嵌入随机概率模型的模具项目模板及其应用

针对模具生产中存在的不确定性因素,在考虑企业关键资源有限性的前提下,采用嵌入随机概率模型的模具项目模板,实现多项目调度,能够为企业制定较为准确且符合实际生产情况的项目计划。     

一种基于多属性单值中智集相关度量测的视频运动目标检测方法

视频运动目标检测是视觉传感器数据分析的主要任务之一,其主要用于视频监控.鉴于效率,一些基本的背景模型经常被用于该项任务.然而,当视频图像中存在由于天气、背景扰动等因素带来的噪声时,基于该类模型的动目标检测效果往往会受到严重影响.基于中智集理论提出了一种改进的运动目标检测方法.首先,利用基础背景模型计算得到背景差图像序列;接着提出面向中智集理论的真(Truth)、不确定(Indeterminacy)、假(Falsity)量测,将各背景差图像转换到中智集范畴;最后,综合利用多周期单值中智集相关度量测和一般单值中智集相关度量测强化图像中的运动目标区域,并利用Otsu方法确定最优分割阈值.通过对现实视频序列测试,实验结果表明本文方法能够克服恶劣天气、背景扰动等不良因素,鲁棒完成运动目标提取.     

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.     

HMAP– A temporal data model managing intervals with different granularities and indeterminacy from natural language sentences

The granularity of given temporal information is the level of abstraction at which information is expressed. Different units of measure allow one to represent different granularities. Indeterminacy is often present in temporal information given at different granularities: temporal indeterminacy is related to incomplete knowledge of when the considered fact happened. Focusing on temporal databases, different granularities and indeterminacy have to be considered in expressing valid time, i.e., the time at which the information is true in the modeled reality. In this paper, we propose HMAP (The term is the transliteration of an ancient Greek poetical word meaning “day”.), a temporal data model extending the capability of defining valid times with different granularity and/or with indeterminacy. In HMAP, absolute intervals are explicitly represented by their start,end, and duration: in this way, we can represent valid times as “in December 1998 for five hours”, “from July 1995, for 15 days”, “from March 1997 to October 15, 1997, between 6 and 6:30 p.m.”. HMAP is based on a three-valued logic, for managing uncertainty in temporal relationships. Formulas involving different temporal relationships between intervals, instants, and durations can be defined, allowing one to query the database with different granularities, not necessarily related to that of data. In this paper, we also discuss the complexity of algorithms, allowing us to evaluate HMAP formulas, and show that the formulas can be expressed as constraint networks falling into the class of simple temporal problems, which can be solved in polynomial time. Received 6 August 1998 / Accepted 13 July 2000 Published online: 13 February 2001     

基于加权中智C均值算法的变压器故障诊断模型

为提高变压器故障诊断准确度,提出了一种基于加权中智C均值算法的变压器故障诊断方法。该方法利用基于样本相似度的加权方法对样本特征进行加权,再引入中智理论对样本的分布重新分配,建立起基于加权中智C均值算法的变压器故障诊断模型。研究结果表明,该方法不仅弥补了传统FCM相同权重分配的不足,有效提高了故障诊断的准确率,且诊断结果产生的中智点对故障的变化预测具有重要意义。     

Possibility neutrosophic soft sets and PNS-decision making method

In this paper, we introduce concept of possibility neutrosophic soft set and define some related concepts such as possibility neutrosophic soft subset, possibility neutrosophic soft null set, and possibility neutrosophic soft universal set. Then, based on definitions of n-norm and n-conorm, we define set theoretical operations of possibility neutrosophic soft sets such as union, intersection and complement, and investigate some properties of these operations. We also introduce AND-product and OR-product operations between two possibility neutrosophic soft sets. We propose a decision making method called possibility neutrosophic soft decision making method (PNS-decision making method) which can be applied to the decision making problems involving uncertainty based on AND-product operation. We finally give a numerical example to display application of the method that can be successfully applied to the problems.     

Evaluating statistical difference, equivalence, and indeterminancy using inferential confidence intervals: An integrated alternative method of conducting null hypothesis statistical tests.

Null hypothesis statistical testing (NHST) has been debated extensively but always successfully defended. The technical merits of NHST are not disputed in this article. The widespread misuse of NHST has created a human factors problem that this article intends to ameliorate. This article describes an integrated, alternative inferential confidence interval approach to testing for statistical difference, equivalence, and indeterminacy that is algebraically equivalent to standard NHST procedures and therefore exacts the same evidential standard. The combined numeric and graphic tests of statistical difference, equivalence, and indeterminacy are designed to avoid common interpretive problems associated with NHST procedures. Multiple comparisons, power, sample size, test reliability, effect size, and cause-effect ratio are discussed. A section on the proper interpretation of confidence intervals is followed by a decision rule summary and caveats. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering

Aim to that Neutrosophic C-mean clustering segmentation does not consider the membership distribution of every sample point to different classes. Herein, an image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering is proposed. When the maximum membership value of a sample point is far greater than other membership values, the centre of the class with the maximum membership value is taken as the centre of the fuzzy class. Otherwise, the average value of the centre of the two classes with the highest and second-highest membership values is used as the centre of the fuzzy class. In the preprocessing stage, wavelet technology is used to remove noise from the processed image, and the improved Bayesian algorithm is employed to calculate the filter threshold. The experiment results for synthetic and natural images show that the proposed method is more accurate and effective than the existing methods.     

模糊自适应控制在给水加药系统中的应用

针对火电厂给水加药过程的复杂性、较大迟延性及难以确切描述的非线性,提出将模糊控制与常规PID控制相结合的新思路,设计了自适应模糊PID控制模型,通过现场实际采集的历史数据,对系统进行仿真分析.结果表明,这种新的控制策略可以很好地处理pH中和过程的非线性和滞后性问题.