Fuzzy Coding Research Articles

A multiscale assessment protocol to quantify effects of restoration works on alluvial vegetation communities

Vegetation mapping is a legal obligation in environmental monitoring prompting the need for easy-to-read methods of quantifying changes in vegetation dynamics. Transition matrix modelling provides an alternative approach to qualitative assessment promoting quantification for revealing current complex changes effects on the trajectory of ecosystems. Transition matrix models (TMMs) and two newly developed metrics, the pixel change (PCI) and zonal change (ZCI) indices, were combined into a methodological scheme that provides a multiscale assessment protocol. This protocol was applied during a field study along the Old Rhine River in order to assess complex shifts in alluvial vegetation communities in relation to restoration works and natural processes. The restoration works aim to restore lateral mobility through instream flow increase and controlled bank erosion with artificial groynes implementation. Characterisation of spatial and temporal pathways was carried out using a ‘before-after control-impact’ (BACI) design, and a fuzzy coding approach has shed further light on shifts in aquatic vegetation functional traits. The multiscale assessment protocol highlighted (i) an increase in alluvial habitat types, including habitats of European concern (Natura 2000), and (ii) low time-scale aquatic vegetation recovery. Both ZCI and PCI recorded high values along the restored section with controlled bank erosion, indicating strong ecosystem change. Use of functional traits detected three requirements for the successful establishment of aquatic vegetation in the restored river section, i.e. high degree of flexibility, flow variation tolerance, and fine-sediment adaptability. Subject to the reliability and availability of vegetation mapping, the method opens the possibility of an efficient tool for precisely monitoring alluvial vegetation communities and identifying pathways. It also discriminates event effects, e.g. natural process effects vs. human-induced effects. At full potential, such a protocol may reveal community responses to disturbance during conservation, restoration and management decision-making projects.

Assessment of the Importance of Parameters Affecting Roof Strata Cavability in Mechanized Longwall Mining

This paper was assesses the cavability of roof strata in mechanized longwall coal mining in terms of their principle parameters. For this purpose, the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method was employed to study and analysis the influencing parameters and interrelationships among them. Since this method is based on the survey of experts to indicate the influences and involves uncertainty, therefore, the fuzzy coding was used. Given that the behavior of roof strata in the caving process is controlled by many factors, seventeen effective parameters in five categories were recognized. As a result of analysis, the parameters with the highest exerted and received influence were introduced. Besides, the impact and relation of factors in the cavability system were determined. After that, an approach was proposed for weighting the parameters in the fuzzy DEMATEL framework and then the importance of significant parameters were assessed. The conducted method provides a systematic approach for holistic analysis the principles parameters affecting the cavability of immediate roof strata in mechanized longwall coal mining and hence, is worthwhile for studying this complex and dynamic problem under uncertainty.

Robust Principal Component Analysis based on Fuzzy Coded Data

In the presence of outlier s in the dataset, the principal component analysis method, like many of the classical statistical methods, is severely affected. For this reason, if there are outliers in dataset, researchers tend to use alternative methods. Use of fuzzy and robust approaches is the leading choice among these methods. In this study, a new approach to robust fuzzy principal component analysis is proposed. This approach combines the power of both robust and fuzzy methods at the same time and collects these two approaches under the framework of principal component analysis. The performance of proposed approach called robust principal component analysis based on fuzzy coded data is examined through a set of artificial dataset that are generated by considering three different scenarios and a real dataset to observe how it is affected by the increase in sample size and changes in the rate of outliers. In light of the study's findings, it is seen that the proposed approach gives better results than the ones in the classical and robust principal component analysis in the presence of outliers in dataset.

A Fast Seamless Acquisition Algorithm for the CL Code of the L2C Navigation Signal

A new algorithm is proposed for the CL code of the L2C navigation signal. We eliminate the fuzzy search range problem by the segmentation process and superposition process, and we eliminate the fuzzy code phase problem by the parameter adjustment process. It can reduce the ambiguity threat in the acquisition process and that it can adapt to lower SNR. In addition, it outperforms the existing algorithms in acquisition precision, adaptability to SNR, processing speed and complexity.

