An integrated recycling approach for GFRP pultrusion wastes: recycling and reuse assessment into new composite materials using Fuzzy Boolean Nets

In this study, efforts were made in order to put forward an integrated recycling approach for the thermoset based glass fibre reinforced polymer (GPRP) rejects derived from the pultrusion manufacturing industry. Both the recycling process and the development of a new cost-effective end-use application for the recyclates were considered. For this purpose, i) among the several available recycling techniques for thermoset based composite materials, the most suitable one for the envisaged application was selected (mechanical recycling); and ii) an experimental work was carried out in order to assess the added-value of the obtained recyclates as aggregates and reinforcement replacements into concrete-polymer composite materials. Potential recycling solution was assessed by mechanical behaviour of resultant GFRP waste modified concrete-polymer composites with regard to unmodified materials. In the mix design process of the new GFRP waste based composite material, the recyclate content and size grade, and the effect of the incorporation of an adhesion promoter were considered as material factors and systematically tested between reasonable ranges. The optimization process of the modified formulations was supported by the Fuzzy Boolean Nets methodology, which allowed finding the best balance between material parameters that maximizes both flexural and compressive strengths of final composite. Comparing to related end-use applications of GFRP wastes in cementitious based concrete materials, the proposed solution overcome some of the problems found, namely the possible incompatibilities arisen from alkalis-silica reaction and the decrease in the mechanical properties due to high water-cement ratio required to achieve the desirable workability. Obtained results were very promising towards a global cost-effective waste management solution for GFRP industrial wastes and end-of-life products that will lead to a more sustainable composite materials industry.

Prescribed burning impact on forest soil properties – a fuzzy boolean nets approach

The Portuguese northern forests are often and severely affected by wildfires during the Summer season. These occurrences significantly affect and negatively impact all ecosystems, namely soil, fauna and flora. In order to reduce the occurrences of natural wildfires, some measures to control the availability of fuel mass are regularly implemented. Those preventive actions concern mainly prescribed burnings and vegetation pruning. This work reports on the impact of a prescribed burning on several forest soil properties, namely pH, soil moisture, organic matter content and iron content, by monitoring the soil self-recovery capabilities during a one year span. The experiments were carried out in soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, Portugal, which was kept intact from prescribed burnings during a period of four years. Soil samples were collected from five plots at three different layers (0–3, 3–6 and 6–18) 1 day before prescribed fire and at regular intervals after the prescribed fire. This paper presents an approach where Fuzzy Boolean Nets (FBN) and Fuzzy reasoning are used to extract qualitative knowledge regarding the effect of prescribed fire burning on soil properties. FBN were chosen due to the scarcity on available quantitative data. The results showed that soil properties were affected by prescribed burning practice and were unable to recover their initial values after one year.

A qualitative description of soil parameters variation due to a prescribed fire in Portuguese northwestern forests using Fuzzy Boolean Nets — The case study of Cabreira mountain

In this paper we study the modifications that occurred in some forest soil properties after a prescribed fire. The research focused on the alterations of soil pH, soil moisture and soil organic matter content during a two-year span, from 2008 to 2009. The study site is located in Anjos, Vieira do Minho municipality, a forest site that has suffered from recurrent wildfires for several decades. Furze (Ulex, sp.), broom (Cytisus, sp.), gorse (Chamaespartum tridentatum) and a very few disperse adult pine (Pinus sylvestris) are the predominant vegetation type in the study area. The average height of this shrub vegetation is around 1.5 m. The prescribed fire was conducted by the National Forestry Authority (AFN) in November 2008. Fuzzy Boolean Nets (FBN) were used to evaluate the alteration in soil parameters when compared with adjacent spots where: i) no fire occurrence was registered since 1998; ii) fire occurrence was registered in 2008; and iii) vegetation pruning by mechanical cut was done in Spring six months prior to the prescribed fire event. Results suggest that in the particular case of the studied site, Anjos, the observed soil properties alterations cannot be related with the prescribed fire.

