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Results for “Fuzzy Logic”

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At least 397 records · Page 22Linked to original sources
PubMed1997

Epidemiological surveillance of tick populations: a model to predict the colonization success of Ixodes ricinus (Acari:Ixodidae).

A model that predicts the habitat availability for permanent populations of the tick Ixodes ricinus has been developed and applied to Spain. The model is based on the fuzzy logic rules and has a spatial resolution of about 4 km. Six long-term climate variables, which cover temperature and rainfall factors, drive the model by assigning the expected probability to find stable tick populations. Because the high accuracy of this inference tool, it could be used to map favourable sites for this tick in the epidemiological lookout of the Ixodes ricinus borne diseases.

Animals↗
PubMedDate not supplied

Computer simulation of sensorless fuzzy control of a rotary blood pump to assure normal physiology.

Rotary blood pumps have been considered effective permanently implantable devices. However, control of such pumps is quite complicated. Sensorless control of pump flow is required because no invasive flow or pressure sensors are wanted. Whereas insufficient pump output can cause underperfusion and should be avoided, overpumping may cause ventricular collapse and must be prevented. An intelligent physiologic control algorithm is highly desirable to reach optimal pump output based on physiologic requirements. We present an intelligent physiologic control mechanism for the blood pump allowing it to achieve normal physiology. Sensorless control of pump flow is gained by analysis of the electric motor current and speed. The required pump output flow is chosen based on heart rate, and an intelligent fuzzy logic based control mechanism is developed to adjust the motor input so that the pump output can reach required flow while also preventing the occurrence of ventricular suction or cannular collapse. Computer simulation was carried out, and the results indicate that the proposed algorithms can achieve required pump flow to obtain normal physiology, whereas overpumping can be prevented to provide safe operation.

Algorithms↗
PubMed2004

Soft-computing base analyses of the relationship between annoyance and coping with noise and odor.

The majority of research on annoyance as an important impact of noise, odor, and other stressors on man, has regarded the person as a passive receptor. It was however recognized that this person is an active participant trying to alter a troubled person-environment relationship or to sustain a desirable one. Coping has to be incorporated. This is of particular importance in changing exposure situations. For large populations a lot of insight can be gained by looking at average effects only. To investigate changes in annoyance and effects of coping, the individual or small group has to be studied. Then it becomes imperative to recognize the inherent vagueness in perception and human behavior. Fortunately, tools have been developed over the past decades that allow doing this in a mathematically precise way. These tools are sometimes referred to by the common label: soft-computing, hence the title of this paper. This work revealed different styles of coping both by blind clustering and by (fuzzy) logical aggregation of different actions reported in a survey. The relationship between annoyance and the intensity of coping it generates was quantified after it was recognized that the possibility for coping is created by the presence of the stressor rather than the actual fact of coping. It was further proven that refinement of this relationship is possible if a person can be identified as a coper. This personal factor can be extracted from a known reaction to one stressor and be used for predicting coping intensity and style in another situation. The effect of coping on a perceived change in annoyance is quantified by a set of fuzzy linguistic rules. This closes the loop that is responsible for at least some of the dynamics of the response to a stressor. This work thus provides all essential building blocks for designing models for annoyance in changing environments.

Adaptation, Psychological↗
PubMed2006

An on-line optimisation of a SBR cycle for carbon and nitrogen removal based on on-line pH and Our: the role of dissolved oxygen control.

A pilot plant sequencing batch reactor (SBR) was applied in a wastewater treatment plant treating urban wastewater focused on carbon and nitrogen removal. From an initial predefined step-feed cycle definition, the evolution of the on-line monitored pH and calculated oxygen uptake rate (OUR) were analysed in terms of knowledge extraction. First, the aerobic phases of the SBR cycle were operated using an On/Off dissolved oxygen (DO) control strategy that concluded with a sinusoidal pH profile that made detecting the "ammonia valley" difficult. After changing to fuzzy logic control of the dissolved oxygen and by adding an air flow meter to the pilot plant, the pH evolution and on-line calculated OUR showed a clearer trend during the aerobic phases. Finally, a proposed algorithm for adjusting the aerobic phases of the SBR for carbon and ammonia removal is presented and discussed.

Aerobiosis↗
PubMed2006

Modeling biological motor control for human locomotion with functional electrical stimulation.