FUZZY CODE ON RNA SECONDARY STRUCTURE

FUZZY CODE ON RNA SECONDARY STRUCTURE

Optimum Solar Panel Implementation Using DC-DC Boost Converter Controlled by Fuzzy Logic Controller

This paper proposes the implementation of a simple fuzzy logic controller (FLC) for a DC-DC boost converter based on a microcontroller to obtain maximum power from the solar system with the maximum power point tracking (MPPT) method. The system includes a solar panel, DC-DC boost converter, the fuzzy logic controller implemented on Arduino Uno for controlling on/off time of MOSFET of the boost converter, voltage divider and optocoupler circuit. This paper presents a fuzzy logic real time code in the Arduino language for ATmega328 microcontroller on the Arduino UNO board. The designed system increases the efficiency of the solar panel based on experimental results.

Design of a Code-Maker Translator Assistive Input Device with a Contest Fuzzy Recognition Algorithm for the Severely Disabled

This study developed an assistive system for the severe physical disabilities, named “code-maker translator assistive input device” which utilizes a contest fuzzy recognition algorithm and Morse codes encoding to provide the keyboard and mouse functions for users to access a standard personal computer, smartphone, and tablet PC. This assistive input device has seven features that are small size, easy installing, modular design, simple maintenance, functionality, very flexible input interface selection, and scalability of system functions, when this device combined with the computer applications software or APP programs. The users with severe physical disabilities can use this device to operate the various functions of computer, smartphone, and tablet PCs, such as sending e-mail, Internet browsing, playing games, and controlling home appliances. A patient with a brain artery malformation participated in this study. The analysis result showed that the subject could make himself familiar with operating of the long/short tone of Morse code in one month. In the future, we hope this system can help more people in need.

Password hardened fuzzy vault for fingerprint authentication system

The present work attempts to build a bio-cryptographic system that combines transformed minutiae pairwise feature and user-generated password fuzzy vault. The fingerprint fuzzy vault is based on a new minutiae pairwise structure, which overcomes the fingerprint feature publication while the secret binary vault code is generated according to the fingerprint fuzzy vault result. The authentication process involves two stages: fuzzy vault matching and secret vault code validation. Our minutiae pairwise transformation produces different templates thus resolving the problem of cross matching attacks in fingerprint fuzzy vault. So, the original fingerprint template cannot be recreated because it is protected by the key generated from the user password. In addition, the proposed bio-cryptographic system ensures an acceptable security level for user authentication.

Transformer Fault Diagnosis Method Based on Information Fusion

According to the characteristics and current situation of power transformer fault diagnosis , information fusion technology is introduced into this field of fault diagnosis of power transformer. By studying the general framework of information fusion fault diagnosis process, combined with the dissolved gas and electrical tests data,it is proposed a fault diagnosis method of information fusion which is based on fuzzy coding boundary and Bias regularization Levenberg Marquardt (LM) network. The algorithm uses a Bias approach to determine the hyper parameters, making the neural network adaptively adjust the parameter in the training process and getting the optimization parameters of the objective function . Combined with fuzzy coding boundary for feature attribute reduction to improve the accuracy of fault diagnosis .The contrast analysis of twice fusion results shows that the fault diagnosis model of the transformer accurate rate is 89.83%. Finally , it proves the validity and practicability of this new diagnosis method. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4054

Assessing Sustainability Performance of Polymer Processing: Case Study of Hollow Fiber Membrane

Sustainability nowadays has become new evolution of quality and efficiency indicator for product and process. Key performance of certain product or process need to be quantified throughout it is life cycle. Polymer processing is one of the chemical processes that need attention in term of sustainability. Hence this paper presents the development of sustainability framework for assessing the sustainability performance of hollow fiber membrane module by considering the reaction of each raw material and process involved. Assessment was carried out by applying fuzzy logic approach by developing its linguistic variables and fuzzy code according the requirement. A framework to assess the sustainability performance for hollow fiber membrane processing was proposed in order to identify its sustainability score for each sustainability element; environmental, economical and social.

