AHP-模糊综合评价法在标准实施效果评价中的应用

开展标准实施效果评价能够对标准的实施状况、标准的科学性有一个客观和科学的认识,对标准的制修订工作有重要的指导意义。本文以石油工业标准为研究对象,从标准实施状况、标准实施监督、标准经济效益和标准社会效益4个方面构建了评价体系,综合运用层次分析法(AHP)和模糊综合评价法对标准实施效果进行评价。评价结果表明,该方法可以实现定性分析和定量分析的有效结合,使评价结果更符合实际情况,为标准实施效果评价提供了一种新的思路。     

基于模糊多模型的堆芯功率控制

传统的堆芯功率PID控制器是基于单一功率水平处的堆芯局部模型设计的，难以准确描述整个堆芯功率水平范围的控制。因此，本文基于5个不同功率水平下的传递函数模型，通过三角隶属度函数加权，建立堆芯模糊多模型，并依据该模型设计堆芯功率模糊PID控制。以TMI型压水堆堆芯为对象，开展不同初始功率水平下的堆芯功率跟踪、堆芯进口温度扰动的控制仿真。结果表明，基于模糊多模型设计的堆芯功率模糊PID控制器可实现对堆芯功率的良好控制。     

Density estimates on the unit simplex and calculation of the mode of a sample

This paper addresses reliable and efficient calculation of the mode of a multivariate sample, which is a classical fusion function. In particular, we focus on the inputs given on the unit simplex, when aggregating elements of Atanassov intuitionistic fuzzy sets, interval-valued fuzzy sets and their extensions, as well as compositional data. We outline the use of a specially designed 2-additive fuzzy measures and the Choquet integral for the purposes of reducing computational complexity in higher dimensions. We present computational analysis and benchmark four different methods of density-based mode estimation.     

模糊时间窗约束下的装配线物料配送方案优化

为解决装配线物料无法及时准确配送的问题,提出了一种动态的联合配送策略,在此基础上以最小化单个产品配送成本、最大化工位满意度为目标,建立模糊时间窗约束下的物料配送数学模型。利用NSGA-Ⅱ算法求解出最佳的工位组划分、小车出发时间和配送路径,并对配送小车容量及配送时间窗进行优化。利用Plant Simulation软件搭建某生产线仿真模型进行模拟,验证了配送方案的可行性。与一般配送策略进行对比,结果表明,在保证配送工位满意度的前提下,优化时间窗能进一步降低单个产品的配送成本。     

基于模糊综合评价的景区解说设施优化研究

目的 系统地研究景区解说设施评价体系,建立合理完善的评价方法,为景区解说设施的优化升级提供科学的理论支撑.方法 选取泉州市崇武古城的解说设施为研究对象,以SERVQUAL(服务质量)模型为基础建立解说设施满意度评价体系,借助主成分分析、模糊综合评价以及IPA分析法3种方法,探索景区解说设施满意度及其影响因素.结论 研究表明,构成景区解说设施满意度评价体系的一级指标主要为有形性(UA)、保证性(UB)、移情性(UC)、可靠性(UD),四者的权重由大到小依次为UB>UA>UD>UC;泉州崇武古城景区解说设施满意度评价结果为良好,基本达到旅游解说要求.但是解说设施的有形性方面亟待提升,在视觉效果上的满意度相对较低,影响游客对古城景区的满意度.     

Ship length distribution modeling on China offshore and ability evaluation of radar ship classification

Radial size estimation using radar high-resolution range profiles(HRRPs) and heading angle estimation are the main means for ship classification.The classification ability is closely related to the range resolution of the radar,precision of radial size estimation,and prior distribution of ship lengths in different offshore areas.We collected the AIS information on about 30 000 ships and their lengths in the four offshore areas of China in the ship information net of China.By fitting the data of ship lengths in each offshore area,it is found that the Weibull distributions provide good-of-fitness to the ship lengths and the parameters in individual area are rather different.Based on the prior distributions of ship lengths,we derived the quantitative relationship between the correct classification probability of big-moderate-small ships and the estimate error of ship radial size.The results indicate that the condition for the big-moderate-small correct classification probability in the offshore areas of China to be up to 90% is that the estimate errors of the ship radial size estimates falls into the interval(-12.67 m,9.41 m) when the heading angle of the ship is between ±75 degrees.     

Hybrid-triggered fault detection filter design for networked Takagi–Sugeno fuzzy systems subject to persistent heavy noise disturbance

In the network environment, the single time-triggered scheme wastes limited bandwidth resources due to all the sampled data are transmitted to the networks, and the single event-triggered scheme may increase system error because of ignoring factors such as changes in network utilization. To reduce the design conservatism, this paper is concerned with the hybrid-triggered L₁ fault detection filter design for a class of nonlinear networked control systems (NCSs) described by Takagi–Sugeno (T-S) fuzzy model. Taking the effects of time-triggered scheme and event-triggered scheme into consideration simultaneously, we construct a fuzzy fault detection system. New results on stability and L₁ performance are proposed for fuzzy fault detection system by exploiting the Lyapunov–Krasovskii functional and by means of the integral inequality method. Specially, attention is focused on the design of fault detection filter that guarantees a prescribed L₁ noise attenuation level . Finally, two examples are presented to demonstrate the effectiveness of the proposed method.     

An efficient method for estimating time-dependent failure possibility by combining adaptive Kriging with adaptive truncated fuzzy simulation

Time-dependent failure possibility (TDFP) can measure the structural safety level for a time interval of interest under fuzzy uncertainty, but its calculational cost is unaffordable by using fuzzy simulation (FS) due to a required large size of FS candidate sampling pool (CSP). Although time-dependent adaptive Kriging model (T-AK) combined with FS (T-AK-FS) was presented to reduce the number of calling performance function, a large FS CSP still makes training T-AK time-consuming. To improve its efficiency, an adaptive truncated FS (ATFS) with T-AK (T-AK-ATFS) is proposed by CSP size reduction approach. By T-AK-ATFS, the largest safety hypercube in fuzzy standard space is adaptively searched, in which the samples are in safety states and can be removed from the FS CSP. Moreover, T-AK is adaptively trained to search the largest safety hypercube and estimate TDFP simultaneously. In adaptively searching process, the FS CSP is divided into several sub-CSPs, on which training T-AK is more time-saving. Overall, strategies of T-AK-ATFS include proposing ATFS to reduce the FS CSP, adaptively searching the largest safety hypercube, estimating the TDFP with the same T-AK and training T-AK in the sub-CSPs sequentially. Verified by examples, these strategies make T-AK-ATFS more efficient than existing FS and T-AK-FS.     

Multi-task learning for video anomaly detection

We propose a multi-task learning framework for video anomaly detection based on a novel pipeline. Our model contains two crossing streams, one stream employs the backbone of Attention-R2U-net for future frame prediction, while the other is designed based on an encoder–decoder network to reconstruct the input optical flow maps. In addition, the latent layers of the two streams are merged together and assigned with a Deep SVDD-based loss at each location individually. Through the combination of these three tasks, the two-stream-crossing pipeline can be trained end-to-end to provide a comprehensive evaluation for the anomaly targets. Experimental results on several popular benchmark datasets show that our model outperforms the state-of-the-art competing models, which can be applied to different types of anomalous targets and meanwhile achieves remarkable precision.     

A Unified Decision-Making Technique for Analysing Treatments inPandemicContext

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation.