重视继续教育中科技人员创造能力的培养

本文结合杭州干部培训中心继续教育的实践,探讨了继续教育中开发科技人员创造力的途径。指出科技人员的继续教育要正确处理好传授知识和培养科研能力的关系,着眼于能力的培养。培训内容要注重先进性和实用性,注意学科间的交叉渗透。培训方法要采用有利于激发学员创造性思维的形式,在继续教育中建立起新型的创造教育的观念。     

现代化地震台网建设与观测技术人员教育培训

本文根据我国现代化地震台网建设的规划和发展战略,较系统地分析了观测技术队伍的素质与现代化台网建设不相适应的问题;论述了继续教育在现代化台网建设中的重要作用;进而提出了观测技术人员继续教育培训的内容和方法,为加强和推动观测技术人员的教育培训提供科学依据。     

地震系统计算机技术人才的继续教育

本文作者列举了大量的数据,分析了国内计算机技术人才的现状和国外的有关情况,以此来说明地震系统计算技术人才继续教育的紧迫性。指出今后地震系统计算技术人才的两个培训方向。即:计算机技术专门人才和应用人才的培训。同时,就培训中心本身提出了改善教学设备,安排合理的培训时间,编制合适的教材和更进一步完善考试制度等方面存在的问题和改进措施的意见。     

试谈继续工程教育中两个值得重视的问题

本文通过对管理工作重要性和实现管理现代化的必要性和迫切性的讨论,以及对管理队伍现状的分析,提出了加强各类各级管理人员的培训是继续教育中一项不容忽视的任务。并指出培训重点应放在决策人员上;本文提出的第二个问题是关于开发科技人员科学创造力的问题,这个问题是从科技人员所作从事的工作的特殊性、创造力对科学发展的重要性及我国大部分科技人员所存在的弱点等三个方面加以探讨的。     

浅论职工教育——安微省地震局职工教育之我见

本文从安徽省地震局职工教育情况出发,论述了职工教育的重要性和迫切性。认为安徽省地震局职工教育的主体是对业务人员的培训,而领导干部与科技管理人员的培训是职工教育的急需,并在现有工作基础上提出了今后工作的基本设想:把岗位培训作为改革职工教育的方向。把培训学科带头人作为科技干部继续教育的重点。制定职工教育法规是搞好职工教育的保障。     

基于四川震情的市县地震应急管理培训工作体系研究

通过对四川省发生的汶川、芦山等重大地震中市县政府及相关单位在地震应急管理培训工作方面的现状和需求调研进行分析,总结了市县政府在地震应急管理培训工作方面存在的主要问题,并结合当前地震工作中的管理培训需求,构建了包含组织体系(实施单位、合作单位)、培训体系(培训对象、培训内容、培训课程设置、培训模式、培训评估等)和保障体系(软硬件保障)的适合市县政府的地震应急管理培训体系,具有一定的实用性、参考性和现实指导性。     

关于走上领导岗位的专业技术人员的继续教育问题

1983年体制改革以来,大批专业技术人员走上各级领导岗位,他们的工作性质、内容、方法都有一个较大的转变。为了适应这种转变,必须重新学习,对他们进行继续教育,使他们尽快熟悉和掌握第二专业,成为“软专家”。 对于走上领导岗位的专业技术人员来说,所谓第二专业,主要是指进行马列主义基本理论,现代领导科学与现代管理科学的培训,完善他们的知识结构。 培训方法可以是短期集中学习,研究讨论,也可以函授,自学。同时,还要继续拓宽与深化他们原来的专业知识。从而提高他们的决策能力,领导与管理水平,提高整个系统的效能。     

市场经济条件下地震系统的继续教育管理

根据地震系统继续教育面临的形势和任务,对建立市场经济条件下的继续教育管理体系以及运行机制进行了探讨,并根据新形势,提出了促进地震系统继续教育工作发展的几点建议。     

地震科技人员继续教育问题初探

本文从三个方面对地震科技人员继续教育问题进行了初步的探讨,指出继续教育本身的特点、科学技术发展和科技工作协作性特点是地震科技人员培养方向的三个主要的决定因素;阐述了继续教育的重点对象和主要任务,强调了加强科技管理人员继续教育的重要性以及在教育程序上应当采取“自上而下”的办法;提出要使地震系统的继续教育具有自己的特色,就必须切实抓好传授知识与发展智力相结合,教会科技人员找到适合于自己的学习方法,积极传递各种信息三个环节的工作;在总结归纳的基础上,探讨了地震系统的六个继续教育的途径和方法。     

