Magnetic characterization of Co1-xNixFe2O4 (0 <= x <= 1) nanoparticles prepared by co-precipitation route

Magnetic nanoparticles of Ni-doped cobalt ferrite [Co1-xNixFe2O4(0 <= x <= 1)] synthesized by coprecipitation route have been studied as a function of doping concentration (x) and particle size. The size of the particles as determined by X-ray diffractometer (XRD) and transmission electron microscope (TEM) analyses was found in the range 12-48 nm. The coercivity (H-C) and saturation magnetization (M-S) showed a decreasing behavior with increasing Ni concentration. M-S of all the samples annealed at 600 degrees C lies in the range 65.8-13.7 emu/gm. Field-cooled (FC) studies of the samples showed horizontal shift (exchange bias) and vertical shift in the magnetization loop. Strong decrease in exchange bias (H-b) and vertical shift (delta M) was found for low Ni concentrations while negligible decrease was found at higher concentrations. The presence of exchange bias in the low Ni-concentration region has been explained with reference to the interface spins interaction between a surface region (with structural and spin disorder) and a ferrimagnetic core region. M(T) graphs of the samples showed a decreasing trend of blocking temperature (T-b) with increasing Ni concentration. The decrease of T-b with increasing Ni concentration has been attributed to the lower anisotropy energy of Ni+2 ions as compared to Co+2 that increases the probability of the jump across the anisotropy barrier which in turn decreases the blocking temperature of the system.

燕沟基本农田粮食稳产高产综合配套技术及试验示范

提出了以新修梯田耕作栽培技术和作物抗旱节水技术为中心的燕沟流域基本农田粮食高产综合配套技术体系 ,并在燕沟流域经过 3年试验示范 ,使粮食生产潜力实现率由原来的 2 3%～ 48% ,平均已达到 5 4 %～ 64% ,典型的抗旱节水综合试验示范地块的潜力实现率提高到 78%～ 87% ;作物平均单产和水分利用效率较原来分别提高了 63%和 5 9.1 % ,粮食单产平均较 1 997年翻了两番 ,粮食产量较 1 997年提高了 33.7%～ 62 .1 % ,人均产粮达到了 5 0 0 kg以上 ,基本实现了减地不减产和增产增效的效果

面向流程企业的可配置MES体系结构

过程系统工程的概念内涵将企业的经营管理、供应链和MES的运作紧密联系在一起,当企业组织结构调整、工艺过程改造、产品方案变动等发生时,要求MES系统具有对工厂过程和业务过程持续变化的适应能力。本文提出了一种面向流程企业的可配置MES体系结构,包含模型层、协同平台层和功能层3个配置层次,通过模型层实现业务功能与工厂描述分离;协同平台层实现业务逻辑与技术支撑分离,以及实现整体过程协同和全局优化;功能层实现不同厂商产品间标准化的功能接口。最后,以基于可配置体系结构的MES产品(SMES)为例,分析了各层次的实现方式以及对流程企业业务持续变化的适应性验证。

Web-based spatiotemporal visualization of marine environment data

With long-term marine surveys and research, and especially with the development of new marine environment monitoring technologies, prodigious amounts of complex marine environmental data are generated, and continuously increase rapidly. Features of these data include massive volume, widespread distribution, multiple-sources, heterogeneous, multi-dimensional and dynamic in structure and time. The present study recommends an integrative visualization solution for these data, to enhance the visual display of data and data archives, and to develop a joint use of these data distributed among different organizations or communities. This study also analyses the web services technologies and defines the concept of the marine information gird, then focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method. We discuss how marine environmental data can be organized based on the spatiotemporal visualization method, and how organized data are represented for use with web services and stored in a reusable fashion. In addition, we provide an original visualization architecture that is integrative and based on the explored technologies. In the end, we propose a prototype system of marine environmental data of the South China Sea for visualizations of Argo floats, sea surface temperature fields, sea current fields, salinity, in-situ investigation data, and ocean stations. An integration visualization architecture is illustrated on the prototype system, which highlights the process-oriented temporal visualization method and demonstrates the benefit of the architecture and the methods described in this study.

可重构装配系统中智能体的开发

为了实现可重构装配系统的快速重构能力,在分析了传统控制结构的基础上,结合分层递阶和分布式控制的优点,提出了基于多智能体系统的混合控制体系结构。这种结构具有低复杂性、强鲁棒性、易维护性、可扩展性以及可重用性等特点,能够适应重构系统的要求。在遵守智能物理代理基金会规范的前提下,以Java智能体开发平台进行了装配系统中各层智能体的实例开发。

一种新的多智能体强化学习算法及其在多机器人协作任务中的应用

在多机器人系统中 ,评价一个机器人行为的好坏常常依赖于其它机器人的行为 ,此时必须采用组合动作以实现多机器人的协作 ,但采用组合动作的强化学习算法由于学习空间异常庞大而收敛得极慢 .本文提出的新方法通过预测各机器人执行动作的概率来降低学习空间的维数 ,并应用于多机器人协作任务之中 .实验结果表明 ,基于预测的加速强化学习算法可以比原始算法更快地获得多机器人的协作策略 .

