Occlusion Culling Algorithm Using Prefetching and Adaptive Level of Detail Technique

A novel approach that integrates occlusion culling within the view-dependent rendering framework is proposed. The algorithm uses the prioritized-layered projection（PLP） algorithm to occlude those obscured objects, and uses an approximate visibility technique to accurately and efficiently determine which objects will be visible in the coming future and prefetch those objects from disk before they are rendered, view-dependent rendering technique provides the ability to change level of detail over the surface seamlessly and smoothly in real-time according to cell solidity value.

Terrain Rendering LOD Algorithm Based on Improved Restrictive Quadtree Segmentation and Variation Coefficient of Elevation

Aiming to deal with the difficult issues of terrain data model simplification and crack disposal,the paper proposed an improved level of detail(LOD)terrain rendering algorithm,in which a variation coefficient of elevation is introduced to express the undulation of topography.Then the coefficient is used to construct a node evaluation function in the terrain data model simplification step.Furthermore,an edge reduction strategy is combined with the improved restrictive quadtree segmentation to handle the crack problem.The experiment results demonstrated that the proposed method can reduce the amount of rendering triangles and enhance the rendering speed on the premise of ensuring the rendering effect compared with a traditional LOD algorithm.

Research on confirmation of basic technological parameters of tension levellers based on neural network and genetic algorithm

Confirmation of basic technological parameters of tension levellers is the most important factor of leveling strip. Up to now, most factories have used experts’ experience to decide these parameters, without any established rule to follow. For better quality of strip, a valid method is needed to decide technological parameters precisely and reasonably. In this paper, a method is used based on neural network and genetic algorithm. Neural network has a good ability to extract rules from work process of tension levellers. Then using neural network, which has learned from a lot of working samples, to be the evaluation of fitness, genetic algorithm could easily find the best or better technological parameters. At the end of this paper, examinations are given to show the effect of this method.

Algorithm of Dynamic Operation Process of Hydraulic Automatically Operated Canals with Constant-Downstream Level Gates

On the basis of analysis the governing process of downstream water level gates AVIO and AVIS, a mathematical model for simulation of dynamic operation process of hydraulically automated irrigation canals instalIed with AVIO and AVIS gates is presented, the main point of this rnathematical model is firstly applying a set of unsteady flow equations （St. Venant equations here） and treating the condition of gate movement as its dynamic boundary, and then deeoupling this interaction of gate movement with the change of canal flow. In this process, it is necessary to give the gateg open-loop transfer function whose input is water level deviation and output is gate discharge. The result of this simulation for a practical reach has shown it has satisfactory accuracy.

Graph Clustering Algorithm for RT Level ALU Technology Mapping

Register transfer level mapping (RTLM) algorithm for technology mapping at RT level is presented,which supports current design methodologies using high level design and design reuse.The mapping rules implement a source ALU using target ALU.The source ALUs and the target ALUs are all represented by the general ALUs and the mapping rules are applied in the algorithm.The mapping rules are described in a table fashion.The graph clustering algorithm is a branch and bound algorithm based on the graph formulation of the mapping algorithm.The mapping algorithm suits well mapping of regularly structured data path.Comparisons are made between the experimental results generated by 1 greedy algorithm and graphclustering algorithm,showing the feasibility of presented algorithm.

基于能量函数基元提取的建筑物LOD2模型自动生成方法

近年来,随着实景三维中国和数字中国的快速发展,建筑物三维模型重建已成为智慧城市建设的重要环节。本文提出了一种全自动建筑物LOD2模型生成框架,旨在解决城市级实景三维场景中建筑物LOD2模型生成的难题。为了自动提取大场景建筑物对象,首先利用场景正射影像,通过图像分割获取建筑物的边界信息;然后使用改进的增强型平面区域分割方法提取高质量的分割平面,并利用混合线性模型对分割结果进行优化,能够更好地处理城市建筑物的局部破损和重建错误等问题。试验结果表明,本文算法生成的建筑物LOD2模型质量和效率更高,能够应对城市建筑模型的复杂情况,得到更稳健的重建结果。

界面捕捉中耦合Level-set与VOF算法

针对传统的level-set方法在处理界面问题时守恒性差的缺点,提出了一种耦合level-set方法和VOF方法的算法(CLSVOF)。不仅保持了level-set界面光滑性好的特点,还结合了VOF高守恒性的优点。Level-set方程的求解采用五阶WENO格式,而VOF的体积分数输运方程采用Gueyffier的界面重构方法求解。三维数值计算结果表明这种耦合算法对level-set的守恒性有明显的改善。

Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm

Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.

