基于Virtools实现森林植被演替规律的可视化
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摘要
随着数字林业的迅猛发展,林业可视化越来越被林业科研工作者所重视,并且逐渐成为图形图像领域的重要研究议题之一。森林演替规律的探索一直是林业可视化研究的重点,研究森林演替的目的,在于了解和掌握森林演替规律,以便根据森林的功能和性质有目的地进行更新、林分改造。丰产培育等经营要求,通过采取相应的措施,调整森林的组成结构,控制演替的方向和速度,发挥森林最大的经济效益和生态效益。
本文基于Virtools模拟了自然状态下的真实森林演替过程,通过使用Virtools自身具有的可视化编程模块和脚本语言,实现了对自然状态下森林演替过程的模拟,其仿真效果以及演替模拟的结果皆达到实验的预期,并且居于占内存较小、运行速率较高等优点,可以起到对森林生产和景观规划的指导作用,对林业生产和科研具有现实意义。
With the rapid development of digital forestry, forestry scientists pay close attention to forestry visualization and become one of the important research issues for Graphics and Image Processing. Forest succession law exploration has been the focus of research of forestry visualization. Research forest succession is in order to understand and grasp of forest succession law, So according to the functions and properties of forest purposefully update, transformation the structure of Forests and method of Cultivation. Take corresponding measures, Adjust the structure of the forest, Control the direction and speed of the succession, exert maximal economic and ecological benefits of forest. Use virtools as the means of realizing forest succession visualization, Whether the model simulation effect or program realization result are better than any other means of virtual reality. And can be distributed on the Internet. Having The practical significance of forestry related specialized teaching research and forestry production.
本文基于Virtools模拟了自然状态下的真实森林演替过程,通过使用Virtools自身具有的可视化编程模块和脚本语言,实现了对自然状态下森林演替过程的模拟,其仿真效果以及演替模拟的结果皆达到实验的预期,并且居于占内存较小、运行速率较高等优点,可以起到对森林生产和景观规划的指导作用,对林业生产和科研具有现实意义。
With the rapid development of digital forestry, forestry scientists pay close attention to forestry visualization and become one of the important research issues for Graphics and Image Processing. Forest succession law exploration has been the focus of research of forestry visualization. Research forest succession is in order to understand and grasp of forest succession law, So according to the functions and properties of forest purposefully update, transformation the structure of Forests and method of Cultivation. Take corresponding measures, Adjust the structure of the forest, Control the direction and speed of the succession, exert maximal economic and ecological benefits of forest. Use virtools as the means of realizing forest succession visualization, Whether the model simulation effect or program realization result are better than any other means of virtual reality. And can be distributed on the Internet. Having The practical significance of forestry related specialized teaching research and forestry production.
引文
1.柴春梅.互联网短文本信息分类关键技术研究[D].北京：上海交通大学,2009.
2.陈明晶,姚建荣,唐志豪.电子商务系统的商品搜索算法研究[J].计算机工程与应用,2003,(3)：209-211.
3.陈明晶,姚建荣,唐志豪.一种改进的电子商务系统商品搜索算法[J].计算机工程,2006,32(8)：219-220.
4.陈晓红,沈洁等.基于用户潜在偏好的协同过滤[J].计算机工程,2007,33(4)：42-44.
5.丁振国,陈静.基于关联规则的个性化推荐系统[J].计算机集成制造系统,2003,9(10)：891-893.
6.杜波,田怀凤,王立,陆汝占.基于多策略的专业领域术语抽取器的设计[J].计算机工程,2005,31(14)：159-160.
7.韩家炜,坎伯.数据挖据概念与技术[M].北京：机械工业出版社,2001.
8.何安.协同过滤技术在电子商务推荐系统中的应用研究[D].浙江：浙江大学,2007.
9.胡吉祥.基于频繁模式的消息文本聚类研究[D].北京：中国科学院计算机技术研究所,2006.
10.龚才春.短文本语言计算的关键技术研究[D].北京：中科院研究生院,2008.
11.桂诗春.实验心理学语言纲要[M].长沙：湖南教育出版社,1997.
12.侯利军.精通LINQ数据库访问技术[M].北京：人民邮电出版社,2008.
13.姜韶华,档延忠.自动提取含字母词语的新领域新术语的研究[J].计算机工程,2007,33(2)：47-49.
14.李欣璐,刘鲁.基于协同过滤的银行产品推荐系统建模[J].计算机与数字工程,2007,35(9)：6-8.
