An application of fuzzy DL-Based semantic perception to soil container classification

Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.

Fuzzy-logic based frequency controller for wind farms augmented with energy storage systems

Displacement of conventional synchronous generators by non-inertial units such as wind or solar generators will result in reduced-system inertia affecting under-frequency response. Frequency control is important to avoid equipment damage, load shedding, and possible blackouts. Wind generators along with energy storage systems can be used to improve the frequency response of low-inertia power system. This paper proposes a fuzzy-logic based frequency controller (FFC) for wind farms augmented with energy storage systems (wind-storage system) to improve the primary frequency response in future low-inertia hybrid power system. The proposed controller provides bidirectional real power injection using system frequency deviations and rate of change of frequency (RoCoF). Moreover, FFC ensures optimal use of energy from wind farms and storage units by eliminating the inflexible de-loading of wind energy and minimizing the required storage capacity. The efficacy of the proposed FFC is verified on the low-inertia hybrid power system.

“I understand. I am a participant”: Navigating the ‘fuzzy’ boundaries of visual methods in qualitative longitudinal research

In this chapter we discuss how utilising the participatory visual methodology, photovoice, in an aged care context with its unique communal setting raised several ‘fuzzy boundary’ ethical dilemmas. To illustrate these challenges, we draw on immersive field notes from an ongoing qualitative longitudinal research (QLR) exploring the lived experience of aged care from the perspective of older residents, and focus on interactions with one participant, 81 year old Cassie. We explore how the camera, which is integral to the photovoice method, altered the researcher/participant ethical dynamics by becoming a continual ‘connector’ to the researcher. The camera took on a distinct agency, acting as a non-threatening ‘portal’ that lengthened contact, provided informal opportunities to alter the relationship dynamics and enabled unplanned participant revelation.

Fuzzy optimization model for water quality management of a river system - Closure

A fuzzy waste-load allocation model, FWLAM, is developed for water quality management of a river system using fuzzy multiple-objective optimization. An important feature of this model is its capability to incorporate the aspirations and conflicting objectives of the pollution control agency and dischargers. The vagueness associated with specifying the water quality criteria and fraction removal levels is modeled in a fuzzy framework. The goals related to the pollution control agency and dischargers are expressed as fuzzy sets. The membership functions of these fuzzy sets are considered to represent the variation of satisfaction levels of the pollution control agency and dischargers in attaining their respective goals. Two formulations—namely, the MAX-MIN and MAX-BIAS formulations—are proposed for FWLAM. The MAX-MIN formulation maximizes the minimum satisfaction level in the system. The MAX-BIAS formulation maximizes a bias measure, giving a solution that favors the dischargers. Maximization of the bias measure attempts to keep the satisfaction levels of the dischargers away from the minimum satisfaction level and that of the pollution control agency close to the minimum satisfaction level. Most of the conventional water quality management models use waste treatment cost curves that are uncertain and nonlinear. Unlike such models, FWLAM avoids the use of cost curves. Further, the model provides the flexibility for the pollution control agency and dischargers to specify their aspirations independently.

Fuzzy Logic-Based Approaches in Water Resource System Modelling

Recent research in modelling uncertainty in water resource systems has highlighted the use of fuzzy logic-based approaches. A number of research contributions exist in the literature that deal with uncertainty in water resource systems including fuzziness, subjectivity, imprecision and lack of adequate data. This chapter presents a broad overview of the fuzzy logic-based approaches adopted in addressing uncertainty in water resource systems modelling. Applications of fuzzy rule-based systems and fuzzy optimisation are then discussed. Perspectives on the scope for further research are presented.

A fuzzy multistage evolutionary (FUME) clustering technique

n this paper, a multistage evolutionary scheme is proposed for clustering in a large data base, like speech data. This is achieved by clustering a small subset of the entire sample set in each stage and treating the cluster centroids so obtained as samples, together with another subset of samples not considered previously, as input data to the next stage. This is continued till the whole sample set is exhausted. The clustering is accomplished by constructing a fuzzy similarity matrix and using the fuzzy techniques proposed here. The technique is illustrated by an efficient scheme for voiced-unvoiced-silence classification of speech.