Combined Novel Gate Level Model and Critical Primary Input Sharing for Genetic Algorithm Based Maximum Power Supply Noise Estimation

A gate level maximum power supply noise （PSN） model is defined that captures both IR drop and di/dt noise effects. Experimental results show that this model improves PSN estimation by 5.3% on average and reduces computation time by 10.7% compared with previous methods. Furthermore,a primary input critical factor model that captures the extent of primary inputs＇ PSN contribution is formulated. Based on these models,a novel niche genetic algorithm is proposed to estimate PSN more effectively. Compared with general genetic algorithms, this novel method can achieve up to 19.0% improvement on PSN estimation with a much higher convergence speed.

Optimization of solar thermal power station LCOE based on NSGA-Ⅱ algorithm

In view of the high cost of solar thermal power generation in China,it is difficult to realize large-scale production in engineering and industrialization.Non-dominated sorting genetic algorithm II(NSGA-II)is applied to optimize the levelling cost of energy(LCOE)of the solar thermal power generation system in this paper.Firstly,the capacity and generation cost of the solar thermal power generation system are modeled according to the data of several sets of solar thermal power stations which have been put into production abroad.Secondly,the NSGA-II genetic algorithm and particle swarm algorithm are applied to the optimization of the solar thermal power station LCOE respectively.Finally,for the linear Fresnel solar thermal power system,the simulation experiments are conducted to analyze the effects of different solar energy generation capacities,different heat transfer mediums and loan interest rates on the generation price.The results show that due to the existence of scale effect,the greater the capacity of the power station,the lower the cost of leveling and electricity,and the influence of the types of heat storage medium and the loan on the cost of leveling electricity are relatively high.

Groundwater level prediction based on hybrid hierarchy genetic algorithm and RBF neural network

As the traditional non-linear systems generally based on gradient descent optimization method have some shortage in the field of groundwater level prediction, the paper, according to structure, algorithm and shortcoming of the conventional radial basis function neural network （RBF NN）, presented a new improved genetic algorithm （GA）： hybrid hierarchy genetic algorithm （HHGA）. In training RBF NN, the algorithm can automatically determine the structure and parameters of RBF based on the given sample data. Compared with the traditional groundwater level prediction model based on back propagation （BP） or RBF NN, the new prediction model based on HHGA and RBF NN can greatly increase the convergence speed and precision.

A Method for Assessing Customer Harmonic Emission Level Based on the Iterative Algorithm for Least Square Estimation

With the power system harmonic pollution problems becoming more and more serious, how to distinguish the harmonic responsibility accurately and solve the grid harmonics simply and effectively has become the main development direction in harmonic control subjects. This paper, based on linear regression analysis of basic equation and improvement equation, deduced the least squares estimation (LSE) iterative algorithm and obtained the real-time estimates of regression coefficients, and then calculated the level of the harmonic impedance and emission estimates in real time. This paper used power system simulation software Matlab/Simulink as analysis tool and analyzed the user side of the harmonic amplitude and phase fluctuations PCC (point of common coupling) at the harmonic emission level, thus the research has a certain theoretical significance. The development of this algorithm combined with the instrument can be used in practical engineering.

Design of nuclear radiation level gauge based on normalized LMS filtering correction

In the measurement of liquid level in industrial site environment,noise interference can affect the measurement accuracy.In order to improve the measurement accuracy of liquid level in the viscous state,a nuclear radiation level measurement system based on the least mean square(LMS)filtering correction method is designed.The system uses STM32F103 as the control core and adopts HART bus HT1200M chip for remote signal transmission and reception.The adaptive LMS algorithm can be used for more accurate filtering,calculating iterative weight vector,updating weighted coefficient,effectively removing system measurement noise and improving the measurement accuracy.The results show that the nuclear radiation level gauge based on normalized LMS can correct the measurement system accuracy in adaptive rules,improve the measurement accuracy to meet the requirements of industrial field environment for liquid level measurement and enhance the industrial automation control degree.

NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.

The Research of the Equation Model on the System-level Fault Diagnosis

In this paper we propose an equation model of system-level fault diagnoses, and construct corresponding theory and algorithms. People can turn any PMC model on ex-test into an equivalent equation (or a system of equations), and find all consistent fault patterns based on the equation model. We can also find all fault patterns, in which the fault node numbers are less than or equal to t without supposing t-diagnosable. It is not impossible for all graphic models.