15.李勇.基于聚类方法对特定领域术语的自动筛选[J].计算机工程与科学,2008,30(2)：64-66.
16.李忠俊,周启海,帅青红.一种基于内容和协同过滤同构化整合的推荐系统模型[J].计算机科学,2009,36(12)：142-145.
17.刘伟权,种义信.自然语言检索与全文情报检索[J].情报理论与实践,1997,20(1)：4346.
18.马春华,朱颢东,钟勇.结合新型文档频和二进制可辨矩阵的特征选择[J].计算机应用,2009,29(8)：2269-2271.
19.邱君瑞.自然语言处理与信息检索系统[J].情报检索,2002,(3)：4748.
20.王灿辉,张敏,马少平.自然语言处理在信息检索中的应用综述[J].中文信息学报,2007,21(2)：35-45.
21.王霞.协同过滤在电子商务推荐系统中应用研究[D].江苏：河海大学,2003.
22.伍谦光.语义学导论[M].长沙：湖南教育出版社,1997.
23.俞奎,王浩,吴信东,姚洪亮.贝叶斯网络的并行EM学习算法[J].模式识别与人工智能,2008,21(5)：670-676.
24.郁雪,李敏强.一种结合有效降维和K-menas聚类的协同过滤推荐模型[J].计算机应用研究,2009,26(10)：3718-3720.
25.詹人风.现代汉语语义学[M].北京：商务印书馆,1997.
26.张海藩.软件工程导论[M].北京：清华大学出版社,2008.
27.张李义.电子商务概论[M].武汉：武汉大学出版社,2002.
28.张学文.智能推荐系统中协同过滤算法的研究[D].上海：上海交通大学,2008.
29. A Artale, B Magnini, C Strapparava. Lexical Discrimination with the Italian Version of WordNet[J]. Proceedings of ACL Workshop on Automatic Information Extraction and Building of Lexical Semantic Resources,1997:32-38.
30. AF Smeaton. Using NLP or NLP Resources for Information Retrieval Tasks[A]. Natural Language Information Retrieval[M]. Kluwer Academic Publisher,1997:99-111.
31. Breesc John S, David Heckerman, Carl Kadie. Empirical Analysis of Predictive Algorithms for Collaborative Filtering [C] Proceedings of UAI'98,1998:43-52.
32. BM Sarwar, G Karypis, JA Konstan, J Riedl. Application of Dimensionality Reduction in Recommender System-A Case Study. In ACM Web KDD 2000 Workshop,2000.
33. David Heckerman. Bayesian Networks for Knowledge Discovery. Advances in Knowledge Discovery and Data Mining[M]. Calif.:AAAI Press,2006.
34. Devasis Bassu, Clifford Behrens. Distributed LSI:Scalable Concept-based Information Retrieval with High Semantic Resolution[A]. Proceeding of the 3rd SIAM International Conference on Data Mining(Text Mining Workshop)[C].San Francisco,CA,2003.1-11.
35. Ellen M,Voorhees. Using WordNet to disambiguate word senses for text retrieval[A]. Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval[C],ACM Press,1993:171-180.
36. Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cereone. Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IR[A]. Proceedings of 19th International Conference on Computational Linguistics[C],2002:72-78.
37. Ghahrmani Z. An Introduction to Hidden Markov Models and Bayesian Networks[J]. Journal of Pattern Recognition and Artificial Intelligence,2001,15(1):9-42.
38. J Wiebe, T O'Hara, R Bruce. Constructing Bayesian Networks from WordNet for Word-Sense Disambiguation:Re presentational and Processing Issues[J]. Proceedings of the Workshop on the Usage of WordNet in Natural Language Processing Systems,1998:23-30.
39. JB Schafer, JA Konstan, J Riedl. Electronic commerce recommender applications[J]. Journal of Data Mining and Knowledge Discovery,2001,2(1/2):115-152.
40. Julia Lerman. Programming Entity Framework[M]. Sebastopol:O'Reilly Media,2009.
41. Miller G, Beckwith R, Fellbaum C. Introduction to WordNet:An Online Lexical Database, in Five Papers on WordNet. CSL report, Cognitive Science Laboratory,Princeton University,1993.
42. O Kurland, L Lee. Corpus structure, language models, and ad hoc information retrieve[A]. Proceeding if the 27th Annual International ACM SIGIR[C]. Sheffield,2004.