Characterizations of semi-groups that are semi-lattices of left groups

Zadeh in 1965 introduce the concept of a fuzzy set which now have a wide range of applications in various fields of Engineering Sciences, Computer Science and Management Sciences. It is worth mentioning here that for applications of fuzzy sets mostly associative algebraic structures are used such as in 2003, Mordeson, Malik and Kuroki have discovered the applications of fuzzy semigroups in fuzzy coding, fuzzy finite-state machines and fuzzy languages. We have applied fuzzy set theory to associative algebraic structures and have discussed related properties. Specifically we have characterized semigroups which are semilattices of left groups by the properties of generalized fuzzy ideals. Key words: -fuzzy ideals, -fuzzy bi-ideals, -fuzzy quasi ideals.

Fuzzy coding in constrained ordinations

Canonical correspondence analysis and redundancy analysis are two methods of constrained ordination regularly used in the analysis of ecological data when ordinations based on several response variables (for example, species abundances) are related linearly to several explanatory variables (for example, environmental variables, spatial positions of samples). In this report I demonstrate the advantages of the fuzzy coding of explanatory variables: first, nonlinear relationships can be diagnosed; second, more variance in the responses can be explained; and third, in the presence of categorical explanatory variables (for example, years, regions) the interpretation of the resulting triplot ordination is unified because all explanatory variables are measured at a categorical level.

Fuzzy Mean Point Clustering using K-means algorithm for implementing the movecentroid function code

The paper focus on combination of K-Means algorithm for Fuzzy Mean Point Clustering Neural Network (FMPCNN). The algorithm is implemented in JAVA program code for implementing the movecentroid function code into FMPCNN. Here we have provided movecentroid’s output to Fuzzy clustering as criteria, movecentroid is the base function of K-means algorithm as in Fuzzy Mean Point Clustering Neural Network (FMPCNN) algorithm, calculation of cluster based on pre-defined criteria and scope is done. In the experiment we have used four datasets and observed results in nano seconds there is huge difference in output as time is reduced for Fuzzy Min-Max code execution of fuzzy calculations of clustering.

Biological traits analyses in the study of pollution gradients and ecological functioning of marine soft bottom species assemblages in a fjord ecosystem

In the present study, biological traits analysis (BTA) was used to explore and characterise effects of pollution on functional attributes of soft bottom infaunal species assemblages. The data comprised 38 sampling stations in the Oslofjord, Norway, ranging from heavily polluted to minimally impacted areas. At each station, species composition (113 taxa in total), contaminants (cadmium, mercury, lead, DDT, PCB) and sediment parameters were determined. Species functions were analysed for eight biological traits defined for activity and life history features. Traits were scored according to the fuzzy coding technique. The most distinct patterns were shown for mobility, size, sediment dwelling depth, feeding type and larval development in relation to contaminants, sediment physical structure and sediment oxidation status. At high levels of contaminants, particularly cadmium, features such as shallow sediment dwelling depth, small size, subsurface deposit feeding and lecitotroph larval development prevailed, while at low contaminant levels characteristic features included deeper sediment dwelling depth, larger size, surface deposit feeding and permanent attachment. Deep sediment dwelling depth (>15cm) was related to minimally contaminated oxidised sediments at greater water depths. Mobility and carnivorous feeding prevailed in coarser sediments. The study showed that BTA detected and depicted specific features that correlated with gradients in pollution and may be important for sediment reworking and nutrient cycling. As part of the present work, trait information for >500 macrofaunal taxa have been assembled and entered in a comprehensive database.