Fuzzy Boolean nets investigation of the alteration of some physical forest soil properties due to a prescribed fire action

Portuguese northern forests are often and severely affected by wildfires during the summer season. Some preventive actions, such as prescribed (or controlled) burnings and clear-cut logging, are often used as a measure to reduce the occurrences of wildfires. In the particular case of Serra da Cabreira forest, due to extremely difficulties in operational field work, the prescribed (or controlled) burning technique is the the most common preventive action used to reduce the existing fuel load amount. This paper focuses on a Fuzzy Boolean Nets analysis of the changes in some forest soil properties, namely pH, moisture and organic matter content, after a controlled fire, and on the difficulties found during the sampling process and how they were overcome. The monitoring process was conducted during a three-month period in Anjos, Vieira do Minho, Portugal, an area located in a contact zone between a two-mica coarse-grained porphyritic granite and a biotite with plagioclase granite. The sampling sites were located in a spot dominated by quartzphyllite with quartz veins whose bedrock is partially altered and covered by slightly thick humus, which maintains low undergrowth vegetation.

Fuzzy modelling of prescribed burning effects on soil physical properties

This paper presents the preliminary work of an approach where Fuzzy Boolean Nets (FBN) are being used to extract qualitative knowledge regarding the effect of prescribed fire burning on soil chemical physical properties. FBN were chosen due to the scarcity on available quantitative data.

Modeling and control of flatness in cold rolling mill using fuzzy petri nets

Today, having a good flatness control in steel industry is essential to ensure an overall product quality, productivity and successful processing. Flatness error, given as difference between measured strip flatness and target curve, can be minimized by modifying roll gap with various control functions. In most practical systems, knowing the definition of the model in order to have an acceptable control is essential. In this paper, a fuzzy Petri net method for modeling and control of flatness in cold rolling mill is developed. The method combines the concepts of Petri net and fuzzy control theories. It focuses on the fuzzy decision making problems of the fuzzy rule tree structures. The method is able to detect and recover possible errors that can occur in the fuzzy rule of the knowledge-based system. The method is implemented and simulated. The results show that its error is less than that of a PI conventional controller.

Improved Kalman filter initialisation using neurofuzzy estimation

It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs, but unfortunately their performance is extremely sensitive to state initialisation accuracy: good initial state estimates ensure fast convergence whereas poor estimates may give rise to slow convergence or even filter divergence. Divergence is generally due to excessive observation noise and leads to error magnitudes that quickly become unbounded (R.J. Fitzgerald, 1971). When a filter diverges, it must be re initialised but because the observations are extremely poor, re initialised states will have poor estimates. The paper proposes that if neurofuzzy estimators produce more accurate state estimates than those calculated from the observed noisy inputs (using the known state model), then neurofuzzy estimates can be used to initialise the states of Kalman and extended Kalman filters. Filters whose states have been initialised with neurofuzzy estimates should give improved performance by way of faster convergence when the filter is initialised, and when a filter is re started after divergence

模糊Petri网自动生成方法研究

为解决模糊Petri网建模效率低、工作量大、易出错等问题,提出了模糊产生式规则自动生成模糊Petri网的方法,并给出了其映射模型。该方法通过模型映射,结合图元生成与定位实现了模糊Petri网的自动建模。避免了模糊Petri网建模的人为失误,提高了建模效率。使知识库与模型库同步更新,保证二者的一致性。有利于充分发挥模糊Petri网的知识表示、模糊信息处理与动态并行推理的优势,对模糊Petri网理论的广泛应用具有推动作用。通过实例表明该方法是可行的。

基于模糊统计的模糊Petri网token确定方法

在模糊Petri网应用研究中,普遍存在模糊token由专家直接给出或主观假定的问题。基于这种情况,提出了通过模糊统计法来获得库所的模糊token,为成功应用模糊Petri网理论创造了条件。给出了计算模糊token的通用形式化算法。实例论证了模糊统计法在求取模糊token时的可行性与有效性。

