煤矿安全预评价的可变模糊识别方法

煤矿安全预评价涉及诸多不确定信息,用传统评价方法难以获得可靠的评价结果。为此,将可变模糊模式识别模型与方法引入煤矿安全领域,提出了煤矿安全预评价的可变模糊识别方法。可变模糊识别方法首先根据指标特征值与指标标准特征值矩阵确定样本的级别上下限,然后利用可变模糊模式识别模型对样本进行识别,得到样本在各级别下的相对隶属度,进而计算级别特征值,并以此为依据进行评价。将该方法应用于黄陵双龙煤矿,得出该矿井的安全等级。结果表明:可变模糊识别方法较文献中方法可更准确地给出安全性等级评价结果,为煤矿安全预评价提供一条新的途径。     

Measurement Diagnostics by Analysis of Last Digits

Though measurement instruments and methods have their natural precision limits, long term monitoring, which is so importantfor environmental studies, requires to set up and maintaincertain standards regarding precision. The elementaryrequirement is to produce measurements with a prescribed numberof valid digits. If the technology further develops and allowsto increase the number of valid digits, the change should bedocumented. In this paper we discuss a simple procedure for validity assessment of the last digits of data in a data base.It may be useful for both calibration control of an instrumentas well as for preliminary data analysis in scientific studies.The procedure utilizes the assumption that, under normalconditions, the last digits of the observed data can usually beconsidered as uniformly distributed random numbers. Failures ordeliberate changes in the observation mechanism can be detectedif particular digits occur more frequently than the others. Thedecision about proper representations of the last digits is doneby means of the familiar chi-square statistics.     

Uncertainty representation and propagation in quantified risk assessment using fuzzy sets

It is generally acknowledged that there are substantial uncertainties present in any analysis of risk. This paper provides a brief overview of the current techniques used for uncertainty analyses, and highlights their inappropriateness for practical use in the complete risk assessment process. The concept of fuzzy sets as a means for quantifying uncertainty is introduced and a case study demonstrates the application of this method to a simple consequence analysis where parameter uncertainty is considered. The results of this fuzzy analysis are compared with those of a more traditional probabilistic approach using a Monte Carlo simulation. This comparison demonstrates that the novel approach of fuzzy sets is a more appropriate technique due to its non-statistical nature and that the amount of computation required is substantially reduced compared to the traditional probabilistic approach. The versatility of fuzzy set theory suggests that this approach could also be used to quantify other types of uncertainty present in the risk assessment process, including model uncertainty and expert opinion.     

烟气脱硫技术的模糊评价

应用模糊数学原理，将素质决策与数量决策相结合，以三种典型烟气脱硫技术为例，对所涉及的定性和定量指标进行处理，经模糊线性加权变换，评估和优选出适合我国国情，具有较好的环境，社会，经济效益的烟气脱硫技术，比较表明，模糊决策方法在脱硫技术的评估和优选中优于单一的素质决策或数量决策方法，是一种有前途的科学的定量化决策方法。     

基于粗糙集和IPA的煤矿生产物流系统安全影响因素分析

针对煤矿生产物流系统安全影响因素众多、各因素对煤矿安全影响作用复杂的问题,提出基于粗糙集和IPA定位分析法的煤矿生产物流系统安全影响因素分析模型。首先运用粗糙集属性重要度思想对煤矿安全影响因素进行排序,然后采用IPA定位分析法识别不同安全状态煤矿安全生产的制约因素,最后通过对河南义马煤业集团跃进煤矿进行实证分析验证模型的可行性。结果表明,各因素对煤矿安全影响的重要度由大到小排序为通风设施、通风安全监控、通风技术管理、排水设备配置、机电安全管理、排水技术人员配备(采掘关系)、采掘机械、排水机构设置、安全运输、应急救援、采掘技术管理,且对于不同安全状态等级的煤矿而言,制约其安全的主要因素也不相同。     

基于毕达哥拉斯模糊贝叶斯网络的海底管道泄漏风险分析*

为分析海底管道运行中存在的泄漏风险,提出1种基于毕达哥拉斯模糊数与贝叶斯网络的风险评估模型。首先,通过毕达哥拉斯模糊数转换专家定性评价,拓展专家意见模糊范围;然后,结合主客观组合赋权法,利用毕达哥拉斯梯形爱因斯坦混合几何算子（PTFEHG）实现专家意见的聚合;最后,通过贝叶斯网络的推理与敏感性分析,计算海底管道泄漏风险的失效概率,并辨识关键风险因素。研究结果表明:该方法可以结合专家意见对海底管道泄漏风险进行定量分析,并识别导致泄漏事故的关键风险因素,对海底管道安全管理具有指导意义。     

基于粗糙集Skowron差别矩阵的矿井火灾风险评价指标约简

在安全评价过程中,当构建的指标体系过于庞大,数据繁琐,不利于进行安全评价工作。为了对矿井火灾风险评价指标体系进行快速简化,基于粗糙集理论属性约简功能,采用理论与实例分析相结合的方法,利用Skowron差别矩阵算法对矿井火灾风险评价决策表进行属性约简。研究表明:通过约简,矿井火灾风险评价指标由原来的16个缩减为6个,大大减少了后续评价过程中权重确定的计算量,降低了评价的复杂程度,减少了评估工作量。同时约简结果也为矿井火灾防治工作提供了一些指导意见,体现了差别矩阵在决策表的属性约简中的高效性。     