A study of fuzzy Abel-Grassmann's groupoids

Fuzzy sets were first introduced by Zadeh in 1965 which have a wide range of applications in various fields of Engineering Sciences, Computer Science and Management Sciences. It is worth mentioning here that for applications of fuzzy sets mostly associative algebraic structures are used such as in 2003, Mordeson, Malik and Kuroki discovered the applications of fuzzy semigroups in fuzzy coding, fuzzy finite-state machines and fuzzy languages. Our aim in this paper is to apply fuzzy sets to non-associative algebraic structures and to develop some new related properties. Specifically we have characterized intra-regular Abel-Grassmann’s groupoids using generalized fuzzy bi-ideals. Key words: Abel-Grassman's groupoid (AG-groupoid), intra-regular, fuzzy sets, fuzzy AG-groupoids, -fuzzy ideals.

Dynamic evolution of the genetic search region through fuzzy coding

A technique for automatic exploration of the genetic search region through fuzzy coding (Sharma and Irwin, 2003) has been proposed. Fuzzy coding (FC) provides the value of a variable on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree-of-membership. It is an indirect encoding method and has been shown to perform better than other conventional binary, Gray and floating-point encoding methods. However, the static range of the membership functions is a major problem in fuzzy coding, resulting in longer times to arrive at an optimum solution in large or complicated search spaces.This paper proposes a new algorithm, called fuzzy coding with a dynamic range (FCDR), which dynamically allocates the range of the variables to evolve an effective search region, thereby achieving faster convergence. Results are presented for two benchmark optimisation problems, and also for a case study involving neural identification of a highly non-linear pH neutralisation process from experimental data. It is shown that dynamic exploration of the genetic search region is effective for parameter optimisation in problems where the search space is complicated.

A large-scale model of the three first stages of the mammalian olfactory system implemented with spiking neurons

In this work we present a large-scale three stage model of the early mammalian olfactory system, including the olfactory epithelium (OE), the olfactory bulb (OB) and the olfactory (piriform) cortex (OC). All neurons in the network are modeled with a single or few compartments using the Hodgkin-Huxley formalism and are implemented in the NEURON simulator for parallel execution [1,2]. We investigate the dynamics of the network response to odorants and its performance in odor classification experiments. The OE model comprises families of olfactory receptor neurons (ORNs) with different sensitivities, each family expressing one type of olfactory receptor (OR) with a vector of affinity values for each ligand [3]. These different ORN families connect to distinct glomeruli and mitral cells (MT) according to a hypothesized wiring scheme to form a fuzzy interval code for odorant concentration in the OB, i.e. each MT cell responds within a certain range of odorant concentration and these ranges overlap for different MT cells within one glomerulus. Mitral cells in different glomeruli respond independently to an odourant forming a sparse and distributed code in the OB, i.e. only a fraction of MT cells in different glomeruli is active when an odorant is present. The OC is modeled by a modular attractor network of pyramidal cells and inhibitory interneurons [4]. The OB response patterns are used to self-organize a projection to the OC based on learning algorithms employing ideas from machine learning (mutual information between the MT responses, multi-dimensional scaling, vector quantization and Hebbian learning) [5,6]. As a result, odourant responses are represented by a sparse distributed code in the OC. Results from runs with network sizes comprising thousands of model neurons show that this biophysically plausible network model generates response patterns of cells reminding of their real counterparts (see Figure ​Figure1),1), produces attractor dynamics in the olfactory cortex, and is able to discriminate between the different trained odors. We investigate effects of the model size, backprojections from OC to OB, study the performance of the model in discriminating mixtures of odorants and compare Calcium concentration in the olfactory cortex with experimental measurements [7]. Figure 1 Schematic of the model (A) and sample membrane potentials of a pyramidal cell (B), a mitral cell (C) and an olfactory receptor neuron (D)

