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At least 415 records · Page 23Linked to original sources
PubMed1997

Generating programs for predicting the activity of functional sites.

The computer system ACTIVITY is intended for generating programs with which to predict the activity of functional sites by nucleotide sequences. ACTIVITY analyzes a basis set of nucleotide sequences with known activity. The novelty of this approach is that Zadeh's fuzzy logic and decision-making theory have been employed for determining the best "sequence-->activity" regression. The best one thus determined is then transformed into the text of a program with which the activity for any nucleotide sequence is to be predicted. Testing with independent data has proved this prediction reliable. We have compared our approach with the two commonly used on identical data sets to find the ACTIVITY-generated programs quite competitive.

Database Management Systems↗
PubMed1999

Computer-enhanced diagnosis of malignant hyperpyrexia.

A computer program using fuzzy logic templates has been developed to enhance the diagnosis of malignant hyperpyrexia. When tested, retrospectively, against the physiological changes that occurred during the onset of a bona fide malignant hyperpyrexia episode it made the diagnosis about 10 minutes before the anaesthetist. When tested against the changes associated with laparoscopic surgery, which has certain similarities, the diagnosis of malignant hyperpyrexia was very transient. The use of this program may assist the anaesthetist in the diagnosis of this rare event.

Algorithms↗
PubMedDate not supplied

Learning from biomedical time series through the integration of qualitative models and fuzzy systems.

Our work deals with a method for the identification of the dynamics of nonlinear (patho-)physiological systems by learning from data. The key idea which underlies our approach consists in the integration of qualitative modeling methods with fuzzy logic systems. The major advantage which derives from such an integrated framework lies in its capability both to represent the structural knowledge of the system at study and to determine, by exploiting the available experimental data, a functional approximation of the system dynamics that can be used as a reasonable predictor of the patient's future state. We have successfully applied our method in the identification of the intracellular kinetics of thiamine from data collected in the intestine cells.

Artificial Intelligence↗
PubMed2002

Quantitative determination of Freon gas using an electronic nose.

This paper reports a quantitative electronic nose (enose) for the quantitative determination of Freon gas within the concentration range 0-1000 ppm in the presence of interfering gases such as water, lubricant and petrol vapours. This quantitative enose is a new type of Freon detection system, composed of an array of four sensors. The artificial neural network (ANN) and fuzzy logic type of ANN (FNN), in combination with the relative error concept in analytical chemistry, are integrated for both quantification and discrimination. The predicted results are satisfied with a pass rate of > 80% within the permitted relative errors. The results show that the Freon enose developed in this study is reliable for both the qualitative and quantitative determination of Freon gas and exhibits the merits of high sensitivity, anti-interference and accuracy.

Air Conditioning↗
PubMed2000

Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation.

An algorithm for the segmentation of a single sequence of three-dimensional magnetic resonance (MR) images into cerebrospinal fluid, gray matter, and white matter classes is proposed. This new method is a possibilistic clustering algorithm using the fuzzy theory as frame and the wavelet coefficients of the voxels as features to be clustered. Fuzzy logic models the uncertainty and imprecision inherent in MR images of the brain, while the wavelet representation allows for both spatial and textural information. The procedure is fast, unsupervised, and totally independent of any statistical assumptions. The method is tested on a phantom image, then applied to normal and Alzheimer's brains, and finally compared with another classic brain tissue segmentation method, affording a relevant classification of voxels into the different tissue classes.

Adult↗
PubMed1995

Computer-assisted assignment of peptides with non-standard amino acids.

A comprehensive peptide assignment program and its application to a cyclic peptide, cyclosporin A, are presented in this paper. A group of graph theoretical algorithms using fuzzy logic are discussed with the aid of examples from cyclosporin A. The algorithms deal with heavily overlapped peaks, recover disjointed and distorted spin coupling networks, and include strategies for sequence-specific assignment. A procedure to extend the Protein Knowledge Base for automatically assigning non-standard amino acid residues is also presented. The program is capable of completely automated assignment for small peptides (approximately 20 residues). For such molecules, it is insensitive to whether the peptide chain is cyclic or acyclic, and to whether amide protons are present or absent. For larger peptides/proteins, more user interaction is required and the sequence-specific assignment step usually must proceed through fragments smaller than the full length to avoid problems due to occurrence of a combinatorial explosion. The program can be applied as a rigorous tool to check manual assignments. The fuzzy graph theoretical concepts built in the program are illustrated with 2D proton spectra of a peptide, but may be extended to higher-dimensional spectra, other biopolymers, natural products and other organic structures.

Algorithms↗
PubMed2005

Automatic detection of erthemato-squamous diseases using adaptive neuro- fuzzy inference systems.