分布式强化学习系统的体系结构研究

强化学习是一种重要的机器学习方法，随着计算机网络和分布式处理技术的飞速发展，多智能体系统中的分布式强化学习方法正受到越来越多的关注。论文将目前已有的各种分布式强化学习方法总结为中央强化学习、独立强化学习、群体强化学习、社会强化学习四类，然后探讨了这四类分布式强化学习方法的体系结构框架，并给出了这四类分布式强化学习方法的形式化定义。

多智能体系统中的分布式强化学习研究现状

对目前世界上分布式强化学习方法的研究成果加以总结,分析比较了独立强化学习、社会强化学习和群体强化学习三类分布式强化学习方法的特点、差别和适用范围,并对分布式强化学习仍需解决的问题和未来的发展方向进行了探讨。

水-盐-气天然流体的物理化学性质模拟与流体包裹体应用研究

Natural fluids with water-salt-gas are often found in every sphere of the Earth, whose physicochemical properties and geochemical behaviors are complicated. To study these properties and behaviors turns out to be one of the challenging issues in geosciences. Traditional approaches mainly depend on experiments and observations. However, it is impossible to obtain a large number of data covering a large T-P space of the Earth by experimental methods in the near future, which will hinder the advance of the theoretical study. Therefore, it is important to model natural fluids by advanced theoretical methods, by which limited experimental data can be extended to a large temperature-pressure-composition space. Physicochemical models developed in this dissertation are not only more accurate, but also extend the applied T-P-m region of the experimental data of the multi-fluid systems by about two times. These models provide the new and accurate theoretical tools for the geochemical research, especially for the water-rock interactions and the study of the fluid inclusions. The main achievements can be summarized as follows: (1) A solubility model on components of natural gases is presented. The solubility model on the systems of CH4-H2O-NaCl, C2H6-H2O-NaCl or N2-H2O-NaCl takes advantage of modern physicochemical theory and methods, and is an improvement over previous models whose prediction and precision are relatively poor. The model can predict not only the gas solubility in liquid phase but also water content in the gas phase. In addition, it can predict gases (methane or nitrogen) solubility in seawater and brine. Isochores can be determined, which are very important in the interpretation of fluid inclusions. (2) A density model on common aqueous salt solutions is developed. The density models with high precision for common aqueous salt solutions (H2O-NaCl, H2O-LiCl, H2O-KCl, H2O-MgCl2, H2O-CaCl2, H2O-SrCl2 or H2O-BaCl2) are absent in the past. Previous density models are limited to the relatively small range of experimental data, and cannot meet the requirement of the study of natural fluids. So a general density model of the above systems is presented by us based on the international standard density model of the water. The model exceeds the other models in both precision and prediction. (3) A viscosity model on common aqueous alkali-chloride solutions is proposed. Dynamic viscosity of water-salt systems, an important physics variable, is widely used in three-dimension simulation of the fluids. But in most cases, due to the lack of viscosity models with a wide T-P range, the viscosity of aqueous salt solutions is replaced by that of the water, giving rise to a relatively large uncertainty. A viscosity model with good prediction for the systems (H2O-NaCl, H2O-LiCl or H2O-KCl) is presented on the base of the international standard viscosity model of water and the density model developed before. (4) Equation of State applied in fluid inclusions. The best Equations of State in the world developed by others or us recently are applied in the study of the fluid inclusions. Phase equilibria and isochores of unitary system (e.g. H2O, CO2, CH4, O2, N2, C2H6 or H2S), binary H2O-NaCl system and ternary H2O-CH4-NaCl system are finished. From these programs and thermodynamic equations of coexisting ores, the physicochemical conditions before or after the deposits form can be determined. To some extent, it is a better tool.