Neuro- and immunotoxic effects of fumonisin B1 in cells

Fumonisin B1 (FB1) is a mycotoxin produced by the fungus Fusarium verticillioides, which commonly infects corn and other agricultural products. Fusarium species can also be found in moisture-damaged buildings, and therefore there may also be human exposure to Fusarium mycotoxins, including FB1. FB1 affects the metabolism of sphingolipids by inhibiting the enzyme ceramide synthase. It is neuro-, hepato- and nephrotoxic, and it is classified as possibly carcinogenic to humans. This study aimed to clarify the mechanisms behind FB1-induced neuro- and immunotoxicity. Four neural and glial cell lines of human, rat and mouse origin were exposed to graded doses of FB1 and the effects on the production of reactive oxygen species, lipid peroxidation, intracellular glutathione levels, cell viability and apoptosis were investigated. Furthermore, the effects of FB1, alone or together with lipopolysaccharide (LPS), on the mRNA and protein expression levels of different cytokines and chemokines were studied in human dendritic cells (DC). FB1 induced oxidative stress and cell death in all cell lines studied. Generally, the effects were only seen after prolonged exposure at 10 and 100 µM of FB1. Signs of apoptosis were also seen in all four cell lines. The sensitivities of the cell lines used in this study towards FB1 may be classified as human U-118MG glioblastoma > mouse GT1-7 hypothalamic > rat C6 glioblastoma > human SH-SY5Y neuroblastoma cells. When comparing cell lines of human origin, it can be concluded that glial cells seem to be more sensitive towards FB1 toxicity than those of neural origin. After exposure to FB1, significantly increased levels of the cytokine interferon-γ (IFNγ) were detected in human DC. This observation was further confirmed by FB1-induced levels of the chemokine CXCL9, which is known to be regulated by IFNγ. During co-exposure of DC to both LPS and FB1, significant inhibitions of the LPS-induced levels of the pro-inflammatory cytokines interleukin-6 (IL-6) and IL-1β, and their regulatory chemokines CCL3 and CCL5 were observed. FB1 can thus affect immune responses in DC, and therefore, it is rather likely that it also affects other types of cells participating in the immune defence system. When evaluating the toxicity potential of FB1, it is important to consider the effects on different cell types and cell-cell interactions. The results of this study represent new information, especially about the mechanisms behind FB1-induced oxidative stress, apoptosis and immunotoxicity, as well as the varying sensitivities of different cell types towards FB1.

A Fuzzy Approximation Scheme for Sequential Learning in Pattern Recognition

An adaptive learning scheme, based on a fuzzy approximation to the gradient descent method for training a pattern classifier using unlabeled samples, is described. The objective function defined for the fuzzy ISODATA clustering procedure is used as the loss function for computing the gradient. Learning is based on simultaneous fuzzy decisionmaking and estimation. It uses conditional fuzzy measures on unlabeled samples. An exponential membership function is assumed for each class, and the parameters constituting these membership functions are estimated, using the gradient, in a recursive fashion. The induced possibility of occurrence of each class is useful for estimation and is computed using 1) the membership of the new sample in that class and 2) the previously computed average possibility of occurrence of the same class. An inductive entropy measure is defined in terms of induced possibility distribution to measure the extent of learning. The method is illustrated with relevant examples.

Structured model-following neuro-adaptive design for attitude maneuver of rigid bodies

A new structured model-following adaptive approach is presented in this paper to achieve large attitude maneuvers of rigid bodies. First, a nominal controller is designed using the dynamic inversion philosophy. Next, a neuro- adaptive design is proposed to augment the nominal design in order to assure robust performance in the presence of parameter inaccuracies as well as unknown constant external disturbances. The structured approach proposed in this paper (where kinematic and dynamic equations are handled separately), reduces the complexity of the controller structure. From simulation studies, this adaptive controller is found to be very effective in assuring robust performance.

Neuro-angiostrongylosis in wild Black and Grey-headed flying foxes (Pteropus spp)

