Construction of Tourist Satisfaction Degree Evaluation Model in Western Tourist Area by Performance Prism and Fuzzy Comprehensive Evaluation Method——with Lijiang in Yunnan Province as the Study Case

Starting from the utilization and protection of local knowledge, with the performance prism as the framework, the evaluation index system of tourist satisfaction degree was established. The weight was determined by using AHP method. Finally, the investigating result was judged with fuzzy comprehensive evaluation method, the evaluation model of tourist satisfaction degree in western tourist area was built, and the case study was carried out. With Lijiang in Yunnan Province as example, according to AHP method, five dimensions weight of the performance prism, various KPI weight and consistency were obtained, fuzzy evaluation on tourist satisfaction degree was conducted. The results showed that the overall was satisfactory, but there were still some problems. Aiming at the utilization and protection of local knowledge, some corresponding countermeasures were put forward which will benefit for further development of tourism in Lijiang of Yunnan Province.

Integration degree of tourism industry based on AHP-fuzzy comprehensive evaluation

By reviewing the traditional measure methods of the integration degree of the tourism industry, we concluded that measure methods of the integration degree of the tourism industry can only be limited to the levels of theoretical analysis due to the defect of the statistical data and the statistical structure. With the help of concept of fusion field and fusion entropy of the tourism industry, a physical model of integration of the tourism industry is established and index system based on AHP is proposed. Using AHP-fuzzy comprehensive evaluation method, the integration degree of Jiangsu and the country's tourism industry are studied. The influencing factors are analyzed and the corresponding countermeasures are proposed.

Life-cycle cost model and evaluation system for power grid assets based on fuzzy membership degree

Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require further research for practical application.This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life.A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets.An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid,providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.

Study on fasART neuro-fuzzy networks for distinguishing the difficulty degree of top coal caving in steep seam

Distinguishing the difficulty degree of top coal caving was a precondition of the popularization and application of the roadway sub-level caving in steep seam. Because of complexity and uncertainty of the coal seam, the expression of influence factors was diffi-culty with exact data. According to the fuzzy and uncertainty of influence factors, triangular fuzzy membership functions were adopted to carry out the factors ambiguity, of which the factors not only have the consistency of semantic meaning, but also dissolve sufficiently expert knowledge. Based on the properties and structures of fasART fuzzy neural net-works of fuzzy logic system and practical needs, a simplified fasART model was put for-ward, stability and reliability of the network were improved, the deficiency of learning sam-ples and uncertainty of the factors were better treated. The method is of effective and practical value was identified by experiments.

Temperature Control Based on Fuzzy Logic Two-degree-of-freedom Smith Internal Model

According to the characteristics of the large time delay,nonlinearity and the great inertia of temperature control system in biomass pyrolysis reactor,a two-degree-of-freedom Smith internal model controller based on fuzzy control is proposed.Firstly,the mathematical model of the temperature control system is established by using the step response method,and then the two-degree-of-freedom Smith internal model controller is designed,and the good tracking performance and disturbance suppression performance can be obtained by designing the set value tracking controller and interference rejection capability.Secondly,the fuzzy control algorithm is used to realize the on-line tuning of the control parameters of the two-degree-of-freedom Smith internal model algorithm.The simulation results show that,compared with the traditional internal model control,fuzzy internal model PID control and two-degree-of-freedom Smith internal model control,the algorithm proposed in this paper improves the influence of lag time on the control system,realizes the separation control of set point tracking and anti-jamming performance and the self-tuning of control parameters,and improves the control performance of the system.

Method for solving fully fuzzy linear programming problems using deviation degree measure

A new fully fuzzy linear programming （FFLP） problem with fuzzy equality constraints is discussed. Using deviation degree measures, the FFLP problem is transformed into a crisp 6-parametric linear programming （LP） problem. Giving the value of deviation degree in each constraint, the 6-fuzzy optimal solution of the FFLP problem can be obtained by solving this LP problem. An algorithm is also proposed to find a balance-fuzzy optimal solution between two goals in conflict： to improve the values of the objective function and to decrease the values of the deviation degrees. A numerical example is solved to illustrate the proposed method.

Rough similarity degree and rough close degree in rough fuzzy sets and the applications

Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar degree between two rough fuzzy sets. The properties and theorems are listed. Using the two new measures, the method of clustering in the rough fuzzy system can be obtained. After clustering, the new fuzzy sample can be recognized by the principle of maximal similarity degree.

Degree of Compactness in I-fuzzy Topological Spaces

In this paper, the concept of degree of compactness is introduced in the general framework of I-fuzzy topological spaces, and its property is discussed. All good compactness are generalized to I-fuzzy topological spaces accordingly.

A New Definition of Intuitionistic Fuzzy Similarity Degree

As far as the problem of intuitionistic fuzzy cluster analysis is concerned, this paper proposes a new formula of similarity degree with attribute weight of each index. We conduct a fuzzy cluster analysis based on the new intuitionistic fuzzy similarity matrix, which is constructed via this new weighted similarity degree method and can be transformed into a fuzzy similarity matrix. Moreover, an example is given to demonstrate the feasibility and validity of this method.

Synthetical condition assessment of long span suspension bridge based on closeness degree and FAHP

Based on the theory of pattern recognition, the concept of closeness degree between fuzzy sets is brought into the condition assessment of long span bridges. Using the fuzzy analytic hierarchy process （FAHP）, a mathematical model of a multi-objective assessment of a long span suspension bridge is set up. An example is given to show the procedure in the synthetical condition assessment of the Runyang Suspension Bridge, which includes the hierarchical division, the definition of factor weights and fuzzy membership functions, and the calculation of closeness degrees, etc. The assessment combines both the data from the health monitoring system and the manual tests. The classification of evaluation items as well as the calculation of deterministic and nondeterministic items is presented. Compared with the traditional method of point rating, this method can better describe the discreteness of monitoring data and the fuzziness in the condition assessment.

