基于流推理的定线制水域船舶航行安全评估方法

为了更有效地利用船舶航行过程中人员、船舶、环境、管理等数据,实时评估船舶航行安全状态,提出基于流推理(SR)的定线制水域船舶航行安全评估方法。首先,利用粗糙集条件信息熵(RSCIE)约简评估要素,结合专家知识,设计评估本体;然后,通过语义Web技术融合实时采集的相关流数据,用SR引擎推理船舶航行安全状态;最后,运用该方法评估南京下游定线制水域的船舶航行安全状态,并与基于云理论(CT)的安全评估方法等对比分析。结果表明:本文提出的方法具有更好的时效性,而且检测率高、误报率低。     

基于RS-SVM的城市埋地燃气管道外腐蚀情况评价

为提高城市埋地燃气管道外腐蚀情况评价的准确性,识别影响管道外腐蚀的主要因素,构建评价指标集,结合粗糙集(RS)与支持向量机(SVM)的优势,建立管道外腐蚀情况预测评价模型。给出具体评价步骤,包括收集样本数据、预处理数据、用属性约简算法筛选核心指标集、用SVM训练器训练数据,形成检验模型。以某条城市燃气管线为例进行实例验证和分析。结果表明:用 RS-SVM 模型预测评价管道的腐蚀等级与实际结果一致,传统方法预测管道腐蚀速率平均相对误差为14.1%,RS-SVM模型预测的平均相对误差为7.9%,较之传统方法精度更高。     

粗糙集综合评价法在绿色制造评价中的应用研究

为提高产品零件制造工艺绿色度评价的客观性，根据军品零件绿色制造的特点和绿色制造评价的要求，应用粗糙集原理，提出了粗糙集综合绿色度评价法。阐述了该方法的原理及运用该方法进行综合评价的步骤，并通过实例验证了该方法的可行性和实用性。     

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。     

基于RS-云模型的硫化矿石自燃倾向性综合评价

为实现硫化矿石自燃倾向性的综合评价,应用云理论,选取矿样的氧化质量增加率、自热点和自燃点等定量化指标构建了评价指标体系,运用粗糙集法（Rough Set, RS）计算各评价指标权重,依据分级标准对云模型进行标准化,进而建立硫化矿石自燃倾向性分级的RS-标准云模型。以30组工程实例进行模型检验,并在实际工程中进行应用。结果表明:RS-标准云模型的判别结果与实际结果较吻合,且判别准确率高于贝叶斯(Bayesian, Bayes)方法,表明RS-标准云模型在硫化矿石自燃倾向性综合评价中具有良好的实用性和可靠性,可为矿山的硫化矿石自燃灾害的研究提供一种新思路。     

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.     

Research on flood risk analysis and evaluation method based on variable fuzzy sets and information diffusion

Floods have become increasingly alarming worldwide. Flood risk management in terms of assessing disaster risk properly is a great challenge that society faces today. Natural disaster risk analysis is typically beset with issues such as imprecision, uncertainty, and partial truth. There are two basic forms of uncertainty related to natural disaster risk assessment, namely, randomness caused by inherent stochastic variability and fuzziness due to macroscopic grad and incomplete knowledge sample. However, the traditional probability statistical method ignores the fuzziness of risk assessment with incomplete data sets and requires a large sample size of data. The fuzzy set methodology is introduced in the area of disaster risk assessment to improve probability estimation. The purpose of the current study is to establish a fuzzy model to evaluate flood risk with incomplete data sets. The present paper puts forward a composite method based on variable fuzzy sets and information diffusion method for disaster risk assessment. The results indicate that the methodology is effective and practical; thus, it has the potential to forecast the flood risk in flood risk management. We hope that by conducting such risk analysis, the impact of flood disasters can be mitigated in the future.     

Handling and updating uncertain information in bow-tie analysis

Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment.Fuzzy set and evidence theory are capable of characterizing the uncertainty associated with expert knowledge. To minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence are equally important in addition to addressing the uncertainties in the knowledge. This paper proposes a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis. A case study comprising a bow-tie for a typical offshore process facility has also been developed to describe the utility of this methodology in an industrial environment.     

Accident data for the Semantic Web

This paper describes concerns about the documentation, dissemination and use of lessons learned from mishap investigations, impediments posed by current practices, and opportunities for improvement. Lessons are presently developed, documented and stored primarily in narrative form and relational databases, and disseminated in many forms and media, including the Internet. Current practices pose many impediments to maximized development, dissemination and use. Investigation process research and new data concepts behind the Semantic Web, exploited elsewhere, offer potential opportunities to overcome these impediments. To exploit these opportunities, formation of a working group to develop an improved Semantic Web-friendly mishap investigation lessons learning system is proposed. An example illustrating an alternative approach is described to support a reasonable expectation that an alternative lessons learning system could be developed.     

基于粗糙集理论的危险货物运输风险分析

为解决不确定环境中危险货物运输风险分析问题,针对风险数值分析等方法无法识别构成风险的主要因素和次要因素,无法获得因素重要度,以及不能揭示风险因素与风险之间的因果关系等问题,提出基于粗糙集理论的危险货物运输风险分析方法。首先将原始样本进行属性约简和规则约简,获得各个属性的重要度,识别影响危险货物运输安全的主要因素和次要因素;然后,通过对原始样本进行实例推理,推导出危险货物运输事故规律;最后通过算例验证模型和算法的有效性。结果表明,道路的平纵曲线半径是影响危险货物运输安全的最重要因素,其次是驾驶员因素和运输车辆因素。