Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator- and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.

Probabilistic Fuzzy Approach to Assess RDS Vulnerability and Plan Corrective Action Using Feeder Reconfiguration

Two common problems for a typical Power distribution system are voltage collapse & instability. Challenge is to identify the vulnerable nodes and apply the effective corrective actions. This paper presents a probabilistic fuzzy approach to assess the node status and proposes feeder reconfiguration as a method to address the same. Feeder reconfiguration is altering the topological structures of distribution feeders by changing the open/closed states of the sectionalizing and ties switches. The solution is converge using a probabilistic fuzzy modeled solution, which defines the nodal vulnerability index (VI) as a function of node voltage and node voltage stability index and predicts nodes critical to voltage collapse. The information is further used to plan best combination of feeders from each loop in distribution system to be switched out such that the resulting configuration gives the optimal performance i.e. best voltage profile and minimal kW losses. The proposed method is tested on established radial distribution system and results are presented.

Mass Constituents of a Flat Lattice Multiverse: Conclusion from Similarity between Two Universal Numbers, the Rocksalt-Type 2<i>D</i>Madelung Constant and the Golden Mean

In fairly good agreement with the consensus range of dark energy to matter this ratio of the critical density is suggested to be connected with the golden mean &phi;=0.6180339887, yielding for dark energy to matter mass fractions .?Assuming the baryonic matter to be only 4.432%, the ratio of matter to baryonic matter would be , and further the ratio of dark matter to baryonic one . If one subtracts from the dark matter a contribution of antimatter with the same mass of baryonic matter, according to the antigravity theories of Villata respectively Hajdukovic, the remaining mass ratio would yield . Replacing the “Madelung” constant α of Villata’s “lattice universe” by &phi;, one reaches again 1 + &phi;as the ratio of the repulsive mass contribution to the attractive one. Assuming instead of a 3D lattice a flat 2D one of rocksalt type, the numerical similarity between the Madelung constant and φ&minus;1 could not be just coincidence. The proposed scaling of the cosmological mass fractions with the square of the most irrational universal number &phi;may indicate that the chaotic cosmological processes have reached a quite stable equilibrium. This may be confirmed by another, but similar representation of the mass constituents by the Archimedes’ constant &pi;, giving for respectively for the dark components . However, the intimate connection of φ with its reciprocal may ignite the discussion whether our universe is intertwined with another universe or even part of a multiverse with the dark constituents contributed from there.

A Decision Support System for Selection of Solar Power Plant Locations by Applying Fuzzy AHP and TOPSIS: An Empirical Study

The objective of this research is to propose a decision support system for avoiding flood on solar power plant site selection. Methodologically, the geographic information system (GIS) is used to determine the optimum site for a solar power plant. It is intended to integrate the qualitative and quantitative variables based upon the adoption of the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. These methods are employed to unite the environmental aspects and social needs for electrical power systematically. Regarding a case study of the choice of a solar power plant site in Thailand, it demonstrates that the quantitative and qualitative criteria should be realized prior to analysis in the Fuzzy AHP-TOPSIS model. The fuzzy AHP is employed to determine the weights of qualitative and quantitative criteria that can affect the selection process. The adoption of the fuzzy AHP is aimed to model the linguistic unclear, ambiguous, and incomplete knowledge. Additionally, TOPSIS, which is a ranking multi-criteria decision making method, is employed to rank the alternative sites based upon overall efficiency. The contribution of this paper lies in the evolution of a new approach that is flexible and practical to the decision maker, in providing the guidelines for the solar power plant site choices under stakeholder needs: at the same time, the desirable functions are achieved, in avoiding flood, reducing cost, time and causing less environmental impact. The new approach is assessed in the empirical study during major flooding in Thailand during the fourth quarter of 2011 to 2012. The result analysis and sensitivity analysis are also presented.