43. Resnick P, Varian.H.R. Recommender system[J]. Communication of the ACM,2001,40(3):56-58.
44. Ricci F, Werthner H. Introduction to the special issue Recommender system[J]. International Journal of Electronic Commerce,2006, 1(2):5-9.
45. Riedl J, Dourish P. Introduce to the Special Section on Recommender System[J]. ACM Transactions on Computer Interaction,2005,12:371-373.
46. Sarwar Badnrl, George Karypis, Joseph Konstan. Analysis of Recommendation Algorithms for E-commerce[C].Proceedings of EC'00,2000.
47. Schafer J B, Kosntan J A, Riedl J. E-commence Recommendation Application[Z]. Data Mining and Knowledge Discovery,2001.
48. Schafer J B, Konstan J A, Riedl J. Recommender system in e-commerce[A]. In ACM Conference on Electronic Commerce (EC-99)[C]. New York:ACM Press,1999:158-166.
49. Waleed A, Zaghloul. Text Mining Using Neural Networks[M]. USA:Proquest Information and
Learning Company,2006.
50. Yuhua Li, David McLean, Zuhair A.Bandar, James D. O'Shea,Keeley Crockett. Sentence Similarity Based on Semantic Nets and Corpus Statistics[J]. IEEE Transaction on Knowledge and Data Engineering,2006,18(8):1138-1150.
51. Vossen P. EuroWordNet:A Multilingual Database with Lexical Semantic Networks[M]. Dordrecht: Kluwer,1998.
2.陈明晶,姚建荣,唐志豪.电子商务系统的商品搜索算法研究[J].计算机工程与应用,2003,(3)：209-211.
3.陈明晶,姚建荣,唐志豪.一种改进的电子商务系统商品搜索算法[J].计算机工程,2006,32(8)：219-220.
4.陈晓红,沈洁等.基于用户潜在偏好的协同过滤[J].计算机工程,2007,33(4)：42-44.
5.丁振国,陈静.基于关联规则的个性化推荐系统[J].计算机集成制造系统,2003,9(10)：891-893.
6.杜波,田怀凤,王立,陆汝占.基于多策略的专业领域术语抽取器的设计[J].计算机工程,2005,31(14)：159-160.
7.韩家炜,坎伯.数据挖据概念与技术[M].北京：机械工业出版社,2001.
8.何安.协同过滤技术在电子商务推荐系统中的应用研究[D].浙江：浙江大学,2007.
9.胡吉祥.基于频繁模式的消息文本聚类研究[D].北京：中国科学院计算机技术研究所,2006.
10.龚才春.短文本语言计算的关键技术研究[D].北京：中科院研究生院,2008.
11.桂诗春.实验心理学语言纲要[M].长沙：湖南教育出版社,1997.
12.侯利军.精通LINQ数据库访问技术[M].北京：人民邮电出版社,2008.
13.姜韶华,档延忠.自动提取含字母词语的新领域新术语的研究[J].计算机工程,2007,33(2)：47-49.
14.李欣璐,刘鲁.基于协同过滤的银行产品推荐系统建模[J].计算机与数字工程,2007,35(9)：6-8.
15.李勇.基于聚类方法对特定领域术语的自动筛选[J].计算机工程与科学,2008,30(2)：64-66.
16.李忠俊,周启海,帅青红.一种基于内容和协同过滤同构化整合的推荐系统模型[J].计算机科学,2009,36(12)：142-145.
17.刘伟权,种义信.自然语言检索与全文情报检索[J].情报理论与实践,1997,20(1)：4346.
18.马春华,朱颢东,钟勇.结合新型文档频和二进制可辨矩阵的特征选择[J].计算机应用,2009,29(8)：2269-2271.
19.邱君瑞.自然语言处理与信息检索系统[J].情报检索,2002,(3)：4748.
20.王灿辉,张敏,马少平.自然语言处理在信息检索中的应用综述[J].中文信息学报,2007,21(2)：35-45.
21.王霞.协同过滤在电子商务推荐系统中应用研究[D].江苏：河海大学,2003.
22.伍谦光.语义学导论[M].长沙：湖南教育出版社,1997.
23.俞奎,王浩,吴信东,姚洪亮.贝叶斯网络的并行EM学习算法[J].模式识别与人工智能,2008,21(5)：670-676.
24.郁雪,李敏强.一种结合有效降维和K-menas聚类的协同过滤推荐模型[J].计算机应用研究,2009,26(10)：3718-3720.
25.詹人风.现代汉语语义学[M].北京：商务印书馆,1997.