一种模糊petri网仿真工具的设计实现

文章将面向对象思想应用于模糊Petri网仿真工具的设计和实现过程,探讨了模糊Petri网建模与仿真的可视化问题,提出了基于网格可视化技术解决方案及具体实现方法。通过对变速箱的自动建模和诊断仿真实例,证明该系统具有良好的实用性,为模糊Petri网理论的普遍应用提供了工具平台。

面向复杂制造过程质量的模糊Petri网预测及诊断方法研究

在复杂制造过程中，存在质量异常预测及诊断能力弱、智能化程度低、效率低等问题。如何针对制造过程质量问题特点采用合适的预测与诊断方法，满足日益提高的过程自动化水平的要求，是该领域研究人员面临重要的亟待解决的问题。 由于模糊Petri网是模糊集理论与Petri网理论有机结合的一种网络理论，其突出优势在于知识表示、推理和处理模糊信息的能力; 目前，尽管模糊Petri网已有成功的应用案例，但仍存在某些不足，需不断地改进与完善。因此，对模糊Petri网理论方法的研究，有利于提高其知识表示能力、模糊动态推理能力、推理结果可靠性与准确性等，对模糊Petri网理论的广泛应用具有推动作用。 本文以制造过程质量问题的预测与诊断为研究和应用背景，对模糊Petri网预测与诊断方法的研究为主线，以研发的系统为辅助分析工具，重点从方法的层面上对模糊Petri网理论进行了研究和探讨。旨在进一步完善模糊Petri网相关理论，并应用于制造过程质量问题的解决，提高过程的质量监控能力、事故预防能力、缩短故障原因查找周期、提高定位准确性及可靠性奠定方法基础。 针对制造过程质量预测与诊断问题特点，在广泛阅读相关文献并深入探索的基础上，对模糊Petri网理论方法进行了较深入的研究和探讨，重点解决了以下问题： 1）模糊Petri网自动建模方法：对模糊Petri网理论研究的基础和前提是建立模糊Petri网模型。为解决当前模糊Petri网建模效率低、工作量大、易出错等问题，本文提出了模糊Petri网的自动建模方法。该方法的提出，易于保证知识库与模型库更新的同步和一致，提高了建模效率，避免了建模的人为失误。 2) 模糊Petri网参数确定：模型建立后，为实现可靠有效地推理，需进行相关参数的确定。提出了确定模糊Petri网的初始库所token的方法。通过模糊统计的方法来获得模糊token，减少确定token时的主观臆断性和不一致性，为物理量与模糊token的实时转换提供了技术支持。由于构建符合客观实际的、连续的隶属函数是确定模糊token的前提条件，本文提出采用最小二乘拟合来构造模糊隶属函数方法。该方法简单，拟合能力强，人工干预少。由于变迁阈值影响推理的正确性及可靠性，这里对阈值设定进行了初步探讨。阈值设定越高，预测及诊断的漏报率越高；反之，误报率越高。给出了阈值设定的总代价计算式，阈值选择的目标是使总代价最小。 在建立了模糊Petri网模型、确定了相关参数后，便可对异常事件进行预测及诊断推理。 3）模糊Petri网预测方法：对预测模式进行了分类与定义，便于对不同模式下进行预测分析。提出了改进的FPN四种基本推理模型，通过禁止弧的引入，避免了激发过的变迁反复被激发，减少不必要的计算，实现了推理与模型结构的一致性。从而提高了推理效率和基于规则系统的响应能力。 4）模糊Petri网诊断方法：给出了一种模糊Petri网诊断推理方法。该方法充分利用模糊Petri网自身的结构与数学特性这一突出优势，实现了并行推理。以矢量计算方式获得中间库所能力，取代了常规的搜索方式，提高了推理效率。通过引入人机交互的处理策略，减少了模糊Petri网的复杂性及规模。指出在实践中，推理方法的效率、成本及实际的应用效果， 在重要性方面，要远大于方法自身的运算效率。 5）模糊着色Petri网推理方法：在建模复杂大型系统时，为解决模糊Petri网存在模型空间过大，模型数据结构松散等问题，提出了FCPN并行推理方法及FPR与FCPN模型转换算法。提出的FCPN与现有方法的主要区别在以下方面：首先，算法实现变迁的单次激发，避免推理激发变迁的重复计算。其次，某个使能变迁前集库所中token在该变迁激发后并不移除，符合实际推理情况。此外，通过输入/输出关联矩阵计算迭代，实现了并行推理。 最后，以一典型制造过程—埋弧自动焊接过程质量问题的预测和诊断为例，来说明模糊Petri网方法的实际应用。通过系统的实现，验证了相应方法是可行的。通过模糊Petri网的预测及诊断推理，便于实现质量异常的分析、预警、处理、过程控制及数字化管理，为生产策略的调整、纠正措施的采取提供了决策依据，加快了系统响应速度。 本文研究工作重点围绕模糊Petri网理论方法展开，虽以制造过程质量问题的预测与诊断为研究和应用背景，但并不局限于该领域，是属于具有一般性的共性方法。因此，所开展的方法研究工作具有良好的科研价值和广泛的应用前景。