Fuzzy ARTMAP神经网络在土地覆盖分类中的应用研究

面对数量激增、包含信息日趋复杂的遥感影像,如何快速有效地自动分类已成为遥感领域亟待解决的问题。以TM影像为实例,探讨了FuzzyARTMAP神经网络在土地覆盖分类方面的应用。在总结FuzzyARTMAP网络警戒系数调整方法的基础上,提出了一种新的设置和调整警戒系数的方法。实验结果表明,这种新方法可以解决人为选择警戒系数效率低、难以取得合适数值的问题,并能提高网络的收敛速度和分类精度,结合本文所提算法的FuzzyARTMAP神经网络与最大似然法和传统FuzzyARTMAP网络相比较,训练时间缩短,分类精度有所提高。FuzzyARTMAP网络用于土地覆盖分类研究可以获得相对较好的分类结果。

Kabbalah Logic and Semantic Foundations for a Postmodern Fuzzy Set and Fuzzy Logic Theory

Despite half a century of fuzzy sets and fuzzy logic progress, as fuzzy sets address complex and uncertain information through the lens of human knowledge and subjectivity, more progress is needed in the semantics of fuzzy sets and in exploring the multi-modal aspect of fuzzy logic due to the different cognitive, emotional and behavioral angles of assessing truth. We lay here the foundations of a postmodern fuzzy set and fuzzy logic theory addressing these issues by deconstructing fuzzy truth values and fuzzy set membership functions to re-capture the human knowledge and subjectivity structure in membership function evaluations. We formulate a fractal multi-modal logic of Kabbalah which integrates the cognitive, emotional and behavioral levels of humanistic systems into epistemic and modal, deontic and doxastic and dynamic multi-modal logic. This is done by creating a fractal multi-modal Kabbalah possible worlds semantic frame of Kripke model type. The Kabbalah possible worlds semantic frame integrates together both the multi-modal logic aspects and their Kripke possible worlds model. We will not focus here on modal operators and axiom sets. We constructively define a fractal multi-modal Kabbalistic L-fuzzy set as the central concept of the postmodern fuzzy set theory based on Kabbalah logic and semantics.

Fire monitoring in coal mines using wireless underground sensor network and interval type-2 fuzzy logic controller

From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise characteristics. Therefore, evaluation and inferring the data perfectly to prevent fire related accidental risk in underground coal mining (UMC) system are very necessary. In the present article, we have proposed a novel type-2 fuzzy logic system (T2FLS) for the prediction of fire intensity and its risk assessment for risk reduction in an underground coal mine. Recently, for the observation of underground coal mines, wireless underground sensor network (WUSN) are being concerned frequently. To implement this technique IT2FLS, main functional components are sensor nodes which are installed in coal mines to accumulate different imprecise environmental data like, temperature, relative humidity, different gas concentrations etc. and these are sent to a base station which is connected to the ground observation system through network. In the present context, a WUSN based fire monitoring system is developed using fuzzy logic approach to enhance the consistency in decision making system to improve the risk chances of fire during coal mining. We have taken Mamdani IT2FLS as fuzzy model on coal mine monitoring data to consider real-time decision making (DM). It is predicted from the simulated results that the recommended system is highly acceptable and amenable in the case of fire hazard safety with compared to the wired and off-line monitoring system for UMC. Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression analysis.

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.

板栗生态适宜性的Fuzzy评价模型

生态适宜性在板栗栽培中起着至关重要的作用,但用常规方法对其评价的工作量大,内容复杂。为此,选择对板栗生长发育起关键作用的年均温、年降水和土壤pH值,建立了生态适宜性的Fuzzy评价模型。应用这一模型,对位于贵州省西部的毕节地区板栗栽培的生态适宜性进行综合评价,划分出板栗栽培的最适宜区、适宜区、较适宜区和适宜较差区。

