A cloud-based fuzzy approach for spatial site selection in decision support system

In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can’t pay attention to the randomness in factors. To remedy the problem, this paper proposes a clouded-base fuzzy approach which combines advantages of cloud transform and fuzzy synthetic evaluation. The cloud transform considers the randomness in the factors and product the higher concept layer for data mining. At the same time, the check mechanism controls the quality of partitions in factors. Then the fuzzy approach was used to get final evaluation value with randomness and fuzziness. It make the final result is optimization. Finally, performance evaluations show that this approach spent less runtime and got more accuracy than the fuzzy synthetic. The experiments prove that the proposed method is faster and more accuracy than the original method.     

Fuzzy Set-Based Risk Evaluation Model for Real Estate Projects

With the rapid development of residential real estate market, risk evaluation has been an important task in the process of project. This paper describes a risk evaluation method for residential real estate projects based on fuzzy set theory which uses linguistic variables and respective fuzzy numbers to evaluate the factors. The primary weights of factors and evaluation of alternatives are determined by applying linguistic variables and fuzzy numbers. The notion of Shapley value is used to determine the global value of each factor in accomplishing the overall objective of the risk evaluation process, so the primary weights are revised, thus the importance of factors can be reflected more precisely. A major advantage of the method is that it allows experts and engineers to express their opinions on project risk evaluation in linguistic variables rather than crisp values. An illustration is presented to demonstrate the application of the method in risk evaluation. The results are consistent with the results calculated by conventional risk evaluation method. The research demonstrates that the method is objective and accurate, and is of an application value in the risk evaluation for residential real estate project.     

Moldability Evaluation for Molded Parts Based on Fuzzy Reasoning

Moldability evaluation for molded parts,which is the basis of concurrent design,is a key design stage in injection molding design.By moldability evaluation the design problems can be found timely and an optimum plastic part design achieved.In this paper,a systematic methodology for moldability evaluation based on fuzzy logic is proposed.Firstly,fuzzy set modeling for six key design attributes of molded parts is carried out respectively.Secondly,on the basis of this,the relationship between fuzzy sets for design attributes and fuzzy sets for moldability is established by fuzzy rules that are based on domain experts‘ experience and knowledge.At last the integral moldability for molded parts is obtained through fuzzy reasoning.The neural network based fuzzy reasoning approach presented in this paper can improve fuzzy reasoning efficiency greatly,especially for system having a large number of rules and complicated membership functions.An example for moldability evaluation is given to show the feasibility of this proposed methodology.     

High-temperature performance prediction of iron ore fines and the ore-blending programming problem in sintering

The high-temperature performance of iron ore fines is an important factor in optimizing ore blending in sintering. However, the application of linear regression analysis and the linear combination method in most other studies always leads to a large deviation from the desired results. In this study, the fuzzy membership functions of the assimilation ability temperature and the liquid fluidity were proposed based on the fuzzy mathematics theory to construct a model for predicting the high-temperature performance of mixed iron ore. Comparisons of the prediction model and experimental results were presented. The results illustrate that the prediction model is more accurate and effective than previously developed models. In addition, fuzzy constraints for the high-temperature performance of iron ore in this research make the results of ore blending more comparable. A solution for the quantitative calculation as well as the programming of fuzzy constraints is also introduced.     

Fabric Wrinkle Grade Assessment Based on Fuzzy Pattern Recognition

The basic principle of fuzzy pattern recognition is brief introduced firstly in this paper, which mainly includes fuzzy rules and fuzzy inference system. Then, the algorithm procedure of fuzzy pattern recognition is proposed. Finally, the application of Mamdani fuzzy model is introduced to evaluate fabric wrinkle grade in detail, and used the correlation coefficient between subject and object evaluation to verify the reliability of fuzzy pattern recognition. It shows the method of fuzzy pattern recognition needs not a large number of testing data and the accuracy of evaluation is up to 97.38%.     

Analytical method for promoting process capability of shock absorption steel

Mechanical properties and low cycle fatigue are two factors that must be considered in developin gnew type steel for shock absorption. Process capability and process control are significant factors in achieving the purpose of research and development programs. Often-used evaluation methods failed to measure processyield and process centering ; so this paper uses Taguchi loss function as basis to establish an evaluation methodand the steps for assessing the quality of mechanical properties and process control of an iron and steel manu-facturer. The establishment of this method can serve the research and development and manufacturing industry and lay a foundation in enhancing its process control ability to select better manufacturing processes that are more reliable than decision making by using the other commonly used methods.     