26.张海藩.软件工程导论[M].北京：清华大学出版社,2008.
27.张李义.电子商务概论[M].武汉：武汉大学出版社,2002.
28.张学文.智能推荐系统中协同过滤算法的研究[D].上海：上海交通大学,2008.
29. A Artale, B Magnini, C Strapparava. Lexical Discrimination with the Italian Version of WordNet[J]. Proceedings of ACL Workshop on Automatic Information Extraction and Building of Lexical Semantic Resources,1997:32-38.
30. AF Smeaton. Using NLP or NLP Resources for Information Retrieval Tasks[A]. Natural Language Information Retrieval[M]. Kluwer Academic Publisher,1997:99-111.
31. Breesc John S, David Heckerman, Carl Kadie. Empirical Analysis of Predictive Algorithms for Collaborative Filtering [C] Proceedings of UAI'98,1998:43-52.
32. BM Sarwar, G Karypis, JA Konstan, J Riedl. Application of Dimensionality Reduction in Recommender System-A Case Study. In ACM Web KDD 2000 Workshop,2000.
33. David Heckerman. Bayesian Networks for Knowledge Discovery. Advances in Knowledge Discovery and Data Mining[M]. Calif.:AAAI Press,2006.
34. Devasis Bassu, Clifford Behrens. Distributed LSI:Scalable Concept-based Information Retrieval with High Semantic Resolution[A]. Proceeding of the 3rd SIAM International Conference on Data Mining(Text Mining Workshop)[C].San Francisco,CA,2003.1-11.
35. Ellen M,Voorhees. Using WordNet to disambiguate word senses for text retrieval[A]. Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval[C],ACM Press,1993:171-180.
36. Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cereone. Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IR[A]. Proceedings of 19th International Conference on Computational Linguistics[C],2002:72-78.
37. Ghahrmani Z. An Introduction to Hidden Markov Models and Bayesian Networks[J]. Journal of Pattern Recognition and Artificial Intelligence,2001,15(1):9-42.
38. J Wiebe, T O'Hara, R Bruce. Constructing Bayesian Networks from WordNet for Word-Sense Disambiguation:Re presentational and Processing Issues[J]. Proceedings of the Workshop on the Usage of WordNet in Natural Language Processing Systems,1998:23-30.
39. JB Schafer, JA Konstan, J Riedl. Electronic commerce recommender applications[J]. Journal of Data Mining and Knowledge Discovery,2001,2(1/2):115-152.
40. Julia Lerman. Programming Entity Framework[M]. Sebastopol:O'Reilly Media,2009.
41. Miller G, Beckwith R, Fellbaum C. Introduction to WordNet:An Online Lexical Database, in Five Papers on WordNet. CSL report, Cognitive Science Laboratory,Princeton University,1993.
42. O Kurland, L Lee. Corpus structure, language models, and ad hoc information retrieve[A]. Proceeding if the 27th Annual International ACM SIGIR[C]. Sheffield,2004.
43. Resnick P, Varian.H.R. Recommender system[J]. Communication of the ACM,2001,40(3):56-58.
44. Ricci F, Werthner H. Introduction to the special issue Recommender system[J]. International Journal of Electronic Commerce,2006, 1(2):5-9.
45. Riedl J, Dourish P. Introduce to the Special Section on Recommender System[J]. ACM Transactions on Computer Interaction,2005,12:371-373.
46. Sarwar Badnrl, George Karypis, Joseph Konstan. Analysis of Recommendation Algorithms for E-commerce[C].Proceedings of EC'00,2000.
47. Schafer J B, Kosntan J A, Riedl J. E-commence Recommendation Application[Z]. Data Mining and Knowledge Discovery,2001.
48. Schafer J B, Konstan J A, Riedl J. Recommender system in e-commerce[A]. In ACM Conference on Electronic Commerce (EC-99)[C]. New York:ACM Press,1999:158-166.
49. Waleed A, Zaghloul. Text Mining Using Neural Networks[M]. USA:Proquest Information and
Learning Company,2006.
50. Yuhua Li, David McLean, Zuhair A.Bandar, James D. O'Shea,Keeley Crockett. Sentence Similarity Based on Semantic Nets and Corpus Statistics[J]. IEEE Transaction on Knowledge and Data Engineering,2006,18(8):1138-1150.
51. Vossen P. EuroWordNet:A Multilingual Database with Lexical Semantic Networks[M]. Dordrecht: Kluwer,1998.