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101.
102.
提高交通安全——实现智能汽车的研究 总被引:1,自引:1,他引:1
主要介绍了通过智能汽车的研究 ,提高城市交通安全的必要性 ;综述了发达国家关于智能汽车研究的现状与趋势 ,并通过国外智能汽车发展的趋势 ;进一步论证智能汽车的研究是汽车工业发展的方向 ,也是提高交通安全的重要手段之一。此外 ,论文提出利用模式识别技术的智能汽车的结构框架 ;指出神经网络方法应用与模式识别技术结合的优势。最后 ,论文在阐述我国需要发展智能汽车的同时 ,提出应结合中国国情相应研究相关理论 ,为今后的实施奠定基础的近期目标 ,并提出展望 相似文献
103.
R. M. Patel S. O. Prasher P. K. God R. Bassi 《Journal of the American Water Resources Association》2002,38(1):91-100
ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site. 相似文献
104.
Shie‐Yui Liong Chandrasekaran Sivapragasam 《Journal of the American Water Resources Association》2002,38(1):173-186
ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool. 相似文献
105.
本文简要介绍地理信息系统GIS的发展概况、主要功能以及在国内外的应用现状.对现有的GIS软件技术加以改进,引进人工神经元网络和模糊综合评判技术,发展了一种智能型的GIS,在我国若干城市的抗震设防区划工作中应用,取得良好的效果。 相似文献
106.
W. A. White L. F. Tischler T. A. Austin 《Journal of the American Water Resources Association》1972,8(3):483-494
ABSTRACT A methodology for predicting the spatial and temporal levels of conservative water quality constituents within a multibasin water resource system is presented. Dissolved solids, sulfates, and chlorides are the constituents used during this investigation; however, any other conservative ion or mineral can be incorporated into the simulation model. The methodology is tested on the proposed Texas Water System. The water quality model, QNET-I, utilizes monthly canal and river flows and reservoir storage levels calculated by the Texas Water Development Board's systems simulation model. Discharge-concentration relationships are developed for each source of water in the system, including significant waste-water discharges. Reservoirs in the system are assumed to be completely mixed with respect to conservative constituents. A mass balance analysis is performed for each node and each month during the simulation period. The output from the water quality simulation is a table of the concentrations of the conservative water quality constituents at each demand point in the system and in each reservoir and canal for every month the system is in operation. The desired quality of the water at the demand locations is used to determine the economic utility of transporting and mixing water from various sources. 相似文献
107.
Y Tachikawa M. Shiiba T Takasao 《Journal of the American Water Resources Association》1994,30(1):9-17
ABSTRACT: To make a distributed rainfall-runoff model, it is very important to build a model of topographic surface of a basin which takes account of the direction of water flow. In this paper, a geographic information system in hydrologic modeling, the BGIS (Basin Geomorphic Information Systems) are presented for modeling a river basin using a TIN-DEM (Triangulated Irregular Network - Digital Elevation Model) data structure. The BGIS have two core systems, which are the TIN-DEM generating system and the topographic analysis system. In the TIN-DEM generating system, landscapes are modeled as a set of contiguous non-overlapping terangular facets whose vertices are made up of points on a regular grid DEM and on river segments. These triangular facets are subdivided, if needed, so that each of them has only one side through which water flows out. The TIN-DEM generating system is made up of four modules, (1) a module for generating triangles from a grid DEM, (2) a module for getting rid of pits, (3) a module for joining discontinuous valley segments to a channel network, (4) a module for subdividing triangular facets. In the topographic analysis system, using datasets processed with the TIN-DEM generating system, a watershed source area for any segments in a stream network are delineated automatically, and topographic attributes of slopes, aspects, flow path lengths and upslope contributing areas are computed. 相似文献
108.
煤层底板采动导水破坏深度计算的神经网络方法 总被引:4,自引:1,他引:3
在综合分析影响煤层底板采动导水破坏深度因素的基础上 ,应用人工神经网络方法 ,建立了底板破坏深度的计算模型。该模型利用现场观测资料作为学习训练样本和测试样本 ,对模型的测算结果、理论计算值和实测值进行了对比分析。结果表明 :用神经网络方法计算底板破坏深度考虑的因素更加全面 ,结果更接近于实际。笔者研究的计算模型和测算方法 ,为承压水上安全采煤决策提供了科学依据。 相似文献
109.
Yoshimitsu?UemuraEmail author Kouichi?Baba Hiroyuki?Ohe Yoshihiro?Ohzuno Yasuo?Hatate 《Journal of Material Cycles and Waste Management》2003,5(2):94-97
A two-stage process for the chemical recycling of plastics is proposed. In this process, which consists of two reactors, plastics are converted into hydrogen and carbon. In the first reactor, plastic chips are thermally decomposed into hydrocarbons. In the second reactor, the hydrocarbons formed in the first reactor are catalytically decomposed into carbon and hydrogen. In this study, in order to obtain basic data for the second reactor, propene was catalytically decomposed in a laboratory-scale spouted-bed reactor (600mm high, 21.6mm internal diameter, made of SUS304). The effect of the type of spouting medium used on the decomposition behavior of propene was investigated using four types of spouting medium (nickel-plated -alumina, palladium-plated -alumina, nickel-impregnated -alumina, and -alumina). The nickel-impregnated -alumina gave the best propene conversion and hydrogen yield. 相似文献
110.
人工神经网络方法在资源与环境预测方面的应用 总被引:15,自引:1,他引:14
用人工神经网络方法对不同水域、不同环境因子之间非线性和不确定性的复杂关系进行学习训练并预测检验。结果表明:人工神经网络方法在模拟和预测方面 优于传统的统计回归模型,在资源与环境方面的应用是可行的。具有较强的模拟预测能力。与传统的回归模型相比,人工神经网络方法不要求监测数据具有很强的规律性,就可用后的网络模型对其进行预报,燕且预测相对误差均比回归模型预测相对误差要小,具有一定的实用性。两个实例的应用 相似文献