Hydrological management strategies for the control of algal blooms in regulated lowland rivers

Reservoirs of lowland floodplain rivers with eutrophic backgrounds cause variations in the hydrological and hydraulic conditions of estuaries and low-dam reservoir areas, which can promote planktonic algae to proliferate and algal bloom outbreaks. Understanding the ecological effects of variations in hydrological and hydraulic processes in lowland rivers is important for algal bloom control. In this study, the middle and lower reaches of the Han River, China, a typical regulated lowland river with a eutrophic background, are selected. Based on the effect of hydrological and hydraulic variability on algal blooms, a hydrological management strategy for river algal bloom control is proposed. The results showed that (a) differences in river morphology and background nutrient levels cause significant differences in the critical threshold flow velocities for algal bloom outbreaks between natural river and low-dam reservoir sections; there is no uniform threshold flow velocity for algal bloom control. (b) There are significant differences in the river hydrological/hydraulic conditions between years with and without algal blooms. The average river flow, water level and velocity in years with algal blooms are significantly lower than those in years without algal blooms. (c) For different river sections where algal blooms occur and to meet the threshold flow velocities, the joint operation of cascade reservoirs and diversion projects is an effective method to prevent and control algal blooms in regulated lowland rivers. This study is expected to deepen our understanding of the ecological significance of special hydrological processes and guide algal bloom management in regulated lowland rivers.     

Study of the flood control scheduling scheme for the Three Gorges Reservoir in a catastrophic flood

The Three Gorges Project is the world's largest water conservancy project. According to the design standards for the 1,000‐year flood, flood diversion areas in the Jingjiang reach of the Yangtze River must be utilized to ensure the safety of the Jingjiang area and the city of Wuhan. However, once these areas are used, the economic and life loss in these areas may be very great. Therefore, it is vital to reduce this loss by developing a scheme that reduces the use of the flood diversion areas through flood regulation by the Three Gorges Reservoir (TGR), under the premise of ensuring the safety of the Three Gorges Dam. For a 1,000‐year flood on the basis of a highly destructive flood in 1954, this paper evaluates scheduling schemes in which flood diversion areas are or are not used. The schemes are simulated based on 2.5‐m resolution reservoir topography and an optimized model of dynamic capacity flood regulation. The simulation results show the following. (a) In accord with the normal flood‐control regulation discharge, the maximum water level above the dam should be not more than 175 m, which ensures the safety of the dam and reservoir area. However, it is necessary to utilize the flood diversion areas within the Jingjiang area, and flood discharge can reach 2.81 billion m³. (b) In the case of relying on the TGR to impound floodwaters independently rather than using the flood diversion areas, the maximum water level above the dam reaches 177.35 m, which is less than the flood check level of 180.4 m to ensure the safety of the Three Gorges Dam. The average increase of the TGR water level in the Chongqing area is not more than 0.11 m, which indicates no significant effect on the upstream reservoir area. Comparing the various scheduling schemes, when the flood diversion areas are not used, it is believed that the TGR can execute safe flood control for a 1,000‐year flood, thereby greatly reducing flood damage.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

交通流量分析与实时最优控制

通过将车流量的增大或减小转化为路长权重的变化。将交通流量的动态问题转化为静态问题，用解决最短路问题的Dijkstra方法，给出交通流量实时最优控制的可行性模型及其有效算法。     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

Phasing of segmented mirrors: a new algorithm for piston detection

The point spread function of a segmented-mirror telescope is severely affected by segment misalignment, which can nullify the performance of adaptive optics systems. The piston and tilt of each segment must be precisely adjusted in relation to the other segments. Furthermore, the direct detection of the alignment error with natural stars would be desirable in order to monitor the errors during astronomical observation.
We have studied the lost information of the piston error caused by the presence of atmospheric turbulence in the measurements of curvature, and present a new algorithm for obtaining the local piston using the curvature sensor. A phase-wrapping effect is shown as responsible for the loss of curvature information and so the piston errors can no longer adequately be mapped; this happens not only in the presence of atmospheric turbulence, but also in its absence.
Good results are obtained using a new iterative method for obtaining the local piston error map. In the presence of atmospheric perturbation, the turbulent phase information obtained from a Shack–Hartmann sensor is introduced in our new iterative method. We propose a hybrid sensor composed of a curvature sensor and a Shack–Hartmann sensor, in order to complete all the information for the phasing. This design takes a short computation time and could be used in real time inside an adaptive optics system, where tilt and piston errors must be corrected.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

LAMOST总控系统程序设计

国家重大科学工程项目LAMOST总控系统的程序设计是一个庞大的软件工程．其总控系统下共包括8个既相互独立又互有关联的子系统．如何通过良好的软件设计方法来处理好这8个子系统之间的关系使其协调运转，以及如何确保软件开发的可维护性、可持续性是研究重点．     

基于专家系统决策的多模式控制及其应用

将专家系统技术与常规控制、模糊控制、超前控制、仿人智能操作控制以及计算机控制技术相结合,设计了一种新的基于专家系统决策的多模式控制(MCDBES)。经试验和实际运行结果表明,MCDBES系统对未知数学模型的被控对象,具有良好的控制性能。本文提出的MCDBES已成功地应用于燃油陶瓷窑炉计算机控制系统。