单（多）井抽灌对浅部地温场的影响研究

The technique of energy extraction using groundwater source heat pumps, as a sustainable way of low-grade thermal energy utilization, has widely been used since mid-1990's. Based on the basic theories of groundwater flow and heat transfer and by employing two analytic models, the relationship of the thermal breakthrough time for a production well with the effect factors involved is analyzed and the impact of heat transfer by means of conduction and convection, under different groundwater velocity conditions, on geo-temperature field is discussed.A mathematical model, coupling the equations for groundwater flow with those for heat transfer, was developed. The impact of energy mining using a single well system of supplying and returning water on geo-temperature field under different hydrogeological conditions, well structures, withdraw-and-reinjection rates, and natural groundwater flow velocities was quantitatively simulated using the finite difference simulator HST3D. Theoretical analyses of the simulated results were also made. The simulated results of the single well system indicate that neither the permeability nor the porosity of a homogeneous aquifer has significant effect on the temperature of the production segment provided that the production and injection capability of each well in the aquifers involved can meet the designed value. If there exists a lower permeable interlayer, compared with the main aquifer, between the production and injection segments, the temperature changes of the production segment will decrease. The thicker the interlayer and the lower the interlayer permeability, the longer the thermal breakthrough time of the production segment and the smaller the temperature changes of the production segment. According to the above modeling, it can also be found that with the increase of the aquifer thickness, the distance between the production and injection screens, and/or the regional groundwater flow velocity, and/or the decrease of the production-and-reinjection rate, the temperature changes of the production segment decline. For an aquifer of a constant thickness, continuously increase the screen lengths of production and injection segments may lead to the decrease of the distance between the production and injection screens, and the temperature changes of the production segment will increase, consequently.According to the simulation results of the single well system, the parameters, that can cause significant influence on heat transfer as well as geo-temperature field, were chosen for doublet system simulation. It is indicated that the temperature changes of the pumping well will decrease as the aquifer thickness, the distance between the well pair and/or the screen lengths of the doublet increase. In the case of a low permeable interlayer embedding in the main aquifer, if the screens of the pumping and the injection wells are installed respectively below and above the interlayer, the temperature changes of the pumping well will be smaller than that without the interlay. The lower the permeability of the interlayer, the smaller the temperature changes. The simulation results also indicate that the lower the pumping-and-reinjection rate, the greater the temperature changes of the pumping well. It can also be found that if the producer and the injector are chosen reasonably, the temperature changes of the pumping well will decline as the regional groundwater flow velocity increases. Compared with the case that the groundwater flow direction is perpendicular to the well pair, if the regional flow is directed from the pumping well to the injection well, the temperature changes of the pumping well is relatively smaller.Based on the above simulation study, a case history was conducted using the data from an operating system in Beijing. By means of the conceptual model and the mathematical model, a 3-D simulation model was developed and the hydrogeological parameters and the thermal properties were calibrated. The calibrated model was used to predict the evolution of the geo-temperature field for the next five years. The simulation results indicate that the calibrated model can represent the hydrogeological conditions and the nature of the aquifers. It can also be found that the temperature fronts in high permeable aquifers move very fast and the radiuses of temperature influence are large. Comparatively, the temperature changes in clay layers are smaller and there is an obvious lag of the temperature changes. According to the current energy mining load, the temperature of the pumping wells will increase by 0.7°C at the end of the next five years. The above case study may provide reliable base for the scientific management of the operating system studied.

汉语文本语篇语句焦点确定及焦点与重音关系研究

This study investigated the method of the focus identification in Chinese text discourse and the relationship between accent and focus, large corpus analysis and decision tree were used in the research. The main results are: 1. Based on the concept of the Focus and understanding of the discourse, Foci identification is consistent and steady; 2. Special Focus markers and specific Focus constructions have greater influence than special constituent order on identifying Focus in Chinese discourse; while information states also have great influence on focus identifying; part of speech，information state, the relative position in the sentence, focus-sensitive operator, specific Focus constructions, contrast relations, relations between the sentences are important factors to focus identifying; 3. Using multi-dimensional tagging and knowledge discovery, it is a feasible way to construct and employ decision trees by computing tagging results to identify Focus; 4. Focus predicting also depends on literal types and styles of the discourse, several types of decision trees should be constructed for different literal types; 5. In the monologue discourse, the most prominent accent is located on the Focus word or in the scope of the Focus; there are some kinds of rules on accent assignment in broad Focus; it is necessary to analyze and classify focus structure for the research of relations between accent and Focus.

A Theory of Networks for Appxoimation and Learning

Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. This paper considers the problems of an exact representation and, in more detail, of the approximation of linear and nolinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of three-layer networks that we call Generalized Radial Basis Functions (GRBF), since they are mathematically related to the well-known Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines, but are also closely related to pattern recognition methods such as Parzen windows and potential functions and to several neural network algorithms, such as Kanerva's associative memory, backpropagation and Kohonen's topology preserving map. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage. The paper introduces several extensions and applications of the technique and discusses intriguing analogies with neurobiological data.

Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples

Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional function. From this point of view, this form of learning is closely related to regularization theory. In this note, we extend the theory by introducing ways of dealing with two aspects of learning: learning in the presence of unreliable examples and learning from positive and negative examples. The first extension corresponds to dealing with outliers among the sparse data. The second one corresponds to exploiting information about points or regions in the range of the function that are forbidden.

Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.