Estimation of crop water requirement based on principal component analysis and geographically weighted regression

In this study the principal component analysis (PCA) and geographically weighted regression (GWR) are combined to estimate the spatial distribution of water requirement of the winter wheat in North China while the effect of the macroand micro-topographic as well as the meteorological factors on the crop water requirement is taking into account. The spatial distribution characteristic of the water requirement of the winter wheat in North China and its formation are analyzed based on the spatial variation of the main affecting factors and the regression coefficients. The findings reveal that the collinearity can be effectively removed when PCA is applied to process all of the affecting factors. The regression coefficients of GWR displayed a strong variability in space, which can better explain the spatial differences of the effect of the affecting factors on the crop water requirement. The evaluation index of the proposed method in this study is more efficient than the widely used Kriging method. Besides, it could clearly show the effect of those affecting factors in different spatial locations on the crop water requirement and provide more detailed information on the region where those factors suddenly change. To sum up, it is of great reference significance for the estimation of the regional crop water requirement.     

Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems

In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.     

Packet Queueing Delay in Resilient Packet Ring Network Nodes

The packet queueing delay is one of the most important performance measures of a data network and is also a significant factor to be considered in the scheduling buffer design for a network node. This paper presents a traffic queueing model for resilient packet ring (RPR) networks and a method for quantitatively analyzing queueing delays in RPR nodes. The method was used to calculate the average queueing delays of different priority traffic for different transit queue modes. The simulations show that, in the transmit direction, lower priority traffic is delayed more than higher priority traffic, and that Class-A traffic is delayed more in a single-queue ring than in a dual-queue ring. In the transit direction, the secondary transit buffer in the dual-queue ring contributes more to the traffic delay than the primary transit buffer in the single-queue ring, which in turn causes more delay than the primary transit buffer in the dual-queue ring.     

Fuzzy clustering method for Web user based on pages classification

A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the site. then computes fuzzy degree of cross page through aggregating on data of Web tog. After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits. and derives the fuzzy similarity matrix from the interest vectors for the Web users. Finally. it gets the clustering result through the fuzzy clustering method. The experimental results show the effectiveness of the method.     

基于云模型的地下铁矿区生态风险模糊综合评价

针对铁矿开采带来的生态环境问题,从固有危险性、现状水平和控制状况三个方面建立地下铁矿区生态风险综合评价指标体系。该指标体系能从宏观上反映矿区生态风险的主要矛盾,利于矿区间进行比较。针对评价过程中的模糊性和随机性,构建了基于云模型的模糊综合评价方法,利用云模型改进模糊评价的评语集和隶属度函数,有效体现了评价过程中模糊性和随机性的统一,同时引入模糊熵概念进一步表征风险评价结果的模糊性和所评矿区生态问题的复杂度。程潮铁矿实证表明,评价结果能较好地反映矿区实际情况。本文所提出的方法为矿区生态风险综合评价提供了一种新的分析框架。     

污水处理PPP项目收益风险云模型综合评价

为了准确评价污水处理PPP(public private partnership)项目收益风险,解决单一方法评价的局限性,提出了一种收益风险云模型综合评价模型.首先建立了污水处理P P P项目收益风险评价指标体系,并提出了改进FSE-SD二次加权模型,在风险指标传统主客观赋权的基础上,对风险指标进行二次加权得到最终权重...     

基于改进的CRITIC法和云模型的科技奖励评价研究

针对科技奖励评价问题,用标准差系数改进CRITIC法对专家评分进行动态赋权,结合云模型用逆向云发生器构造模糊评价矩阵,用虚拟云计算出评价项目的期望、熵和超熵,得出评价结果并排序.既考虑了科技奖励评价过程中专家评分的模糊性和随机性,又实现了定性语言与定量数值之间的转换,减少了专家主观因素的干扰,使评价结果更加准确、客观.     

