Vegetation change detection using artificial neural networks with ancillary data in Xishuangbanna,Yunnan Province,China

Timely and accurate change detection of the Earth’s surface features provides the foundation for better planning, management and environmental studies. In this study ANN change detection was used to perform vegetation change detection, and was compared with post-classification method. Before the post-classification was performed the ANN classification was used to yield multitemporal vegetation maps. ANN were also used to perform a one-pass classification for the images in 2003 and 2004. DEM and slope were used as two extra channels. During the training stage, the training data was separated into 82 subclasses including 36 change subclasses and 46 no change subclasses. Moreover NDVI differencing methods were used to develop the change mask. The result showed that combining the NDVI differencing method with visual interpretation when identifying reference areas can produce more accurate change detection results for the ANN one pass change classification. Moreover, it is effective to use elevation and slope as extra channels together with PCA components, to perform ANN-based change detection in mountainous study areas. It is also important to separate the vegetation transition classes into subclasses based on spectral response patterns, especially for mountainous terrains. This processing can reduce the topographic effect and improve the change detection accuracy.

     

A taxonomic investigation of variation withinCissus quadrangularis L. (Vitaceae) in Kenya

Using information from literature as well as records of localities on herbarium specimen labels at the East African Herbarium Nairobi (EA), materials ofCissus quadrangularis L.,C. cactiformis Gilg. andC. quinquangularis Chiov. were collected and new localities were sought. On the basis of the nature of their stem angles, the specimens ofC. quadrangularis were sorted into two variants, A and B. Variant A has smooth stem angles while variant B has rough stem angles. The authors have proposed that variant B should be recognized as a new variety withinC. quadrangularis. Variant A is the type variety ofC. quadrangularis. A map showing the distribution of the two variants in Kenya has been prepared. The rough-edged variant was found to be absent from the low-lying semi-arid regions of northern Kenya. The variants were found occurring in distinct, separate populations but without separate geographical patterns. The results of t-test show no significant difference (p=0.05) between the means of measurements of dimensions of various organs in the two variants. The stem angles of specimens of the two variants were studied under scanning electron microscope (SEM). This showed a clear morphological difference between the two variants. The results of the investigation support the hypothesis that the variation withinC. quadrangularis represents distinct kinds that can be recognized as taxa at infra-specific level. Foundation item: Supported by the China Ministry of Education Fund for University Staff Development in Key Subject Areas and Kenyatta University. Biography: Robert Wahiti. Gituru (1967-), male, Kenya, Ph. D. candidate, research direction: plant biosystematics.     

考夫曼法采观今论

围绕审知法采的连系性,考夫曼将演绎、归纳、骈推与比类连成体系。其中,演绎为仆,比类是首脑,归纳辅佐,骈推守护清白。演绎在审判训疏与说服总系统中是协和元。考夫曼归纳观的归宿是对旧的律意说服的重新组织。其创新点是说服的三阶式。他的贡献是以逻辑组织连系骈推与演绎。考夫曼认为,法采进程的本质不是逻辑推合,而是比较。律令的颁布与司法中逻辑组织的相似性在于其比类基础。考察了考夫曼比较点的选择后显示,不可抹煞两领域之间的红线。认为考夫曼的重大贡献是将比类引入审判陈说的总组织,它是原省、衡议后的第三元。在论述的基础上,为比类设定了两则对行的准法。     

Automatic generation of very efficient programs by Generalized Partial Computation

Generalized Partial Computation (GPC) is a program transformation method utilizing partial information about input data, properties of auxiliary functions and the logical structure of a source program. GPC uses both an inference engine such as a theorem prover and a classical partial evaluator to optimize programs. Therefore, GPC is more powerful than classical partial evaluators but harder to implement and control. We have implemented an experimental GPC system called WSDFU (Waseda Simplify-Distribute-Fold-Unfold). This paper discusses the power of the program transformation system, its theorem prover and future works.     