HYDROCHEMICAL INDEXES OF SEAWATER INTRUSION AND COMPREHENSIVE JUDGEMENT ON INTRUSIVE DEGREE

The paper discusses the use of five hydrochemical indexes in evaluating seawater intrusion, and ranking the intrusion degree for Cl -, M, Br -, rHCO 3/rCl and SAR. The fuzzy mathematical comprehensive judgement model has been adopted as the method of adjustment. Based on more than 300 water samples obtained from four typical profiles in the coastal region of Laizhou Gulf during the period from June 1993 to June 1995, we made a comprehensive judgement of seawater intrusion degree. The result shows that intrusion degree was becoming heavier from June 1993 to June 1994, but more steady and inactive in the high level of intrusion degree in the perennial severe intrusion zone from December 1994 to June 1995. Since entering the 1990s up to now, the process of seawater intrusion has translated from its peak developmental stage from the 1970s to the 1980s into a stage of equilibrium and slow development mainly because the intruded area expanded inland, precipitation increased at some extent recently and various measures of prevention and harness begin to show the effect.

Applying Possibility Degree Method for Ranking Interval Numbers to Partnership Selection

A new interval number ranking approach is applied for assessment of priorities of the alternative partners, where the attribute values are given out as interval numbers while the weight of each criterion is still exact numerical value pattern. After aggregating with the weighted arithmetic averaging operator, the result is still in the form of interval number. To achieve the priorities of alternative partners we take the possibility method for ranking interval numbers into account which could derive priorities from inconsistent attribute values, thus eliminating the adjustment to the inconsistent attribute values. Moreover, this method is very simple and needs less calculation. An illustrative example is given out to demonstrate this smart method.

A Support Vector Machine-based Evaluation Model of Customer Satisfaction Degree in Logistics

This paper presents a novel evaluation model of the customer satisfaction degree （CSD） in logistics based on support vector machine （SVM）. Firstly, the relation between the suppliers and the customers is analyzed. Seondly, the evaluation index system and fuzzy quantitative methods are provided. Thirdly, the CSD evaluation system including eight indexes and three ranks based on one-against-one mode of SVM is built, last simulation experint is presented to illustrate the theoretical results.

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.

生猪生长环境舒适度实时监测系统设计与实现

为了提高猪舍环境舒适度评价的准确性,克服猪舍多环境因子相互耦合,避免采用单一环境因子评价猪舍内环境质量状况不准确问题。本研究根据猪舍环境养殖标准,构建了猪舍舒适度评价指标体系和权重,建立了温度、湿度、风速、CO_(2)、NH_(3)和H_(2)S各环境因子的隶属度函数,提出了基于层次分析-模糊理论的猪舍多环境因子综合评价方法,搭建了生猪生长环境舒适度实时监测系统。以育肥猪舍环境监测的24组数据为例对本文提出的猪舍舒适度模糊综合评价方法进行验证。结果表明,该系统建立的猪舍多环境因子舒适度模糊综合评价方法能准确反映猪舍空气质量状况,可为猪舍环境精准调控提供科学决策依据,具有一定的应用价值。

基于IAHP-Fuzzy法的绿色建筑绿色度评价

绿色建筑是推进我国建筑领域实现碳中和的重要发展方向,为进一步完善我国绿色建筑评价方法,以适应绿色建筑的快速发展,结合我国新版GB/T 50378—2019《绿色建筑评价标准》和国内外学者提出的绿色建筑评价模型,本文从安全耐久、健康舒适、生活便利、资源节约、环境宜居五个维度构建绿色建筑绿色度评价指标体系,运用改进层次分析法确定各指标权重,根据模糊综合评价建立了绿色建筑绿色度评价模型,并通过实例研究验证了该方法的可行性和实用性,并针对评价结果提出相应对策,以期为我国绿色建筑发展提出切实有效的建议。

面向类重叠日志的一致性异常检测模型

在系统日志异常检测中,决策边界出现的类重叠问题将导致传统分类器很难实现正确分类。为避免耗时的预处理技术或依赖特定算法,提出一致性异常检测模型。该模型计算样本与不同类别的隶属度,根据传统分类器的准确率差值选择最佳模糊度分离出类重叠日志;通过集成分类器的不一致性度量函数得到p值,根据预设置信度得到类重叠日志标签。实验结果表明,相比传统分类器,该模型的召回率和F值等平均提高10百分点左右,验证了该模型在处理类重叠问题的有效性。

匝道合流交通场景下自动驾驶汽车安全性测试评价方法

针对高速匝道交通场景提出了一种基于自然驾驶数据的自动驾驶汽车安全性评价方法。通过分析合流区冲突特征,建立碰撞时间(Time to Collision,TTC)模型、后侵占时间(Post Encroachment Time,PET)模型、最小安全换道距离(Minimum Safe Spacing,MSS)交通冲突指标计算模型来确定安全性评价指标,利用模糊聚类自然驾驶指标数据确定指标阈值范围,搭建自动驾驶汽车仿真测试试验,应用层间相关性的重要性准则权重分配方法和灰色关联评分模型,计算得到不同控制算法下自动驾驶汽车安全性的综合评价得分。结果表明,被试车辆驾驶行为与理想驾驶行为在各安全性指标的关联度分布明显,计算总体关联度,得分可以直观说明不同自动驾驶系统的综合安全性能。