Parameterization and Application of Storm Surge/Tide Modeling Using a Genetic Algorithm for Typhoon Periods

A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge/Tide Operational Model （STORM） to improve sea level predictions.The genetic algorithm was applied to nine typhoons that affected the Korean Peninsula during 2005-2007.The following model parameters were used：the bottom drag coefficient,the background horizontal diffusivity,Smagorinski＇s horizontal viscosity,and the sea level pressure scaling.Generally,the simulation results using the optimized,mean,and median parameter values improved sea level predictions.The four estimated parameters improved the sea level prediction by 76% and 54% in the bias and root mean square error for Typhoon Kalmaegi （0807） in 2008,respectively.One-month simulations of February and August 2008 were also improved using the estimated parameters.This study demonstrates that parameter optimization on STORM can improve sea level prediction.

An effective multi-level algorithm based on ant colony optimization for graph bipartitioning

Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph is proposed. During its coarsening phase, an improved matching approach based on the global information of the graph core is developed with its guidance function. During the refinement phase, the vertex gain is exploited as ant＇s heuristic information and a positive feedback method based on pheromone trails is used to find the global approximate bipartitioning. It is implemented with American National Standards Institute （ANSI） C and compared to MeTiS. The experimental evaluation shows that it performs well and produces encouraging solutions on 18 different graphs benchmarks.

Region-time-length algorithm and its application to the study of intermediate-short term earthquake precursor in North China

The Region-Time-Length Algorithm (RTL algorithm) is introduced and improved in the paper. Compared with the original definition, the influence of rupture length on RTL function is emphasized and the weights of epicentral distance function, time function, and rupture length function are ensured to be equal. The retrospective examinations of RTL algorithm in North China have indicated that the anomalies obtained by the improved RTL algorithm show the short or intermediate-short term precursory features in most cases. There are two types of RTL anomalous patterns before the main shock. For the I-type, the variation pattern of the VRTL, numerical values of the VRTL(x, y, z, t) function, is complete and most of them have shown the changing pattern of rising from 0 turning dropping or dropping from 0 turning rising. For the II-type, the variation pattern of VRTL is not complete, which increases or decreases quickly from 0 and there is no evident turning, the main shock generally occurs in the short period around the peak VRTL. The rising of VRTL indicates an increase of seismic activity relative to the background level, which means the enhancement of seismic activity, while the dropping of VRTL indicates the decrease of seismic activity relative to the background level, which represents the seismic quiescence to a certain extent. According to statistical examination results of RTL algorithm in North China, the methods to distinguish the intermediate and short-term anomalies and to estimate the occurrence time of the coming main shock are given in the paper. For both I and II-type RTL anomalies, the R-value, i.e., the forecasting score, is about 0.6 and 0.3 for the 3 months forecasting period and about 0.7 and 0.4 for the 6 months forecasting period. The preliminary discussion is also made for the influences of characteristic time-span t0, characteristic distance r0, and threshold magnitude M0 on computation of VRTL, as well as some other significant problems in application.

基于ε-level蝙蝠算法的无人机三维航迹规划

针对复杂地形环境、存在各种威胁和约束的无人机航迹规划问题,提出了一种基于ε-level改进蝙蝠算法的航迹规划算法。根据无人机目标函数和约束条件,建立无人机三维航迹规划模型;其次,针对蝙蝠算法处理高维带约束问题存在的早熟现象,设计了自适应权重系数和迭代阈值以平衡算法的探索能力和开发能力,并结合ε-level比较策略提高算法对非凸非线性约束优化问题的处理能力;设计了转弯半径可变的Dubins曲线用来对航迹进行平滑处理并解决2个航迹点连线贯穿地形的问题。通过仿真实验,并与蝙蝠算法、粒子群优化算法、ε-level粒子群优化算法和ε-level差分进化算法相比较,所提算法在开发能力、稳定性和成功率等方面都表现出较优的性能。

Ridge regression energy levels calculation of neutral ytterbium(Z=70)

In view of the difficulty in calculating the atomic structure parameters of high-Z elements,the Hartree–Fock with relativistic corrections(HFR)theory in combination with the ridge regression(RR)algorithm rather than the Cowan code’s least squares fitting(LSF)method is proposed and applied.By analyzing the energy level structure parameters of the HFR theory and using the fitting experimental energy level extrapolation method,some excited state energy levels of the Yb I(Z=70)atom including the 4f open shell are calculated.The advantages of the ridge regression algorithm are demonstrated by comparing it with Cowan code’s LSF results.In addition,the results obtained by the new method are compared with the experimental results and other theoretical results to demonstrate the reliability and accuracy of our approach.