High precision topographic data on Lop Nor basin's Lake "Great Ear" ? and the timing of its becoming a dry salt lake

High precision elevation measurements using DGPS were carried out along three representative tran- sects for the "Great Ear" area, a dry salt lake within the Lop Nor basin. Results indicate that the Lop Nor basin is only 5.2 m deep and its lowest point occurs at the center of the "Great Ear". In addition, the basin is asymmetric - steeper in the southwest (0.19‰) and gentler in the northeast (0.09‰). Points along the same "Great Ear" ring were found to have an identical elevation value, but different when from different ones (lower towards the center). The spacing of the "Great Ear" rings was found to be closely related with the surface steepness. The closer the "Great Ear" rings are spaced, the steeper the ground surface, and vice versa. These findings support the argument that the "Great Ear" rings are the former shoreline trails left behind by Lop Nor water during the last few episodes of recession towards its total dry up. A comprehensive analysis of the high precision elevation data, historical accounts, aerial and satellite photographs and imagery, and official topographic maps of the study area suggests that the "Great Ear" area in the Lop Nor basin was incorrectly mapped as being covered by a great body of water on the 1963 topographic maps. A re-interpretation of the 1958 aerial photographs and newer remote sensing imagery indicated that the "Great Ear" ring structure was already in place in 1958 and it continued to appear on the subsequent remote sensing data without any major changes. It is estimated that lake water in the "Great Ear" area of the Lop Nor basin disappeared between the late 1930s and early 1940s.     

电动势的测定及其应注意的问题

本课题通过用不同的方法测定的电动势分析,经过电动势测定的误差分析,找到各种方法的利弊,并且对电极制备以及测定电动势的装置进行改进,近而对问题进行探讨。改进后找到实验和研究的捷径,从而在高等学校化学实验以及生产中节省很多的人力和财力。     

基于多元时间序列融合的飞行动作识别方法

针对飞行动作数据随机性强与长度不一致的问题,提出通过减小动态时间规整(DTW)算法的搜索空间,并定义不同特征参数贡献度的概念,实现对飞行数据的多元时间序列融合,从而完成对战术机动动作的识别。通过引入预分类和细分类结合的方式,对动作数据进行预处理,然后根据改进的动态时间规整(WDTW)算法对待测数据进行识别。仿真实验表明,相比传统DTW算法,WDTW算法通过降低算法复杂度,识别计算时间变化明显;对核密度与精准度系数的分析表明识别准确率亦有所提高。实验结果验证了所提方法的准确性。     

一类时滞系统的模糊预测控制方法

对模糊控制和预测控制相结合的控制方法进行研究,提出了一种简单易于实现的模糊预测控制的组合方法.此方法将模糊控制的定性分析特点和预测控制的定量分析特点有效地结合起来,同时积分器的引入可以消除稳态偏差.仿真结果表明,该方法对控制作用的滞后时间不确定的系统十分有效.     

Time Series Prediction Based on Chaotic Attractor

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series.     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

A NEW DESCRIPA NEW DESCRIPTOR OF AMINO ACIDS BASED ON THE THREEDIMENSIONAL VECTOR OF ATOMIC INTERACTION FIELD

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) IS A VERY IMPORTANT TOPIC FOR MANY SCIENTIFIC FIELDS AND CAN BE TERMED AS QUANTITATIVE SEQUENCE ACTIVITY MODELS(QSAM)[1] WHEN REFERRED TO THE RESEARCH ON RELATION- SHIPS BETWEEN STRUCTURES AND ACTIVITIES OF BIOLOGICAL MOLECULES, SUCH AS PROTEINS, AND NUCLEIC ACIDS. HOW- EVER, IT IS VERY DIFFICULT TO ESTABLISH A QSAM MODEL,…     

Besov-Morrey spaces: Function space theory and applications to non-linear PDE

This paper is devoted to the analysis of function spaces modeled on Besov spaces and their applications to non-linear partial differential equations, with emphasis on the incompressible, isotropic Navier-Stokes system and semi-linear heat equations. Specifically, we consider the class, introduced by Hideo Kozono and Masao Yamazaki, of Besov spaces based on Morrey spaces, which we call Besov-Morrey or BM spaces. We obtain equivalent representations in terms of the Weierstrass semigroup and wavelets, and various embeddings in classical spaces. We then establish pseudo-differential and para-differential estimates. Our results cover non-regular and exotic symbols. Although the heat semigroup is not strongly continuous on Morrey spaces, we show that its action defines an equivalent norm. In particular, homogeneous BM spaces belong to a larger class constructed by Grzegorz Karch to analyze scaling in parabolic equations. We compare Karch's results with those of Kozono and Yamazaki and generalize them by obtaining short-time existence and uniqueness of solutions for arbitrary data with subcritical regularity. We exploit pseudo-differential calculus to extend the analysis to compact, smooth, boundaryless, Riemannian manifolds. BM spaces are defined by means of partitions of unity and coordinate patches, and intrinsically in terms of functions of the Laplace operator.