CRITICAL DATA REQUIREMENTS FOR PREDICTION OF EROSION AND SEDIMENTATION IN MOUNTAIN DRAINAGE BASINS1

ABSTRACT: The potential for understanding and, where necessary, managing sedimentation in humid mountain drainage basins increases with awareness of the conditions that lead to shallow landsliding, debris flows, and catastrophic sedimentation in stream channels. Progress in understanding has involved: improved recognition of source areas and the potential for downstream effects of slope failure; improved understanding of hydrological conditions required for failure; and a general theory of slope stability in shallow colluvium, including the role of plants, fires, timber harvest, and other disturbances. The theory acknowledges spatial variability in topographic and geotechnical terrain characteristics, the stochastic nature of climatic triggering events such as forest fires and rainstorms, and the integrating nature of channel networks in modulating the cumulative effects of transient processes within a basin. Anthropogenic fire regimes, road effects, and timber harvest can readily be included. Continued application and modification of the theory over an expanded geographical range require improvements in field data and their systematic storage in spatial databases. Improvements in digital topographic data for mountain basins, systematic network-wide surveys of channel conditions, and new technology for rapid documentation of soil depths in landslide source areas would enhance the prediction of mass failure, its consequences for channel habitat, and the basin-wide or regional distribution of hillslope and channel conditions. Computations of the probabilities of transient effects throughout basins could then form the basis of ecological risk analyses. Large-scale spatial data sets of a few critical variables are required before this next level of understanding can be developed and applied to sedimentation impacts on ecosystems and other resources.     

FUZZY DECISION MAKING IN GROUND WATER NITRATE RISK MANAGEMENT1

ABSTRACT: Ground water nitrate contamination is widespread in the United States and especially prevalent in agriculture-intensive areas such as the Midwest. To reduce human health risks (i.e., methemoglobinemia and cancer risks) from nitrates in ground water supplies, several nitrate risk-management strategies can be developed based on acceptable levels of human health risks, the reasonableness of the cost required for risk reduction, and the technical feasibility of nitrate-control methods. However, due to a lack of available information, assessing risk, cost, and technical feasibility contains elements of uncertainty. In this paper, a nitrate risk-management methodology using fuzzy sets in combination with a multicriterion decision-making (MCDM) technique is developed to assist decision makers in evaluating, with uncertain information, possible regulatory actions along with the various nitrate risk-management strategies in order to determine an appropriate strategy. The methodology is illustrated using data from a community with a nitrate water-quality problem.     

GIS and geostatistics: Essential partners for spatial analysis

Initially, geographical information systems (GIS) concentrated on two issues: automated map making, and facilitating the comparison of data on thematic maps. The first required high quality graphics, vector data models and powerful data bases, the second is based on grid cells that can be manipulated by suites of mathematical operators collectively termed map algebra. Both kinds of GIS are widely available and are taught in many universities and technical colleges. After more than 20 years of development, most standard GIS provide both kinds of functionality and good quality graphic display, but until recently they have not included the methods of statistics and geostatistics as tools for spatial analysis. Recently, standard statistical packages have been linked to GIS for both exploratory data analysis and statistical analysis and hypothesis testing. Standard statistical packages include methods for the analysis of random samples of cases or objects that are not necessarily co-located in space—if the results of statistical analysis display a spatial pattern then that is because the underlying data also share that pattern. Geostatistics addresses the need to make predictions of sampled attributes (i.e., maps) at unsampled locations from sparse, often expensive data. To make up for lack of hard data geostatistics has concentrated on the development of powerful methods based on stochastic theory. Though there have been recent moves to incorporate ancillary data in geostatistical analyses, insufficient attention has been paid to using modern methods of data display for the visualization of results. GIS can serve geostatistics by aiding geo-registration of data, facilitating spatial exploratory data analysis, providing a spatial context for interpolation and conditional simulation, as well as providing easy-to-use and effective tools for data display and visualization. The value of geostatistics for GIS lies in the provision of reliable interpolation methods with known errors, methods of upscaling and generalization, and for supplying multiple realizations of spatial patterns that can be used in environmental modeling. These stochastic methods are improving understanding of how errors in models of spatial processes accrue from errors in data or incompleteness in the structure of the models. New developments in GIS, based on ideas taken from map algebra, cellular automata and image analysis are providing high level programming languages for modeling dynamic processes such as erosion or the development of alluvial fans and deltas. Research has demonstrated that these models need stochastic inputs to yield realistic results. Non-stochastic tools such as fuzzy subsets have been shown to be useful for spatial analysis when probabilistic approaches are inappropriate or impossible. The conclusion is that in spite of differences in history and approach, the linkage of GIS, statistics and geostatistics provides a powerful, and complementary suite of tools for spatial analysis in the agricultural, earth and environmental sciences.