继续教育与人事管理

本文从人事管理的角度论述了继续教育是人事管理工作的重要内容,进而探讨继续教育对于开发人力资源,促进科学技术转化为生产力的重要作用,以及为适应经济建设的新形势要树立的几个新观念,针对当前地震工作的主要任务,提出了加强继续教育工作的重点.     

基于模糊理论的高铁连续梁桥地震风险评估

以某高速铁路线上一座连续梁桥为例,运用模糊综合评判法,结合基于位移的支座损伤分析和截面曲率的桥墩损伤分析,以全概率理论地震损失模型为基础,提出了基于模糊理论的桥梁系统地震经济风险评估方法。结果表明:综合考虑桥梁系统的模糊地震经济风险分析方法能更全面地计算出连续梁桥在地震作用下的经济损失,仅以桥墩构件代表全桥所得地震经济损失误差较大。基于模糊理论的年预期损失风险框架方法通过结构抗震性能的概率特征可对高速铁路连续梁桥的地震直接经济风险进行全面评估,为该类桥梁的抗震设计、维修加固和灾后重建等方案做出合理评价。     

Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm

This paper introduces the dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi–Sugeno (TS) type fuzzy inference system with on-line and local learning algorithm for complex dynamic hydrological modeling tasks. Our DNFLMS is aimed to implement a fast training speed with the capability of on-line simulation where model adaptation occurs at the arrival of each new item of hydrological data. The DNFLMS applies an on-line, one-pass, training procedure to create and update fuzzy local models dynamically. The extended Kalman filtering algorithm is then implemented to optimize the parameters of the consequence part of each fuzzy model during the training phase. Local generalization in the DNFLMS is employed to optimize the parameters of each fuzzy model separately, region-by-region, using subsets of training data rather than all training data.     

Fuzzy neural network models for liquefaction prediction

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined.     

Fuzzy Logic Determination of Lithologies from Well Log Data: Application to the KTB Project Data set (Germany)

Fuzzy logic has been used for lithology prediction with remarkable success. Several techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for lithofacies determination. In this paper, a fuzzy inference methodology has been implemented as a MATLAB routine and applied for the first time to well log data from the German Continental Deep Drilling Program (KTB). The training of the fuzzy inference system is based on the analysis of the multi-class Matthews correlation coefficient computed for the classification matrix. For this particular data set, we have found that the best suited membership function type is the piecewise linear interpolation of the normalized histograms; that the best combination operator for obtaining the final lithology degrees of membership is the fuzzy gamma operator; and that all the available properties are relevant in the classification process. Results show that this fuzzy logic-based method is suited for rapidly and reasonably suggesting a lithology column from well log data, neatly identifying the main units and in some cases refining the classification, which can lead to a better interpretation. We have tested the trained system with synthetic data generated from property value distributions of the training data set to find that the differences in data distributions between both wells are significant enough to misdirect the inference process. However, a cross-validation analysis has revealed that, even with differences between data distributions and missing lithologies in the training data set, this fuzzy logic inference system is able to output a coherent classification.     

Cluster-based fuzzy models for groundwater flow in the unsaturated zone

In this paper fuzzy models are used as an alternative to describe groundwater flow in the unsaturated zone. The core of these models consists of a fuzzy rule-based model of the Takagi–Sugeno type. Various fuzzy clustering algorithms are compared in the data-driven identification of these Takagi–Sugeno models. The performance of the resulting fuzzy models is evaluated on the training surface on which they were identified, and on time series measurements of water content values obtained through an experiment carried out by the non-vegetated terrain (NVT) workgroup of the European Microwave Signature Laboratory (EMSL) (see [Mancini M, Hoeben R, Troch PA. Multifrequency radar observations of bare surface soil moisture content: a laboratory experiment. Water Resour Res 1999;35(6):1827–38] and [Hoeben R, Troch PA. Assimilation of active microwave observation data for soil moisture profile estimation. Water Resour Res 2000;36(10):2805–19]). Despite higher errors at the borders of high water content values in the training surface, good results are obtained on the simulation of the time series.     