This paper develops a novel control system for functional electrical stimulation (FES) locomotion, which aims to generate normal locomotion for paraplegics via FES. It explores the possibility of applying ideas from biology to engineering. The neural control mechanism of the biological motor system, the central pattern generator, has been adopted in the control system design. Some artificial control techniques such as neural network control, fuzzy logic, control and impedance control are incorporated to refine the control performance. Several types of sensory feedback are integrated to endow this control system with an adaptive ability. A musculoskeletal model with 7 segments and 18 muscles is constructed for the simulation study. Satisfactory simulation results are achieved under this FES control system, which indicates a promising technique for the potential application of FES locomotion in future.

Electric Stimulation Therapy↗
PubMed1999

Depth of anesthesia estimation and control.

A fully automated system was developed for the depth of anesthesia estimation and control with the intravenous anesthetic, Propofol. The system determines the anesthesia depth by assessing the characteristics of the mid-latency auditory evoked potentials (MLAEP). The discrete time wavelet transformation was used for compacting the MLAEP which localizes the time and the frequency of the waveform. Feature reduction utilizing step discriminant analysis selected those wavelet coefficients which best distinguish the waveforms of those responders from the nonresponders. A total of four features chosen by such analysis coupled with the Propofol effect-site concentration were used to train a four-layer artificial neural network for classifying between the responders and the nonresponders. The Propofol is delivered by a mechanical syringe infusion pump controlled by Stanpump which also estimates the Propofol effect-site and plasma concentrations using a three-compartment pharmacokinetic model with the Tackley parameter set. In the animal experiments on dogs, the system achieved a 89.2% accuracy rate for classifying anesthesia depth. This result was further improved when running in real-time with a confidence level estimator which evaluates the reliability of each neural network output. The anesthesia level is adjusted by scheduled incrementation and a fuzzy-logic based controller which assesses the mean arterial pressure and/or the heart rate for decrementation as necessary. Various safety mechanisms are implemented to safeguard the patient from erratic controller actions caused by external disturbances. This system completed with a friendly interface has shown satisfactory performance in estimating and controlling the depth of anesthesia.

Algorithms↗
PubMed1996

[Detection of rapid eye movement with rapidly adapting neuronal fuzzy systems in imprecise REM syntax].

Both living beings and artificial neuronal networks are capable of 'learning' and behavioural adaptation. But also the fuzzy program designed to detect rapid eye movements (REM) during sleep and described here, can be provided with a self-learning option that provides important information about REM sleep. The algorithm computes REM on the basis of horizontal and vertical EOG. EEG, EMG and actiography signals are employed to optimize the method and eliminate artefacts. In a second step, the fuzzy system learns to detect REM with the aid of a sample data set and a minimal set of syntax rules. From sample data and the actions and reactions of visual scorers, the program extracts additional rules and information, which are then used to build a complete fuzzy structure. Thereafter, the REM detection program optimizes the fuzzy logic structure, independently of visual monitoring, on its own. A direct comparison of the results of the algorithm in a 10-night analysis with those of two experienced visual scorers revealed a better than 95% agreement. Re-analysis with the algorithm showed a 100% concurrence. Complete visual measurement of the eye movements occurring in a single night requires several hours; this compares with only 15 minutes required by the algorithm.

Algorithms↗
PubMed1997

Continuous estimation of CO2 production during exercise.

A new method was developed for continuous isotopic estimation of human whole body CO2 rate of appearance (Ra) during non-steady state exercise. The technique consisted of a breath-by-breath measurement of 13CO2 enrichment (E) and a real-time fuzzy logic feedback system which controlled NaH13CO3 infusion rate to achieve an isotopic steady state. Ra was estimated from the isotope infusion rate and body 13CO2 enrichment which was equal to E at the isotopic steady state. During a non-steady state incremental cycle exercise (5 w/min or 10 w/min), NaH13CO3 infusion rate was successfully increased by the action of feedback controller so as to keep E constant.

Adult↗
PubMedDate not supplied

A simulation study for the analysis of uncertain binary responses: application to first insemination success in beef cattle.

A simulation was carried out to investigate the methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included, herd, year, and month of mating as fixed effects; and unrelated service sire, sire and residual as random effects was used to generate binary data. Binary responses were assigned using the difference between days to calving and average gestation length. Females deviating from average gestation length lead to uncertain binary responses. Thus, the methods investigated were the following: (1) a threshold model fitted to certain (no uncertainty) binary data (M1); (2) a threshold model fitted to uncertain binary data ignoring uncertainty (M2); and (3) analysis of uncertain binary data, accounting for uncertainty from day 16 to 26 (M3) or from day 14 to 28 (M4) after introduction of the bull, using a threshold model with fuzzy logic classification. There was virtually no difference between point estimates obtained from M1, M3, and M4 with true values. When uncertain binary data were analyzed ignoring uncertainty (M2), sire variance and heritability were underestimated by 22 and 24%, respectively. Thus, for noisy binary data, a threshold model contemplating uncertainty is needed to avoid bias when estimating genetic parameters.

