;结合生成对抗网络,提高了整体算法的稳健性,最终得到较优的检测与剔除结果。采用水深格网数据进行试验,并与中值滤波法、趋势面滤波法进行比较。结果表明,本文方法在精度、稳定性及噪声稳健性方面有所提高,验证了本文方法在海底地形数据处理上具有可行性。     

结合深度学习和图割法的遥感影像建筑物检测

提出一种基于卷积神经网络和图割法的自动提取高分影像建筑物的方法。首先,通过卷积神经网络定位与检测建筑物的位置,逐一提取单个建筑物轮廓,利用检测结果分别建立建筑物和非建筑物的高斯混合模型（GMM）,然后结合最大流最小割的图像分割方式实现全局优化,完成建筑物初步提取,最后用形态学进行优化。通过试验证明了该方法的可行性。     

A literature synthesis of LiDAR applications in transportation: feature extraction and geometric assessments of highways

Interest in using Light Detection and Ranging (LiDAR) technology in Transportation Engineering has grown over the past decade. The high accuracy of LiDAR datasets and the efficiency by which they can be collected has led many transportation agencies to consider mobile LiDAR as an alternative to conventional tools when surveying roadway infrastructure. Nonetheless, extracting semantic information from LiDAR datasets can be extremely challenging. Although extracting roadway features from LiDAR has been considered in previous research, the extraction of some features has received more attention than others. In fact, for some roadway design elements, attempts to extract those elements from LiDAR have been extremely scarce. To document the research that has been done in this area, this paper conducts a thorough review of existing studies while also highlighting areas where more research is required. Unlike previous research, this paper includes a thorough review of the previous attempts at data extraction from LiDAR while summarizing the detailed steps of the extraction procedure proposed in each study. Moreover, the paper also identifies common tools and techniques used to extract information from LiDAR for transportation applications. The paper also highlights common limitations in existing algorithms that could be improved in future research. This paper represents a valuable resource for researchers and practitioners interested in knowing the current state of research on the applications of LiDAR in the field of Transportation Engineering while also understanding the opportunities and challenges that lie ahead.     

River centerline extraction using the multiple direction integration algorithm for mixed and pure water pixels

The river centerline is a basic hydrological characteristic. Most prior studies have used remote sensing data to extract the river centerline from the open water region in a pure water pixel region. Extracting this type of river is relatively easy. However, extracting the centerline of a micro-river, which is mainly composed of mixed water pixels, is challenging. This paper presents a novel method, called the Multiple Direction Integration Algorithm (MDIA), to extract the river centerline using an image-enhancing method combined with river morphology. MDIA can be applied to regions mainly composed of pure water pixels, as well as to regions consisting of mixed water pixels in the index image. The method first calculates the normalized difference vegetation index (NDVI) and enhances the river linear structure using a Hessian matrix. Second, a small window is constructed as a circular structural element. In the window region, the local threshold is automatically obtained using water-oriented clustering segmentation and prior river knowledge to judge the pixel type. After completing the river centerline extraction in the current window, the next detecting window is generated to continue judgment. The structural element automatically executes river centerline judgment until the entire river centerline is extracted. The Landsat 8 images of six regions with different geomorphologies were chosen to analyze the method’s performance. The test sites include high mountain region, low mountain region, plains region with farmland and a residential region. The experimental results show that the optimal threshold of the processing results ranged from 0.2 to 0.3. In this range, the user’s accuracy is 0.813 to 0.997, and the producer’s accuracy is 0.981 to 1. The MDIA effectively and correctly extracts the river network in mixed-pixel regions. The presented method provides an effective algorithm for river centerline extraction that can be used to expand and update river datasets and provide reliable river centerline data for relevant hydrology studies.     

基于DEM数据的月表撞击坑自动提取研究

提出一种基于地形与形态学的撞击坑自动提取算法,该算法主要利用撞击坑的形态特征进行提取,利用形态学方法提高提取结果的连续性并去除提取结果中的噪声,使提取的撞击坑边界线更为简单,定位更加准确,易于存储与计算。该算法使用月球勘测轨道器照相机(LROC)提供的100 m分辨率DEM数据进行测试,测试结果显示该算法对于形态较为完整的简单撞击坑提取效果较好,提取的边界线较为准确且能够反映撞击坑的真实形态。     

基于面片裁切技术的点云轮廓线生成算法

随着点云在各个领域的广泛应用,对点云处理相关技术的研究更为活跃。目前,研究的热点主要集中在点云去噪、配准、分割、增强、特征提取和重建等方面。其中,特征提取起着承前启后的作用,具有十分重要的研究价值。基于面片裁切技术的点云轮廓线生成算法。首先,对点云数据进行面片提取,通过面片生长、融合生成特征面片,并根据提出的面片双向索引法快速构造特征线,最终实现点云平面轮廓线的生成。实验证明,本算法可快速、准确、有效地实现点云轮廓线的生成。     

异源多分辨率卫星影像匹配算法研究

以异源多分辨率遥感卫星影像为研究对象,对影像特征提取、特征描述、特征匹配及匹配提纯算法进行了较为深入的研究;并将不同的特征提取与匹配算法进行组合,通过试验对比得出了各种算法的适用性。在此基础上,设计了一套面向异源多分辨率卫星影像的匹配流程。     

多时相阴影轮廓差分辅助下的城市水体提取

如何避免水体提取中阴影信息与水体信息的混淆,是利用遥感数据提取城市水体信息需要解决的一个问题。本文以高分一号WFV图像及Landsat8 OLI图像为数据源,利用阴影轮廓的位置与形状在不同太阳高度角及太阳方位角下的差异性,提出一种基于多时相阴影轮廓差分的城市水体提取方法(WMSD)。以广州市天河区为试验区进行水体信息提取,同时运用NDWI、MNDWI及SWI指数法分别提取水体信息,进行精度对比分析。结果显示,本文所提出的WMSD方法分类精度超过88%,较NDWI法、SWI法及MNDWI法的水体提取精度分别提高了8.50%、9.50%及4.67%。说明基于阴影轮廓位置与形状的差异提取水体信息的方法能够较好地解决阴影与水体提取信息混淆的问题,为利用遥感数据提取城市地区水体提供了一个可行的处理方法。     

运用人工鱼群算法的3D扫描碎片重建探究

针对传统三维碎片拼接匹配过程中依赖单一特征及存在误差累积的问题,提出了一种运用鱼群算法的全局最优匹配方法。该方法先对碎片点云数据进行多特征提取,结合纹理、专家经验信息对混合在一起的多种类型碎片进行粗糙集分类,之后采用鱼群算法的最优解求得最佳匹配方案。实例验证所提全局匹配方法具有能力强、与初始位置无关及较强的稳健性等特点,为三维碎片的全局匹配提供了一种有效的解决方案。