A NESTED PARTITIONS FRAMEWORK FOR SOLVING LARGE-SCALE MULTICOMMODITY FACILITY LOCATION PROBLEMS

Large-scale multicommodity facility location problems are generally intractable with respect to standard mixed-integer programming (MIP) tools such as the direct application of general-purpose Branch & Cut (BC) commercial solvers i.e. CPLEX. In this paper, the authors investigate a nested partitions (NP) framework that combines meta-heuristics with MIP tools (including branch-and-cut). We also consider a variety of alternative formulations and decomposition methods for this problem class. Our results show that our NP framework is capable of efficiently producing very high quality solutions to multicommodity facility location problems. For large-scale problems in this class, this approach is significantly faster and generates better feasible solutions than either CPLEX (applied directly to the given MIP) or the iterative Lagrangian-based methods that have generally been regarded as the most effective structure-based techniques for optimization of these problems. We also briefly discuss some other large-scale MIP problem classes for which this approach is expected to be very effective. This work is supported partly by the National Science Foundation under grant DMI-0100220, DMI-0217924, by the Air Force of Scientific Research under grant F49620-01-1-0222, by Rockwell Automation, and by John Deere Horicon Works. Leyuan Shi is a Professor of the Department of Industrial Engineering at University of Wisconsin-Madison. She received her Ph.D. in Applied Mathematics from Harvard University in 1992, her M.S. in Engineering from Harvard University in 1990, her M.S. in Applied Mathematics from Tsinghua University in 1985, and her B.S. in Mathematics from Nanjing Normal University in 1982. Dr. Shi has been involved in undergraduate and graduate teaching, as well as research and professional service. Dr. Shi’s research is devoted to the theory and applications of large-scale optimization algorithms, discrete event simulation and modeling and analysis of discrete dynamic systems. She has published many papers in these areas. Her work has appeared in Discrete Event Dynamic Systems, Operations Research, Management Science, IEEE Trans., and, IIE Trans. She is currently a member of the editorial board for Journal of Manufacturing & Service Operations Management, is an Associate Editor of Journal of Discrete Event Dynamic Systems, and an Associate Editor of INFORMS Journal on Computing. Dr. Shi is a member of IEEE and INFORMS. Robert R. Meyer received a B.S. in Mathematics from Caltech and an M.S. and a Ph.D. in Computer Sciences from the University of Wisconsin-Madison. He is currently Professor of Computer Sciences at the University of Wisconsin-Madison, where he has been on the faculty since 1973. He has co-edited six volumes of optimization conference proceedings and written more than 80 articles focusing on areas such as nonlinear network optimization, parallel decomposition algorithms for large-scale optimization, genetic algorithms, and theory and applications of discrete optimization. Mehmet Bozbay is a research assistant in the Department of Industrial Engineering at University of Wisconsin-Madison. He is currently working towards his Ph.D. degree in Industrial Engineering. He received M.S. degrees in Industrial Engineering and Computer Sciences from University of Wisconsin-Madison, and a B.S. degree in Mathematics from Beloit College. He has been dealing with real-life supply chain optimization problems for the last 4 years. He is a member of INFORMS. Andrew J. Miller is an Assistant Professor of the Department of Industrial Engineering at the University of Wisconsin—Madison. He received his Ph.D. in Industrial Engineering from the Georgia Institute of Technology in 1999, his M.S. in Operations Research from the Georgia Institute of Technology in 1996, and his B.S. in Mathematics from Furman University in 1994. Dr. Miller has been involved in research, undergraduate and graduate teaching, and professional service, as well as in some software prototyping and development. Dr. Miller’s research focuses on theoretical and computational aspects of mixed integer programming, and on its application to areas in production planning, supply chain design, and other fields. He has published several papers in these areas, including articles in Mathematical Programming, Operations Research, and European Journal of Operations Research. He has refereed numerous articles for the journals mentioned above, as well as for Management Science, Annals of Operations Research, and others. Dr. Miller is a member of INFORMS and of the Mathematical Programming Society.