基于贝叶斯修正IFAHP和灰色模糊综合评判的绝缘子污秽评估

绝缘子状态评估中指标信息兼具模糊、随机和灰色性特点。针对当前评估方法较少考虑到参量因素灰色性的问题,基于贝叶斯修正IFAHP法并结合灰色模糊理论和灰云模型提出一种绝缘子污秽状态的灰色模糊综合评判模型。该模型首次引入IFAHP和直觉模糊熵理论以更为科学合理地确定评判因素权重集,并基于贝叶斯公式建立主观权重的优化模型以实现指标的二阶段赋权;同时,利用灰云模型求得评判指标对应各状态等级的关联度并作为评判矩阵模部,再以灰部兼顾评判信息的灰性,进而对建立的评判矩阵综合处理。实例对比表明,贝叶斯修正IFAHP法确定的权重集更为合理适用,基于灰色模糊评判的结果也更贴近实际情况。     

基于云理论的隧道结构健康诊断方法

隧道健康状态诊断过程作为一个模糊系统,具有随机性和模糊性的特点.传统的隧道结构健康诊断方法无法妥善地将系统的模糊性和随机性关联在一起.基于此,针对隧道结构健康状态诊断系统的模糊性和随机性特点,提出了基于云理论的隧道结构健康状态诊断方法.首先,建立反映隧道结构健康状况等级评语集的正态云模型.然后,依据逆向云发生器原理,将隧道结构健康状态指标的监测数据的归一化结果值转化为隶属度云模型.将健康状态指标的重要性语言值转化为权重云,用于表征各健康状态指标的重要程度.最后,运用云理论的计算方法对隧道结构健康状态等级进行诊断,得到健康状态等级诊断结果的云模型.应用云模型的改进方法,对某市地铁2号线盾构隧道结构健康状态进行了诊断,极大提高了诊断结果的可视化和鲁棒性.     

基于云模型的PM_(2.5)污染程度评价

空气污染问题是近期公众关注的焦点问题.考虑污染因子PM2.5污染程度的模糊性和随机性,采用武汉市130个以PM2.5为首要污染物的AQI数据,利用云模型建立了空气质量评价体系,并用于PM2.5污染程度评价.以PM2.5指数88为例,评价其属于良好等级的隶属度区间为[0.64,0.87],属于轻度污染等级的隶属度区间为[0.03,0.13],最终评价为良好等级,与国家标准一致,但信息表达更充分.结果表明:采用云模型进行空气污染程度评价,实现了空气质量分级评估随机性与定性语言表达模糊性的统一,更符合人类对事物的认知规律.云模型评价空气质量是国家评价标准的有益补充.     

黄土地基承载力的模糊评判方法

针对黄土地基承载力影响因素复杂的特点，基于模糊综合评判原理提出了黄土地基承载力模糊综合评判的确定方法。通过对黄土地区大量实测资料进行统计分析，构造了隶属函数形式，建立了承载力评价模型，权重采用了主观和客观因素相结合的综合权重原则。工程实例验证表明，用该方法确定的承载力更客观、全面，精度明显提高。     

山岭隧道洞口震害因素分析与抗震风险模糊综合评价

针对山岭隧道洞口抗震风险的随机性与模糊性特点,提出采用模糊综合评价模型对其进行风险评估.通过统计分析汶川地震中山岭隧道洞口段震害资料,总结影响隧道洞口震害的因素,再遴选出主要因素作为模糊综合评价的影响因子.采用层次分析法和隶属函数分别计算隧道洞口段震害各风险因素的相对权重及各风险因素对风险水平的隶属度,将各相关风险因素的权重与风险因素对风险水平的隶属度进行模糊综合运算,得出隧道洞口抗震风险水平.最后采用模糊综合评价法对实际工程进行抗震风险评估.研究结果可供高烈度地震区隧道洞口抗震设计提供参考.     

基于云模型的食品安全监理风险评估研究

为了及时预测食品安全问题,降低食品安全风险,通过分析并建立3层影响食品安全的风险因素体系,利用云模型来消除食品安全风险评估过程中的模糊性和随机性,用赋权法确定指标权重集,最后利用模糊法计算风险等级结果,从而实现对评估对象的风险等级的预测。     

基于组合赋权法和云重心评估法的燃气轮机健康状态评估

鉴于表征燃气轮机健康状态的信息具有模糊性和随机性的特点,提出了一种基于组合赋权法和云重心评估法相结合的健康状态评估方法。方法首先运用层次分析法和熵权法分别计算燃气轮机评估指标的权重值,再通过组合赋权法,得到具有主客观意义的权重;在此基础上,引入云重心评估法对燃气轮机进行评估;并采用加权偏离度衡量系统状态变化的程度,从而确定燃气轮机健康状态等级。以某型真实燃气轮机为例,对其进行了试验验证。验证结果表明,提出的基于组合赋权法和云重心评估法的健康状态评估方法能有效对燃气轮机进行评估;并能够弱化燃气轮机健康状态监测信息的随机性和模糊性。