Biplots of fuzzy coded data

A biplot, which is the multivariate generalization of the two-variable scatterplot, can be used to visualize the results of many multivariate techniques, especially those that are based on the singular value decomposition. We consider data sets consisting of continuous-scale measurements, their fuzzy coding and the biplots that visualize them, using a fuzzy version of multiple correspondence analysis. Of special interest is the way quality of fit of the biplot is measured, since it is well known that regular (i.e., crisp) multiple correspondence analysis seriously under-estimates this measure. We show how the results of fuzzy multiple correspondence analysis can be defuzzified to obtain estimated values of the original data, and prove that this implies an orthogonal decomposition of variance. This permits a measure-of-fit to be calculated in the familiar form of a percentage of explained variance, which is directly comparable to the corresponding fit measure used in principal component analysis of the original data. The approach is motivated initially by its application to a simulated data set, showing how the fuzzy approach can lead to diagnosing nonlinear relationships, and finally it is applied to a real set of meteorological data.

Assessing pollution of toxic sediment in streams using bio‐ecological traits of benthic macroinvertebrates

Summary1. We examined the accuracy of a multimetric approach based on 22 biological and ecological traits of benthic macroinvertebrate communities to assess toxic sediment pollution in streams.2. Faunal and chemical data from sites located on 150 medium‐sized mountain streams in France were analysed. We used 18 additional sites, not included in the multimetric tool development as a test data set. A toxic quality class (from ‘high’ to ‘poor/bad’) was pre‐assigned to each site using the French water quality assessment system (SEQ‐Eau) based on toxic substances (metals, PAH and PCB) in sediment.3. Each trait was described in multiple categories. The affinity of macroinvertebrate taxa for the different categories of a trait was described using a fuzzy coding procedure. The relative abundance of trait categories was calculated for communities at each site.4. A nonparametric multiple comparison statistical procedure was used to compare relative abundances of trait categories between groups of sites assigned to different quality classes (e.g. high versus good; good versus moderate, etc.), to identify the combinations of trait categories that best separated sites between adjacent toxic quality classes. Based on such sets of trait categories, we propose a statistical procedure to allocate sites to toxic quality classes from the attributes of its benthic macroinvertebrate community.5. Predictions from the trait‐based functional tool achieved approximately 73% of correct site post‐assignments to toxic quality classes pre‐assigned using chemical criteria. All of the sites pre‐assigned to the ‘high’ quality class, were post‐classified into the same quality class using benthic community attributes. From 0–90% of sites from other quality classes were classified identically using both chemistry‐ and trait‐based criteria, the observed biological impact being often lower than expected. We suggest that the rapid decline of sensitive species could lead to both low taxonomic richness and low diversity of adaptations, giving communities a less diverse range of potential functional responses to increasing toxic pressure.6. Biological criteria based on macroinvertebrate traits could provide new methods for biological assessment. This study is the first step towards an in situ functional tool of stream sediment contamination assessment at community level. However, we need to increase the number of sites included in the development data set to improve the tool’s precision, extend the procedure to additional stream types and validate the design at a larger spatial scale.

Exploring Time Series Retrieved from Cardiac Implantable Devices for Optimizing Patient Follow-Up

Current cardiac implantable devices (IDs) are equipped with a set of sensors that can provide useful information to improve patient follow-up and prevent health deterioration in the postoperative period. In this paper, data obtained from an ID with two such sensors (a transthoracic impedance sensor and an accelerometer) are analyzed in order to evaluate their potential application for the follow-up of patients treated with a cardiac resynchronization therapy (CRT). A methodology combining spatiotemporal fuzzy coding and multiple correspondence analysis (MCA) is applied in order to: 1) reduce the dimensionality of the data and provide new synthetic indexes based on the "factorial axes" obtained from MCA; 2) interpret these factorial axes in physiological terms; and 3) analyze the evolution of the patient's status by projecting the acquired data into the plane formed by the first two factorial axes named "factorial plane." In order to classify the different evolution patterns, a new similarity measure is proposed and validated on the simulated datasets, and then, used to cluster observed data from 41 CRT patients. The obtained clusters are compared with the annotations on each patient's medical record. Two areas on the factorial plane are identified, one being correlated with a health degradation of patients and the other with a stable clinical state.