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极度灵感

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[交流] 【原创】总结文献的思路已有81人参与

看到一个不错的写文献总结的提纲，列出来分享一下。

原本是一门课的作业，老师布置了几篇论文，让写个一页的总结，paper summary。这个如果想糊弄，其实是很容易的，随便copy几段文字下来，就一页了。但我想这样做没什么意义，不如认认真真的理一些思路出来。于是在网上搜到了这个样板提纲。

我认为这个提纲好在，他比较重视introduction部分的阅读，前三个问题都是intro里面的。弄懂这三个问题，才能更好的把握文章背景信息。话说回来，要写一篇好的文章，前8点内容是必然要交代清楚的。如果从文章里提取不到这些信息，那文章不能说是成功的文章，关键的问题都没交代清楚。

模板精简并翻译如下：

1. What is the problem the authors are trying to solve? 作者想解决什么问题？(研究目的)

2. What other approaches or solutions existed at the time that this work was done? 这个问题现在有什么解决方法？

3. What was wrong with the other approaches or solutions? 这些方法存在什么问题？

4. What is the authors' approach or solution? 作者提出了什么方法？

5. Why is it better than the other approaches or solutions? 为什么说作者的这个方法好于其他方法？

6. How did they test their solution? 作者如何检验自己的方法？

7. How does it perform? 方法检验效果如何？

8. Why is this work important? 为什么该文的研究比较重要？（研究意义）

more comments/questions 附加评注，或者问题。随读者背景和认识程度的不同，可以灵活的提出一些问题。

链接在此： http://users.soe.ucsc.edu/~sbrandt/221/samplesummary.shtml

[ Last edited by 极度灵感 on 2010-1-22 at 12:22 ]

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极度灵感

银虫 (正式写手)

应助: 0 (幼儿园)
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金币: 266.6
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专业: Geo Math&Computing

提供一个case

★ ★
xinmeng8803(金币+2,VIP+0):欢迎原创 12-27 11:09

本人拙作，也就是作业。欢迎拍砖。

summary on “A. Boucher (2009): Considering complex training images with search tree partitioning, Computers & Geosciences, 35, 1151-1158.”

1. What is the problem the authors are trying to solve?
To solve the difficulty of large complex TI to be used in SNESIM.

2. What other approaches or solutions existed at the time that this work was done?
To decrease the size of search tree, general approaches are taking a smaller TI or using a smaller template. Besides, there are two alternative approaches in current practice:  1) region approach 2) probability ﬁeld approach.

3. What was wrong with the other approaches or solutions?
A major issue with the region approach is that there is no guarantee that the different TIs used are compatible with one another. The problem with this probability ﬁeld approach is that integration changes the conditional probability derived from the search tree and affects the pattern reproduction in an unknown manner.

4. What is the authors' approach or solution?
  The authors proposed a search tree partitioning approach which includes:
 Applying the filters on the TI to gain a series of filter scores that are indicative of the underlying patterns.
 Defining partition classes through the filter scores with a clustering algorithm
 Building search tree for each partition class
 At each pixel along the simulation path, the partition class is retrieved first and used to select the appropriate search tree.

5. Why is it better than the other approaches or solutions?
The proposed algorithm adds local information without using a probability field which distorts the conditional distribution obtained from the TI. It improves the region approach by requiring a single complete TI and implicitly models the transitions between regions. It facilitates the use of hierarchical framework by identifying the hierarchy structures with partition classes.

6. How did they test their solution?
They gave two examples of implementation of their approach. The first example is to simulate a series of fracture.  The second is to simulate Shallow-water tidal system. They also made the comparison to the result pattern by global search tree approach.

7. How does it perform?
Performance is very good. In the fracture case, the trend in orientation is well reproduced. In the second one the geological consistency of the TI is reproduced. These features are not exhibited in the realization by global search tree approach. Besides, the speed improved.

8. Comments & other questions
*The simulation grid can be obtained in many ways. Do the different simulation grids influence realization?
*FILTERSIM as a similar approach is interesting to compare with the authors approach.
*A. Boucher’s effort to integrate new algorithm to SGeMS is interesting and respectable. Open Geostatistic software will allow more researchers to test, apply and extend the algorithm in an easier manner.

[ Last edited by 极度灵感 on 2009-12-27 at 06:59 ]