Yanbin Yuan

An improved PSO approach to short‐term economic dispatch of cascaded hydropower plants

Purpose – The purpose of this paper is to establish the optimization model and solve the short‐term economic dispatch of cascaded hydro‐plants.Design/methodology/approach – An improved particle swarm optimization (IPSO) approach is proposed to solve the short‐term economic dispatch of cascaded hydroelectric plants. The water transport delay time between connected reservoirs is taken into account and it is easy in dealing with the difficult hydraulic and power coupling constraints using the proposed method in practical cascaded hydroelectric plants operation. The feasibility of the proposed method is demonstrated for actual cascaded hydroelectric plant.Findings – The simulation results show that this approach can prevent premature convergence to a high degree and keep a rapid convergence speed.Research limitations/implications – The optimal values of parameters in the proposed method are the main limitations where the method will be applied to the economic operation of the hydro‐plant.Practical implication...

Development of a campus information navigation system based on GIS

Using GIS and other spatial information technology to build digital campus is an effective way to achieve intelligent management of the campus. According to the actual situation of Wuhan University of Technology, the framework of GIS-based navigation intelligent system for Wuhan University of Technology (WHUT) is proposed. The development and implementation of GIS-based campus information navigation system is presented from data collection, database design, system implementation and other aspects. The system realizes some functions such as education management, information inquiry, service guide, virtual campus navigation and decision making etc. Eventually, it draws a conclusion and prospects the future of campus GIS.

Development of a Data Mining Application for Agriculture Based on Bayesian Networks

Data mining is a process by which the data can be analyzed so as to generate useful knowledge. It aims to use existing data to invent new facts and to uncover new relationships previously unknown even to experts. Bayesian network is a powerful tool for dealing with uncertainties, and has a widespread use in the area of data mining. In this paper, we focus on development of a data mining application for agriculture based on Bayesian networks. Let features (or objects) as variables or the nodes in Bayesian network, let directed edges present the relationships between features, and the relevancy intensity can be regarded as confidence between the variables. Accordingly, it can find the relationships in the agricultural data by learning a Bayesian network. After defining the domain variables and data preparation, we construct a model for agricultural application based on Bayesian network learning method. The experimental results indicate that the proposed method is feasible and efficient, and it is a promising approach for data mining in agricultural data.

An approach based on Decision Tree to agricultural land grading

Land resources assessment is the premise and basis for the sustainable utilization of land resources. As a classification technology, Decision Tree has already been applied wildly in the area of information classification. This paper firstly introduces the fundamental theory and characteristics of Decision tree as well as its learning process. Then Iterative Dichotomiser 3 (ID3) algorithm and Decision tree pruning algorithm are combined to construct the Decision tree for agricultural land grading with a good result. The sampling and testing result shows that the accuracy in this case reaches as high as 86%. It can be easily concluded that Decision tree is an effective way for the agriculture land grading.

Rough set for quantitative analysis of characteristic information in metallogenic prediction

Purpose – The purpose of this paper is to extract the characterized mineralization information from large numbers of data obtained from geologic exploration based on rough set; analyze the inherent relation between mineral information genes and metallogenic probability, and offer the scientific basis for target prediction.Design/methodology/approach – Mineral information includes all kinds of relative metallogenic information. In order to extract comprehensive metallogenic prediction information, it is necessary to filter initial observation information to emphasize the factors that are most advantageous to metallogenic prognosis. Rough set can delete irrespective or unimportant attributes on the premises of no information missing and no classification ability changing, without supplementary information or prior knowledge, which has important theoretic and practical value for metallogenic prognosis.Findings – The association and importance of geological information referring to prospecting are found out t...