Analysis of Variance↗
PubMed2001

[Progress in knowledge-based X-ray fluorescence spectrometry].

The review focuses on the expert systems and knowledge engineering in X-ray fluorescence spectrometry. It includes mainly a knowledge-controlled strategy combining the scan-based method with the fixed channel measurements, a XRF interpretation system of spectra with fuzzy logic and pattern recognition, and an expert system for qualitative interpretation of XRF spectra using a certainty factor. In the review, a series of the studies of exploring the knowledge engineering system in XRF are also included, which consists of four parts, i.e. spectra identification, pattern recognition with decision-making, quantitative determination combined with the theoretical alpha coefficients and neural networks, and XRF analysis without standards.

Algorithms↗
PubMed2002

[Interictal brain SPECT in patients with medically refractory temporal lobe epilepsy].

The objective of this study was to evaluate the utility of interictal brain SPECT in localizing the epileptogenic focus in a population of patients of Epilepsy Clinic of Hospital Universitário Clementino Fraga Filho (HUCFF/UFRJ), with medically refractory temporal lobe epilepsy (TLE) and normal computed tomography (CT) scans, studying the correlation between SPECT, electroencephalogram (EEG) and, in 11 cases, brain magnetic resonance imaging (MRI), and to compare the results to the other six literature series. Twelve (52.2%) patients presented abnormal SPECT. Among these, five (41.6% of abnormal SPECTs) presented unilateral SPECT changes at the same side of EEG (hypoperfusion in four and hyperperfusion in one), three (25% of abnormal SPECTs) presented bilateral hypoperfusion and bilateral EEG changes too, and four (33.3%) presented unilateral hypoperfusion and bilateral EEG changes. The statistical analysis was based on fuzzy logic. The correlation index among SPECT X EEG, SPECT X MRI and SPECT X EEG X MRI were highly significant, with significance levels at 0.01, p < 0.0005 and trust interval at 99% in all correlations. The correlation studies between the series presented similar results.

Adolescent↗
PubMed1999

A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques.

In this paper, a hybrid approach is presented for discriminating a few upper limb movements by processing the electromyographic (EMG) signals from selected shoulder muscles. Statistical techniques, such as the Generalized Likelihood Ratio test, the Principal Component Analysis, autoregressive parametric modeling techniques and cepstral analysis techniques, combined with a fuzzy logic based classifier (the Abe-Lan network) are used to construct low-dimensional feature spaces with high classification rates. The experimental results show the ability of the algorithm to correctly classify all the EMG patterns related to the selected planar arm pointing movements. Moreover, the structure presented offers promise for real-time applications because of the low computation costs of the overall algorithm.

Adult↗
PubMed2003

The Salomon advisory system supports a depressive episode therapy.

"Salomon" system is Polish computer advisory system for diagnostics of affective disorders. The system makes it possible to assess the relative intensity of depression measured by the depressive episode characteristic symptoms set based on scientific criteria for ICD-10 classification, M. Hamilton's depression rating scale and fuzzy logic. After two following consultations the system is able to generate the therapeutic index. The index depends on clinical situation. Basing on new computer technologies and ICD-10 classification the creation of the rank of the most effective therapeutic methods for the specific disorders is possible. Applying of neural network simulator makes it possible to find the similar depressive episode clinical subtype with its characteristic clinical picture used by clinician making a therapeutic decision.

Depressive Disorder↗
PubMed2004

Construction, training and clinical validation of an interpretation system for genotypic HIV-1 drug resistance based on fuzzy rules revised by virological outcomes.

