English - according to 的用法说明

本文详细解析了accordingto与accordingas这两个短语的不同用法及注意事项,包括accordingto后接名词或代词的具体场景,以及accordingas后接从句的应用实例。

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1. 用于according to,意为“根据”,为复合介词,后接名词或代词。注意以下用法:  

(1) 主要用来表示“根据”某学说、某书刊、某文件、某人所说等或表示“按照”某法律、某规定、某惯例、某情况等。如:  

Everything went off according to plan. 一切都按照计划实现了。  

According to my watch it is five o’clock. 照我的表,现在是5点钟。  

Each man will be paid according to his ability. 每个人将根据他的能力获得报酬。  

(2) according to 表示“根据”,通常是指根据别人或别处,而不能根据自己,所以其后不能接表示第一人称的代词(如me, us),同时也很少接表示第二人称的代词(you),但用于第三人称(如 him, her, Jim, Mary, the doctor等)则属正常用法。如:  

误:According to me, the film is wonderful.  

正:In my opinion, the film is wonderful. 依我看,这部电影很不错。  

另外注意,according to后也不接view(看法)和opinion(意见)这类词表示看法的词。如:  

误:According to my opinion, he did it very well.  

正:In my opinion, he did it very well. 在我看来,他干得很不错。  

 

2. 用于according as,意为“根据”“随……而定”,后接从句。如:  

Everyone contributes according as he is able. 每个人根据自己的能力做出贡献。  

You will be praised or blamed according as your work is good or bad. 根据你工作的好坏,你会得到表扬或批评。  

但是,对于那些由what, which, whether, how, when, where等引导的句子,其前要用according to,不用according as。如:  

He is an honest businessman, according to what everyone says. 根据大家所说,他是位诚实的商人。  

The amount of tax people pay varies according to where they live. 居住地不同,人们所交的税额也各不相同。  