Objective: To identify nematodes seen in histological sections of brains of flying foxes (fruit bats) and describe the associated clinical disease and pathology. Proceedures: Gross and histological examination of brains from 86 free-living flying foxes with neurological disease was done as part of an ongoing surveillance program for Australian bat lyssavirus. Worms were recovered, or if seen in histological sections, extracted by maceration of half the brain and identified by microscopic examination. Histological archives were also reviewed. Results: There was histological evidence of angiostrongylosis in 16 of 86 recently submitted flying foxes with neurological disease and in one archival case from 1992. In 10 flying foxes, worms were definitively identified as Angiostrongylus cantonensis fifth-stage larvae. A worm fragment and third stage larvae were identified as Angiostrongylus sp, presumably A cantonensis, in a further three cases. The clinical picture was dominated by paresis, particularly of the hindlimbs, and depression, with flying foxes surviving up to 22 days in the care of wildlife volunteers. Brains containing fifthstage larvae showed a moderate to severe eosinophilic and granulomatous meningoencephalitis (n = 14), whereas there was virtually no inflammation of the brains of bats which died when infected with only smaller, third-stage larvae (n = 3). There was no histological evidence of pulmonary involvement. Conclusion: This is the first report of the recovery and identification of A cantonensis from free-living Australian wildlife. While angiostrongylosis is a common cause of paresis in flying foxes, the initial clinical course cannot be differentiated from Australian bat lyssavirus infection, and wildlife carers should be urged not to attempt to rehabilitate flying foxes with neurological disease.

A fuzzy logic based dynamic wave model inversion algorithm for canal regulation

A fuzzy logic based centralized control algorithm for irrigation canals is presented. Purpose of the algorithm is to control downstream discharge and water level of pools in the canal, by adjusting discharge release from the upstream end and gates settings. The algorithm is based on the dynamic wave model (Saint-Venant equations) inversion in space, wherein the momentum equation is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. The fuzzy rule based model is developed on fuzzification of a new mathematical model for wave velocity, the derivational details of which are given. The advantages of the fuzzy control algorithm, over other conventional control algorithms, are described. It is transparent and intuitive, and no linearizations of the governing equations are involved. Timing of the algorithm and method of computation are explained. It is shown that the tuning is easy and the computations are straightforward. The algorithm provides stable, realistic and robust outputs. The disadvantage of the algorithm is reduced precision in its outputs due to the approximation inherent in the fuzzy logic. Feed back control logic is adopted to eliminate error caused by the system disturbances as well as error caused by the reduced precision in the outputs. The algorithm is tested by applying it to water level control problem in a fictitious canal with a single pool and also in a real canal with a series of pools. It is found that results obtained from the algorithm are comparable to those obtained from conventional control algorithms.

Structural Damage Detection Using Modal Curvature and Fuzzy Logic

A fuzzy logic system (FLS) with a new sliding window defuzzifier is proposed for structural damage detection using modal curvatures. Changes in the modal curvatures due to damage are fuzzified using Gaussian fuzzy sets and mapped to damage location and size using the FLS. The first four modal vectors obtained from finite element simulations of a cantilever beam are used for identifying the location and size of damage. Parametric studies show that modal curvatures can be used to accurately locate the damage; however, quantifying the size of damage is difficult. Tests with noisy simulated data show that the method detects damage very accurately at different noise levels and when some modal data are missing.

Estimation of fuzzy memberships from histograms

Based on the conclusions drawn in the bijective transformation between possibility and probability, a method is proposed to estimate the fuzzy membership function for pattern recognition purposes. A rational function approximation to the probability density function is obtained from the histogram of a finite (and sometimes very small) number of samples. This function is normalized such that the highest ordinate is one. The parameters representing the rational function are used for classifying the pattern samples based on a max-min decision rule. The method is illustrated with examples.

Damage assessment of structures with uncertainty by using mode-shape curvatures and fuzzy logic

Uncertainties associated with the structural model and measured vibration data may lead to unreliable damage detection. In this paper, we show that geometric and measurement uncertainty cause considerable problem in damage assessment which can be alleviated by using a fuzzy logic-based approach for damage detection. Curvature damage factor (CDF) of a tapered cantilever beam are used as damage indicators. Monte Carlo simulation (MCS) is used to study the changes in the damage indicator due to uncertainty in the geometric properties of the beam. Variation in these CDF measures due to randomness in structural parameter, further contaminated with measurement noise, are used for developing and testing a fuzzy logic system (FLS). Results show that the method correctly identifies both single and multiple damages in the structure. For example, the FLS detects damage with an average accuracy of about 95 percent in a beam having geometric uncertainty of 1 percent COV and measurement noise of 10 percent in single damage scenario. For multiple damage case, the FLS identifies damages in the beam with an average accuracy of about 94 percent in the presence of above mentioned uncertainties. The paper brings together the disparate areas of probabilistic analysis and fuzzy logic to address uncertainty in structural damage detection.

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA)problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max-min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga-Bhadra river system in India.