OBJECTIVES: To evaluate whether fuzzy operators can be usefully applied to the interpretation of genotypic HIV-1 drug resistance by experts, and to improve the prediction of salvage therapy outcome by adapting interpretation rules of genotypic resistance on the basis of their association with virological response data. METHODS: We used a clinical dataset of 231 patients failing highly active antiretroviral therapy (HAART) and starting salvage therapy with baseline resistance genotyping and virological outcomes after 3 and 6 months. A set of rules predicting genotypic resistance was initially derived from an expert (ADL). Rules were implemented using a fuzzy logic approach and the virological outcomes dataset used for the training phase. The resulting algorithm was validated using a separate set of 184 selected patients by correlating the resulting predicted activity with observed virological response at 3 months. For comparison, the expert systems from the drug resistance group of the Agence Nationale de Recherches sur le SIDA (ANRS-AC11) and the algorithm from the Stanford's HIV drug resistance database (Stanford HIVdb) were evaluated on the same set. RESULTS: The starting algorithm had a correlation with virological outcomes of R2=0.06 (P=0.0001). After the training phase the correlation with virological outcomes increased to R2=0.19 (P<0.000001). In the validation set of patients, the activity of the salvage regimen predicted by the fuzzy algorithm was the only variable independently predictive of the 3-month viral load change even after adjusting by the activity predicted by the two expert systems and baseline viral load (for each 10% salvage regimen's activity increase, mean HIV RNA change from baseline: -0.27 log10 copies/ml; 95% CI -0.39, -0.15). CONCLUSION: Using fuzzy operators in a virological outcomes training database to implement a rules-based algorithm for genotypic resistance interpretation, significant improvements of outcomes prediction were obtained. The resulting algorithm showed an independent predictive capability of virological outcomes over that of two rules-based interpretation algorithms made by experts. Although the system was trained and validated on a limited number of cases, the approach deserves further evaluation.

Adolescent↗
PubMed1998

An analysis of training activities in the use of critical care equipment within hospitals in the United Kingdom.

We investigated how training in the use of electromedical devices in adult and neonatal intensive care departments is organised, designed and delivered. Forty-one departments within two Regional Health Authorities in the United Kingdom returned a detailed questionnaire, from which several nonlinear models were developed using fuzzy logic analysis. The results clearly indicate that training in the use of equipment is a very minor activity, with less than 1% of departmental time spent on providing or receiving such training. Departments that had a higher level of expertise required less assistance from technical personnel. Critical care equipment can be utilised more fully, cost effectively and safely if a formal and regular training programme is implemented.

Adult↗
PubMed2000

Examination of the use of fuzzy sets to describe relative speed perception.

In recent years a range of new methods have been proposed with which to describe and evaluate driver behaviour. One such method is that of fuzzy logic, where variables used in the driver decision-making process may be described linguistically, allowing a quantifiable degree of uncertainty to be introduced. This paper explores the use of such a formalism to describe the driver perception of 'closing speed' between two vehicles engaged in 'car-following' on a motorway, and by using data from an instrumented vehicle experiment, it tests a number of models using relative speed, visual angle and the time to collision. Several of these models fit the data quite well, and there is both a small positive perception bias present and a number of reversals in sign judgement. Additionally, a brief examination is made of potential variations on the methodology that may both make data collection easier and/or allow a 'more fuzzy' representation to be made.

Automobile Driving↗
PubMed2005

Biomechanical study using fuzzy systems to quantify collagen fiber recruitment and predict creep of the rabbit medial collateral ligament.

In normal daily activities, ligaments are subjected to repeated loads, and respond to this environment with creep and fatigue. While progressive recruitment of the collagen fibers is responsible for the toe region of the ligament stress-strain curve, recruitment also represents an elegant feature to help ligaments resist creep. The use of artificial intelligence techniques in computational modeling allows a large number of parameters and their interactions to be incorporated beyond the capacity of classical mathematical models. The objective of the work described here is to demonstrate a tool for modeling creep of the rabbit medial collateral ligament that can incorporate the different parameters while quantifying the effect of collagen fiber recruitment during creep. An intelligent algorithm was developed to predict ligament creep. The modeling is performed in two steps: first, the ill-defined fiber recruitment is quantified using the fuzzy logic. Second, this fiber recruitment is incorporated along with creep stress and creep time to model creep using an adaptive neurofuzzy inference system. The model was trained and tested using an experimental database including creep tests and crimp image analysis. The model confirms that quantification of fiber recruitment is important for accurate prediction of ligament creep behavior at physiological loads.

Algorithms↗
PubMed2006

Model-supported optimization of phototrophic growth in a stirred-tank photobioreactor.

A fuzzy-logic light attenuation model was developed and validated for a stirred-tank photobioreactor. Based on this model, local light intensities were used to calculate local specific growth rates of the cyanobacteria Synechococcus PCC 7942. The light regime for maximization of biomass space-time yield in a batch process was estimated using a genetic algorithm taking into account the integral average of the individual growth rates.

Algorithms↗