二相系での重合・分離を基盤とする実用的有機テルル媒介ラジカル重合の開発

京都大学新制・課程博士博士(工学)甲第24814号工博第5157号新制||工||1985(附属図書館)京都大学大学院工学研究科高分子化学専攻(主査)教授 山子 茂, 教授 辻井 敬亘, 教授 大内 誠学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDGA

Determination of Elevations for Excavation Operations Using Drone Technologies

Using deep learning technology to rapidly estimate depth information from a single image has been studied in many situations, but it is new in construction site elevation determinations, and challenges are not limited to the lack of datasets. This dissertation presents the research results of utilizing drone ortho-imaging and deep learning to estimate construction site elevations for excavation operations. It provides two flexible options of fast elevation determination including a low-high-ortho-image-pair-based method and a single-frame-ortho-image-based method. The success of this research project advanced the ortho-imaging utilization in construction surveying, strengthened CNNs (convolutional neural networks) to work with large scale images, and contributed dense image pixel matching with different scales.This research project has three major tasks. First, the high-resolution ortho-image and elevation-map datasets were acquired using the low-high ortho-image pair-based 3D-reconstruction method. In detail, a vertical drone path is designed first to capture a 2:1 scale ortho-image pair of a construction site at two different altitudes. Then, to simultaneously match the pixel pairs and determine elevations, the developed pixel matching and virtual elevation algorithm provides the candidate pixel pairs in each virtual plane for matching, and the four-scaling patch feature descriptors are used to match them. Experimental results show that 92% of pixels in the pixel grid were strongly matched, where the accuracy of elevations was within ±5 cm.Second, the acquired high-resolution datasets were applied to train and test the ortho-image encoder and elevation-map decoder, where the max-pooling and up-sampling layers link the ortho-image and elevation-map in the same pixel coordinate. This convolutional encoder-decoder was supplemented with an input ortho-image overlapping disassembling and output elevation-map assembling algorithm to crop the high-resolution datasets into multiple small-patch datasets for model training and testing. Experimental results indicated 128×128-pixel small-patch had the best elevation estimation performance, where 21.22% of the selected points were exactly matched with “ground truth,” 31.21% points were accurately matched within ±5 cm. Finally, vegetation was identified in high-resolution ortho-images and removed from corresponding elevation-maps using the developed CNN-based image classification model and the vegetation removing algorithm. Experimental results concluded that the developed CNN model using 32×32-pixel ortho-image and class-label small-patch datasets had 93% accuracy in identifying objects and localizing objects’ edges

Low–High Orthoimage Pairs-Based 3D Reconstruction for Elevation Determination Using Drone

This paper presents a 3D reconstruction method for fast elevation determination on construction sites. The proposed method is intended to automatically and accurately determine construction site elevations using drone-based, low–high orthoimage pairs. This method requires fewer images than other methods for covering a large target area of a construction site. An up–forward–down path was designed to capture approximately -scale images at different altitudes over target stations. A pixel grid matching and elevation determination algorithm was developed to automatically match images in dense pixel grid-style via self-adaptive patch feature descriptors, and simultaneously determine elevations based on a virtual elevation model. The 3D reconstruction results were an elevation map and an orthoimage at each station. Then, the large-scale results of the entire site were easily stitched from adjacent results with narrow overlaps. Moreover, results alignment was automatically performed via the U-net detected ground control point. Experiments validated that in 10–20 and 20–40 orthoimage pairs, 92% of 2,500- and 4,761-pixels were matched in the strongest and strong levels, which was better than sparse reconstructions via structure from motion; moreover, the elevation measurements were as accurate as photogrammetry using multiscale overlapping images

Linear Spaces of Symmetric Matrices with Non-Maximal Maximum Likelihood Degree

We study the maximum likelihood degree of linear concentration models in algebraic statistics. We relate the geometry of the reciprocal variety to that of semidefinite programming. We show that the Zariski closure in the Grassmanian of the set of linear spaces that do not attain their maximal possible maximum likelihood degree coincides with the Zariski closure of the set of linear spaces defining a projection with non-closed image of the positive semidefinite cone. In particular, this shows that this closure is a union of coisotropic hypersurfaces

Study on Employee Satisfaction in Enterprises-- Based on the Empirical Analysis of Ningbo Foreign Trade Enterprises

By improving employee satisfaction and fully mobilize the enthusiasm of the employees improve the core competence of enterprises has become one of the important factors, this article through to ningbo home and foreign trade enterprise employee satisfaction survey, the empirical analysis of the influence factors of employee satisfaction, and puts forward relevant suggestions

Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies

This paper presents an accurate and stable method for object and defect detection and visualization on building and infrastructural facilities. This method uses drones and cameras to collect three- dimensional (3D) point clouds via photogrammetry, and uses orthographic or arbitrary views of the target objects to generate the feature images of points’ spectral, elevation, and normal features. U-Net is implemented in the pixelwise segmentation for object and defect detection using multiple feature images. This method was validated on four applications, including on-site path detection, pavement cracking detection, highway slope detection, and building facade window detection. The comparative experimental results confirmed that U-Net with multiple features has a better pixelwise segmentation performance than separately using each single feature. The developed method can implement object and defect detection with different shapes, including striped objects, thin objects, recurring and regularly shaped objects, and bulky objects, which will improve the accuracy and efficiency of inspection, assessment, and management of buildings and infrastructural facilities

Scan4Façade: Automated As-Is Façade Modeling of Historic High-Rise Buildings Using Drones and AI

This paper presents an automated as-is façade modeling method for existing and historic high-rise buildings, named Scan4Façade. To begin with, a camera drone with a spiral path is employed to capture building exterior images, and photogrammetry is used to conduct three-dimensional (3D) reconstruction and create mesh models for the scanned building façades. High-resolution façade orthoimages are then generated from mesh models and pixelwise segmented by an artificial intelligence (AI) model named U-net. A combined data augmentation strategy, including random flipping, rotation, resizing, perspective transformation, and color adjustment, is proposed for model training with a limited number of labels. As a result, the U-net achieves an average pixel accuracy of 0.9696 and a mean intersection over union of 0.9063 in testing. Then, the developed twoStagesClustering algorithm, with a two-round shape clustering and a two-round coordinates clustering, is used to precisely extract façade elements’ dimensions and coordinates from façade orthoimages and pixelwise label. In testing with the Michigan Central Station (office tower), a historic high-rise building, the developed algorithm achieves an accuracy of 99.77% in window extraction. In addition, the extracted façade geometric information and element types are transformed into AutoCAD command and script files to create CAD drawings without manual interaction. Experimental results also show that the proposed Scan4Façade method can provide clear and accurate information to assist BIM feature creation in Revit. Future research recommendations are also stated in this paper