Using landscape indicators and Analytic Hierarchy Process (AHP) to determine the optimum spatial scale of urban land use patterns in Wuhan, China

Quantifying land use patterns and functions is critical for modeling urban ecological processes, and an emerging challenge is to apply models at multiple spatial scales. Methods of determining the optimum scale of land use patterns are commonly considered using landscape metrics. Landscape metrics are quantitative indicators for analyzing landscape heterogeneity at the landscape level. In this study, due to their widespread use in urban landscape analyses and well-documented effectiveness in quantifying landscape patterns, landscape metrics that represent dominance, shape, fragmentation and connectivity were selected. Five metrics include Patch Density, Contagion, Landscape Shape Index, Aggregation Index and Connectivity. Despite a wide application of landscape metrics for land use studies, the majority mainly focuses on the qualitative analysis of the characteristics of landscape metrics. The previous models are limited in exploring the optimum scale of land use patterns for their lack of quantitation. Therefore, taking the City of Wuhan as an example, the land use unit was treated as a patch, and the landscape pattern metrics at different spatial scales were calculated and compared so as to find the optimum one. Furthermore, a mathematical model of landscape metrics was proposed to quantify the scale effect of urban land use patterns, generating a complementary tool to select the optimum scale. In addition, Analytic Hierarchy Process (AHP) was introduced to determine the respective weights of the chosen landscape metrics in this model. Fractal dimension was ultimately applied to verify the chosen optimum scale of our study region. The results indicated that 60xa0m is confirmed to be the optimum scale for capturing the spatial variability of land use patterns in this study area.

A fractal and entropy-based model for selecting the optimum spatial scale of soil erosion

Soil erosion is an important issue of global climate change, which directly relates to the ecological environment quality and social sustainable development. Scale dependence is an intrinsic property of geographical phenomena and processes; therefore, it is scientifically significant to select an appropriate research scale for studying the driving mechanism and the ecological environment effect of soil erosion. The optimum scale selection model based on fractal and entropy theory was built in this paper. The model firstly used the entropy index from gray level co-occurrence matrix to evaluate the data redundancy, and adopted the similarity of Area-Weighed Mean Patch Fractal Dimension (AWMFD) to assess the expression ability of geographical intrinsic features. Then the correlation between each index and scale was computed, and finally the ratio of the correlation to their summation was taken as weight to calculate the weighted summation. In this paper, Danjiangkou reservoir area was taken as the case study. The results of the experiment show that the optimum scale for studying soil erosion in Danjiangkou reservoir area is 90xa0m, which means under this scale the optimum balance between data redundancy and the expression ability of intrinsic features is approached. Scalograms of landscape metrics were used to verify the rationality and feasibility of the optimum scale selection model.

Study on spatial knowledge representation and reasoning based on Bayesian networks

Spatial information plays an essential role on the progress of science and technology, and has a profound impact on economic growth and society progress in the twenty-first century. Spatial knowledge representation and reasoning are very important for us to utilize spatial information. In this paper, a review is presented on spatial knowledge representation and reasoning. And then we propose a method of spatial knowledge representation and reasoning based on Bayesian networks. We focused on how to represent spatial relationship, spatial objects and spatial features by using Bayesian networks. Let spatial features (or spatial objects, spatial relationships) as variables or the nodes in Bayesian network, let directed edges present the relationships between spatial features, and the relevancy intensity can be regarded as confidence between the variables (the same as probability parameter in Bayesian network). Accordingly, the problem of spatial knowledge representation will be changed to the problem of learning Bayesian networks. The experimental results are given to verify the practical feasibility of the proposed methodology. Eventually, we conclude with a summary and a statement of future work.

Development of flood routing simulation system of digital Qingjiang based on integrated spatial information technology

Based on digital technology, flood routing simulation system development is an important component of digital catchment. Taking QingJiang catchment as a pilot case, in-depth analysis on informatization of Qingjiang catchment management being the basis, aiming at catchment datas multi-source, - dimension, -element, -subject, -layer and -class feature, the study brings the design thought and method of subject-point-source database (SPSD) to design system structure in order to realize the unified management of catchments data in great quantity. Using the thought of integrated spatial information technology for reference, integrating hierarchical structure development model of digital catchment is established. The model is general framework of the flood routing simulation system analysis, design and realization. In order to satisfy the demands of flood routing three-dimensional simulation system, the object-oriented spatial data model are designed. We can analyze space-time self-adapting relation between flood routing and catchments topography, express grid data of terrain by using non-directed graph, apply breadth first search arithmetic, set up search method for the purpose of dynamically searching stream channel on the basis of simulated three-dimensional terrain. The system prototype is therefore realized. Simulation results have demonstrated that the proposed approach is feasible and effective in the application.