A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

纳米晶PbTiO_3负载CuO催化NO分解

A large specific surface area perovskite-type mixed oxide PbTiO3 supported cupric oxide was synthesized as a catalyst for NO decomposition and characterized by techniques such as XPS, XRD, H2-TPR before and after NO decomposition reactions. The catalytic properties were tested with a fix-bed micro-reactor. The results showed that the PbTiO3 was inactive for the reactions, but 1wt % Cu/PbTiO3 catalyst gave fairly good activities for NO decomposition at temperature as low as 473 K. Copper species were found well-dispersed but weakly interacted with the support before NO decomposition, and the NO decomposition caused significant change in the environment of the copper species, which became Cu(Ⅰ)and most probably incorporated into surface crystal lattice of the nano-sized PbTiO3. In NO reaction, a large amount of oxygen atoms from the decomposition of NO penetrated into the nano-sized PbTiO3 support and caused small expansion of crystal lattice. The transport of oxygen between the copper species and the catalyst support may be helpful to speed up the kinetic regeneration of active metal sites from oxygen occupancy and resulted in good catalytic performance.

Solid Waste Management:A MADM Approach Using Fuzzy Parameterized Possibility Single-Valued Neutrosophic Hypersoft Expert Settings

The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Many scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated ambiguity.The goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site selection.The characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these uncertainties.To tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s method.Following this,an intelligent algorithm is suggested.The steps of the suggested algorithm are explained with an example that explains itself.The compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their viability.This study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,respectively.It uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior literature.It is simpler for the decisionmakers to look at each option separately because the decision is uncertain.Comparing the computed results,it is discovered that they are consistent and dependable because of their preferred properties.

Robust fuzzy sliding-mode control for T-S model based permanent magnet synchronous motor

A fuzzy sliding-mode control (FSMC) scheme based on T-S fuzzy models was proposed for the permanent magnet synchronous motor (PMSM) drive system to solve the speed tracking problem. A T-S fuzzy model was firstly formed to represent the nonlinear system of PMSM. For converting the tracking control into a stabilization problem, a new control design was proposed to define the internal desired states. Then, the FSMC controller for PMSM system with parameter variation and load disturbance was designed based on the fuzzy model. The performance of the proposed controller was verified by experimental results on PMSM system. The results show that the FSMC scheme can drive the dynamics of PMSM into a designated sliding surface in finite time and guarantee the property of asymptotical stability. The information of upper bound of modeling errors as well as perturbations is not required when using the FSMC controller.

Evaluating Watershed Vulnerability in Bernalillo County, New Mexico Using Expert Testimony, Fuzzy Analytic Hierarchy Process, and GIS

Geographic Information System (GIS) software was used to create a watershed vulnerability model for Bernalillo County, New Mexico. Watershed vulnerability was investigated as a function of soil erosion and infiltration criteria: precipitation, land slope, soil erodibility (K-factor), vegetation cover (NDVI), land use, drainage density, saturated hydraulic conductivity, and hydrologic soil group. Respective criteria weights were derived using a Fuzzy Analytic Hierarchy Process (FAHP) supported by expert opinion. A survey of 10 experts, representing New Mexico Institute of Mining and Technology (NMT), the New Mexico Bureau of Geology and Mineral Resources (NMBGMR), and the United States Geologic Survey (USGS), provided model input data for an integrated pair-wise comparison matrix for soil erosion and for infiltration. Individual criteria weights were determined by decomposing the respective fuzzy synthetic extent matrix using the centroid method. GIS layers were then combined based on criteria weights to produce maps of soil erosion potential and infiltration potential. A composite watershed vulnerability map was generated by equal weighting of each input map. Model results were categorized into five vulnerability categories: not vulnerable (N), slightly vulnerable (SV), moderately vulnerable (MV), highly vulnerable (HV), and extremely vulnerable (EV). The resulting FAHP/GIS model was used to generate a watershed vulnerability map of discrete areas in Bernalillo County, which may be vulnerable to stormwater run-off events and soil erosion. Such high volume run-off events can cause erosion damage to property and infrastructure. Alternatively, in areas near urban development, stormwater run-off may contribute non-point-source pollutant contamination of New Mexico’s surface water resources. The most problematic areas in Bernalillo County are present in the Eastern and Northwestern portions. However, less than 1% of the total area lies within the lowest and highest vulnerability categories with the majority centered around moderate vulnerability. The results of the model were compared with a previously published crisp AHP method. Both methods showed similar regional vulnerability trends. This MCDS/GIS approach is intended to provide support to local governments and decision makers in selection of suitable structural or nonstructural stormwater control measures.