These apples have been graded according to how big they are. 这些苹果已经按照大小分了等级。  

They were arranged according to when they happened. 它们是按发生的时间安排的。

内容概要:本文探讨了在MATLAB/SimuLink环境中进行三相STATCOM(静态同步补偿器)无功补偿的技术方法及其仿真过程。首先介绍了STATCOM作为无功功率补偿装置的工作原理,即通过调节交流电压的幅值和相位来实现对无功功率的有效管理。接着详细描述了在MATLAB/SimuLink平台下构建三相STATCOM仿真模型的具体步骤,包括创建新模型、添加电源和负载、搭建主电路、加入控制模块以及完成整个电路的连接。然后阐述了如何通过对STATCOM输出电压和电流的精确调控达到无功补偿的目的,并展示了具体的仿真结果分析方法,如读取仿真数据、提取关键参数、绘制无功功率变化曲线等。最后指出,这种技术可以显著提升电力系统的稳定性与电能质量,展望了STATCOM在未来的发展潜力。 适合人群:电气工程专业学生、从事电力系统相关工作的技术人员、希望深入了解无功补偿技术的研究人员。 使用场景及目标:适用于想要掌握MATLAB/SimuLink软件操作技能的人群,特别是那些专注于电力电子领域的从业者;旨在帮助他们学会建立复杂的电力系统仿真模型,以便更好地理解STATCOM的工作机制,进而优化实际项目中的无功补偿方案。 其他说明:文中提供的实例代码可以帮助读者直观地了解如何从零开始构建一个完整的三相STATCOM仿真环境,并通过图形化的方式展示无功补偿的效果,便于进一步的学习与研究。
# 3D Cinemagraphy from a Single Image (CVPR 2023) [Xingyi Li](https://scholar.google.com/citations?user=XDKQsvUAAAAJ&hl)<sup>1,3</sup>, [Zhiguo Cao](http://english.aia.hust.edu.cn/info/1085/1528.htm)<sup>1</sup>, [Huiqiang Sun](https://huiqiang-sun.github.io/)<sup>1</sup>, [Jianming Zhang](https://jimmie33.github.io/)<sup>2</sup>, [Ke Xian](https://kexianhust.github.io/)<sup>3*</sup>, [Guosheng Lin](https://guosheng.github.io/)<sup>3</sup> <sup>1</sup>Huazhong University of Science and Technology, <sup>2</sup>Adobe Research, <sup>3</sup>S-Lab, Nanyang Technological University [Project](https://xingyi-li.github.io/3d-cinemagraphy/) | [Paper](https://github.com/xingyi-li/3d-cinemagraphy/blob/main/pdf/3d-cinemagraphy-paper.pdf) | [arXiv](https://arxiv.org/abs/2303.05724) | [Video](https://youtu.be/sqCy7ffTEEY) | [Supp](https://github.com/xingyi-li/3d-cinemagraphy/blob/main/pdf/3d-cinemagraphy-supp.pdf) | [Poster](https://github.com/xingyi-li/3d-cinemagraphy/blob/main/pdf/3d-cinemagraphy-poster.pdf) This repository contains the official PyTorch implementation of our CVPR 2023 paper "3D Cinemagraphy from a Single Image". ## Installation ``` git clone https://github.com/xingyi-li/3d-cinemagraphy.git cd 3d-cinemagraphy bash requirements.sh ``` ## Usage Download pretrained models from [Google Drive](https://drive.google.com/file/d/1ROxvB7D-vNYl4eYmIzZ5Gitg84amMd19/view?usp=sharing), then unzip and put them in the directory `ckpts`. To achieve better motion estimation results and controllable animation, here we provide the controllable version. Firstly, use [labelme](https://github.com/wkentaro/labelme) to specify the target regions (masks) and desired movement directions (hints): ```shell conda activate 3d-cinemagraphy cd demo/0/ labelme image.png ``` A screenshot here: ![labelme](assets/labelme.png) It is recommended to specify **short** hints rather than long hints to avoid artifacts. Please follow [labelme](https://github.com/wkentaro/labelme) for detailed instructions if needed. After that, we can obtain an image.json file. Our next step is to convert the annotations stored in JSON format into datasets that can be used by our method: ```shell labelme_json_to_dataset image.json # this will generate a folder image_json cd ../../ python scripts/generate_mask.py --inputdir demo/0/image_json ``` We now can create 3D cinemagraphs according to your preference: ```shell python demo.py -c configs/config.yaml --input_dir demo/0/ --ckpt_path ckpts/model_150000.pth --flow_scale 1.0 --ds_factor 1.0 ``` - `input_dir`: input folder that contains src images. - `ckpt_path`: checkpoint path. - `flow_scale`: scale that used to control the speed of fluid, > 1.0 will slow down the fluid. - `ds_factor`: downsample factor for the input images. Results will be saved to the `input_dir/output`. ## Known issues - Due to the limited size of the training dataset, the intermediate frame may occasionally experience flickering. - The utilization of a fixed distance threshold in agglomerative clustering within the disparity space can occasionally result in the presence of visible boundaries between different layers. - We may sometimes see artifacts when the fluid is moving very fast. You can either slow down the fluid by increasing the `flow_scale` or try to specify short hints rather than long hints, to avoid artifacts. - The motion estimation module occasionally generates motion fields that do not perfectly align with the desired preferences. ## Citation If you find our work useful in your research, please consider to cite our paper: ``` @InProceedings{li2023_3dcinemagraphy, author = {Li, Xingyi and Cao, Zhiguo and Sun, Huiqiang and Zhang, Jianming and Xian, Ke and Lin, Guosheng}, title = {3D Cinemagraphy From a Single Image}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {4595-4605} } ``` ## Relevant works - [Animating Pictures with Eulerian Motion Fields](https://openaccess.thecvf.com/content/CVPR2021/papers/Holynski_Animating_Pictures_With_Eulerian_Motion_Fields_CVPR_2021_paper.pdf), CVPR 2021 - [Controllable Animation of Fluid Elements in Still Images](https://openaccess.thecvf.com/content/CVPR2022/papers/Mahapatra_Controllable_Animation_of_Fluid_Elements_in_Still_Images_CVPR_2022_paper.pdf), CVPR 2022 - [Simulating Fluids in Real-World Still Images](https://arxiv.org/pdf/2204.11335), arXiv 2022 - [3D Photography using Context-aware Layered Depth Inpainting](https://openaccess.thecvf.com/content_CVPR_2020/papers/Shih_3D_Photography_Using_Context-Aware_Layered_Depth_Inpainting_CVPR_2020_paper.pdf), CVPR 2020 - [3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image](https://openaccess.thecvf.com/content/CVPR2022/papers/Mu_3D_Photo_Stylization_Learning_To_Generate_Stylized_Novel_Views_From_CVPR_2022_paper.pdf), CVPR 2022 - [3D Moments from Near-Duplicate Photos](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_3D_Moments_From_Near-Duplicate_Photos_CVPR_2022_paper.pdf), CVPR 2022 - [3D Video Loops from Asynchronous Input](https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_3D_Video_Loops_From_Asynchronous_Input_CVPR_2023_paper.pdf), CVPR 2023 ## Acknowledgement This code borrows heavily from [3D Moments](https://github.com/google-research/3d-moments) and [SLR-SFS](https://github.com/simon3dv/SLR-SFS). We thank the respective authors for open sourcing their methods.
06-02
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