Combination of decisions from a multiple neural network classifier system

In this paper, a study of the effectiveness of a multiple classifier system (MCS) in a medical diagnostic task is described. A hybrid network, based on the integration of a fuzzy ARTMAP and the probabilistic neural network, is employed as the basis of the MCS. Outputs from multiple networks are combined using some decision combination method to reach a final prediction. By using a real medical database, a set of experiments has been conducted to evaluate the performance of the MSC with different network configurations. The experimental results reveal the potential of the MCS as a useful decision support tool in the medical field.

Fuzzy concepts and formal methods

It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Specification languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the information systems problem domain, may be difficult to model in crisp or precise terms. It may also be desirable that formal modelling should commence as early as possible, even when our understanding of parts of the problem domain is only approximate. This thesis suggests fuzzy set theory as a possible representation scheme for this imprecision or approximation. A fuzzy logic toolkit that defines the operators, measures and modifiers necessary for the manipulation of fuzzy sets and relations is developed. The toolkit contains a detailed set of laws that demonstrate the properties of the definitions when applied to partial set membership. It also provides a set of laws that establishes an isomorphism between the toolkit notation and that of conventional Z when applied to boolean sets and relations. The thesis also illustrates how the fuzzy logic toolkit can be applied in the problem domains of interest. Several examples are presented and discussed including the representation of imprecise concepts as fuzzy sets and relations, system requirements as a series of linguistically quantified propositions, the modelling of conflict and agreement in terms of fuzzy sets and the partial specification of a fuzzy expert system. The thesis concludes with a consideration of potential areas for future research arising from the work presented here.

Optimized fuzzy association rule mining for quantitative data

With the advance of computing and electronic technology, quantitative data, for example, continuous data (i.e., sequences of floating point numbers), become vital and have wide applications, such as for analysis of sensor data streams and financial data streams. However, existing association rule mining generally discover association rules from discrete variables, such as boolean data (`O' and `l') and categorical data (`sunny', `cloudy', `rainy', etc.) but very few deal with quantitative data. In this paper, a novel optimized fuzzy association rule mining (OFARM) method is proposed to mine association rules from quantitative data. The advantages of the proposed algorithm are in three folds: 1) propose a novel method to add the smoothness and flexibility of membership function for fuzzy sets; 2) optimize the fuzzy sets and their partition points with multiple objective functions after categorizing the quantitative data; and 3) design a two-level iteration to filter frequent-item-sets and fuzzy association-rules. The new method is verified by three different data sets, and the results have demonstrated the effectiveness and potentials of the developed scheme.