Fuzzy收益率的提取方法及实际应用

Markowitz的均值—方差模型作为现代组合投资理论的起点。它的投资组合理论是要求风险资产的市场价格客观上服从某一联合分布,而且分布的数字特征是不变的;并要求投资者能够掌握大量的资料和历史数据,能准确的计算出所有风险资产之间的协方差。然而以往诸多研究投资分析的模型中,往往以概率论方法来处理投资中的不确定性,而忽视了另一种不确定性—模糊性。事实上,模糊性也是金融市场不确定性中的一个重要表征。当分析投资行为时,我们很难确切地知道未来的资金流、成熟期或者收益率等,但是这些因素均被广泛应用于投资范围内。我们认为用模糊理论来处理信息会比用概率论来处理更为恰当。目前,国内外有些学者已经开始从模糊性的角度去研究组合投资决策的问题,并形成了一定的理论基础取得了丰硕的成果。其中对于证券模糊收益率的提取,前人给出的了三种方法,分别是区间判断及其综合集成、模糊判断的平均数综合方法、历史数据与专家经验相结合的获取方法,这些在数学模型上、实际应用效果上都存在较大的差异,本文就是针对模糊判断的平均数综合法进行实证分析,证明其有效性。

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

A novel similarity measure between intuitionistic fuzzy sets based on the mid points of transformed triangular fuzzy numbers with applications to pattern recognition and medical diagnosis

Similarity measure is an essential tool to compare and determine the degree of similarity between intuitionistic fuzzy sets (IFSs). In this paper, a new similarity measure between intuitionistic fuzzy sets based on the mid points of transformed triangular fuzzy numbers is proposed. The proposed similarity measure provides reasonable results not only for the sets available in the literature but also gives very reasonable results, especially for fuzzy sets as well as for most intuitionistic fuzzy sets. To provide supportive evidence, the proposed similarity measure is tested on certain sets available in literature and is also applied to pattern recognition and medical diagnosis problems. It is observed that the proposed similarity measure provides a very intuitive quantification.

Fuzzy控制在浮选回路中应用的研究

作者们以一个浮选模拟器作为假想的Fuzzy控制目标,研究发展了一种Fuzzy浮选控制器。当使用Monte-Carlo法编制的伪随机数发生器给模拟器以干扰后,模拟器的指标产生一个偏差及偏差微分。Fuzzy控制器据此对模拟器给出控制指令从而使模拟器的指标恢复到设定值。一些模拟控制运行表明,Fuzzy控制有可能成为有潜在能力的浮选回路控制方法。

Automatic Detection Instrument for the Ratio of Chlorine to Bromine in Oxidized Liquid of Bromine Production Based on Fuzzy Method

In the process of bromine production,because of lag adjustment methods,there are problems of adjusting delay,raw material wastage and low growth rate.By considering the nature of bromine production,with the help of fuzzy data processing method,computer detection and display technique,we designed an automatic detection instrument for the ratio of chlorine to bromine in oxidized liquid of bromine production.This instrument can be used to detect the different parameters of raw materials adjustment and control in real time,and afford assurance that raw materials will be adjusted in time.This paper briefly introduces the working mechanism,hardware and software design of the instrument.

Comparative Study of Conventional, Fuzzy Logic and Neural PID Speed Controllers with Torque Ripple Minimization for an Axial Magnetic Flux Switched Reluctance Motor

Three speed controllers for an axial magnetic flux switched reluctance motor with only one stator, are described and experimentally tested. As it is known, when current pulses are imposed in their windings, high ripple torque is obtained. In order to reduce this ripple, a control strategy with modified current shapes is proposed. A workbench consisting of a machine prototype and the control system based on a microcontroller was built. These controllers were: a conventional PID, a fuzzy logic PID and a neural PID type. From experimental results, the effective reduction of the torque ripple was confirmed and the performance of the controllers was compared.