River suspended sediment modelling using a fuzzy logic approach

Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating‐curve models. Benchmarking was based on a 5‐year period of continuous streamflow and sediment concentration data of Quebrada Blanca Station operated by the United States Geological Survey. The benchmark results showed that the fuzzy model was able to produce much better results than rating‐curve models. The fuzzy model proposed in the study is site specific and does not simulate the hysteresis effects. Copyright © 2006 John Wiley & Sons, Ltd.     

RESPONSE ANALYSIS FOR FUZZY STOCHASTIC DYNAMICAL SYSTEMS WITH MULTIPLE DEGREES OF FREEDOM

Most real-life structural/mechanical systems have complex geometrical and material properties and operate under complex fuzzy environmental conditions. These systems are certainly subjected to fuzzy random excitations induced by the environment. For an analytical treatment of such a system subjected to fuzzy random excitations, it becomes necessary to establish the general theory of dynamic response of a system to fuzzy random excitations. In this paper, we extend the work published in Reference [1], and discuss the case of Multi-Degree-of-Freedom (MDF) fuzzy stochastic dynamical systems. The theory of the response, fuzzy mean response and fuzzy covariance response of multi-degree-of-freedom system to fuzzy random excitations in the time domain and frequency domain is put forward. Two cases to determine the fuzzy response statistics of the fuzzy stochastic dynamical system with multiple degrees of freedom are discussed. Two examples are considered in order to demonstrate the rationality and validity of the theory. © 1997 by John Wiley & Sons, Ltd.     

Daily suspended sediment modelling using a fuzzy differential evolution approach / Modélisation journalière des matières en suspension par une approche d’évolution différentielle floue

Abstract

Abstract Correct estimation of the sediment volume carried by a river is important with respect to pollution, channel navigability, reservoir filling, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. However, conventional sediment rating curves are not able to provide sufficiently accurate results. In this study, models incorporating fuzzy logic are developed as a superior alternative to the sediment rating curve technique for determining the daily suspended sediment concentration for a given river cross-section. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating curve models. Benchmarking was based on a five-year period of continuous streamflow and sediment concentration data from the Quebrada Blanca Station operated by the United States Geological Survey (USGS). Nine different fuzzy models were developed to estimate sediment concentration from streamflow. Each fuzzy model has a different number of membership functions. The parameters of the membership functions were found using a differential evolution algorithm. The benchmark results showed that the fuzzy models were able to produce much better results than rating curve models for the same data inputs.     

Modeling the initiation of sediment motion under a wide range of flow conditions using a Geno-Mamdani Fuzzy Inference System method

The current study introduces a novel approach to estimate the incipient motion of sediments under a wide range of flow regimes by developing a fuzzy model with a fuzzy-band that refers to a transition from weak motion to general motion of sediment. The partial sediment entrainment is defined by fuzzy sets considering the uncertainty related to the individual ratio of inertia to viscous forces which is the definition of shear Reynolds number. In the current study, the Mamdani Fuzzy Inference System (Mamdani FIS) is used to develop a comprehensive fuzzy model of the incipient motion of sediment. The Mamdani FIS has a shortcoming regarding the training of the fuzzy model. To estimate the dimensionless shear stress, a new method is developed by combining a genetic algorithm with the fuzzy approach which is named the Geno-Mamdani Fuzzy Inference System (GMFIS) method. The performance of the GMFIS model is evaluated using experimental data by considering root mean square error (RMSE), Nash-Sutcliffe coefficient of efficiency (CE), degree of robustness (D_r), and concordance coefficient (CC) as evaluation criteria. The GMFIS model performed very well based on the RMSE, CE, D_r, and CC values and satisfactorily represented the three types of incipient motion. Finally, a new range of fuzzy, dimensionless, critical shear stress values is established in all flow conditions from weak to general sediment entrainment.