A new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the ANFIS classifiers learned how to differentiate a new case in the domain. The six ANFIS classifiers were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. To improve diagnostic accuracy, the seventh ANFIS classifier (combining ANFIS) was trained using the outputs of the six ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of erythemato-squamous diseases were obtained through analysis of the ANFIS. The performances of the ANFIS model were evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has some potential in detecting the erythemato-squamous diseases. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.

Algorithms↗
PubMed2005

Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals.

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects. The three ANFIS classifiers were used to detect internal carotid artery conditions (normal, stenosis and occlusion) when two features, resistivity and pulsatility indices, defining changes of internal carotid arterial Doppler waveforms were used as inputs. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of internal carotid artery stenosis and occlusion were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of classification accuracies and the results confirmed that the proposed ANFIS classifiers have some potential in detecting the internal carotid artery stenosis and occlusion. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.

Adolescent↗
PubMed1992

Microcomputer-based automatic regulation of extracorporeal circulation: a trial for the application of fuzzy inference.

Since its establishment many researchers have been trying to automate the process of extracorporeal circulation (ECC). We developed a preliminary experimental model of an automatic regulatory system for ECC. The purpose of the system was to regulate basic hemodynamic parameters such as pump flow and withdrawal blood volume. It was divided into three main components: data sampling unit, central processing unit, and controlling unit. Based on this model we were able to achieve autoregulation of ECC using minimum configuration; however, the system lacked smoothness. This was partly because it was based on a "static" regulation system which used conditional statements having multiple parameters. In this study, we applied fuzzy logic to the former model to achieve more accurate and reliable regulation. We report experimental results for the new system and compare the data between clinical circulation in 13 infants (mean body weight, 13.32 +/- 5.99 kg) and experimental regulation in 7 mongrel dogs (mean body weight, 11.9 +/- 2.53 kg). The comparative study revealed no statistical difference between the two groups. This result suggests that the automatic regulation of ECC may be an alternative to manual operation by a professional perfusionist in the near future.

Animals↗
PubMed2006

Application of a fuzzy algorithm for pH control in a struvite crystallisation reactor.

A struvite crystallisation process is highly dependent on pH. To achieve a high phosphorus recovery as struvite it is important to have an accurate control of pH in the reactor. The high non-linear response of pH value makes manual pH control difficult. Therefore, a software based on fuzzy logic control (FLC) has been developed to maintain the pH at a set value in a stirred reactor to crystallise struvite. The FLC developed has been based on Larsen's inference. In order to confirm the improvement of the pH stability using FLC software, different experiments have been carried out with manual control of the pH value, and with the FLC software. It has been demonstrated that using FLC software allows a precise control of pH with high stability, optimises the process, and minimises the operator intervention.

Algorithms↗
PubMed2006

A new technique for imaging the human spinal cord in vivo.

Diffusion tensor imaging has been used extensively in the brain for identifying morphological characteristics based on gray and white matter structures; however, similar applications in the spinal cord have not been as successful. Spatial resolution and limited contrast have undermined accurate delineation between gray and white matter in the spinal cord. This study applied fuzzy logic to diffusion tensor images of the human spinal cord to discriminate between gray and white matter. The technique used common anisotropy indices and newly developed indices based on properties of the diffusion ellipsoid. Preliminary applications to subjects with varying levels of spinal cord injury are also presented in this study. Results indicated larger contrast between gray and white matter compared to the traditional fractional anisotropy index and thus presents a superior technique for determining precise morphology of the spinal cord.

Adult↗
PubMed2005

Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS).

The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.

Algorithms↗
PubMed2005

Fuzzy combination of fuzzy and switching state-feedback controllers for nonlinear systems subject to parameter uncertainties.

This paper presents a fuzzy controller, which involves a fuzzy combination of local fuzzy and global switching state-feedback controllers, for nonlinear systems subject to parameter uncertainties with known bounds. The nonlinear system is represented by a fuzzy combined Takagi-Sugeno-Kang model, which is a fuzzy combination of the global and local fuzzy plant models. By combining the local fuzzy and global switching state-feedback controllers using fuzzy logic techniques, the advantages of both controllers can be retained and the undesirable chattering effect introduced by the global switching state-feedback controller can be eliminated. The steady-state error introduced by the global switching state-feedback controller when a saturation function is used can also be removed. Stability conditions, which are related to the system matrices of the local and global closed-loop systems, are derived to guarantee the closed-loop system stability. An application example will be given to demonstrate the merits of the proposed approach.

Algorithms↗
PubMed2006

An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination.

An integrated fuzzy-stochastic risk assessment (IFSRA) approach was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The contaminant concentrations in groundwater predicted from a numerical model were associated with probabilistic uncertainties due to the randomness in modeling input parameters, while the consequences of contaminant concentrations violating relevant environmental quality guidelines and health evaluation criteria were linked with fuzzy uncertainties. The contaminant of interest in this study was xylene. The environmental quality guideline was divided into three different strictness categories: "loose", "medium" and "strict". The environmental-guideline-based risk (ER) and health risk (HR) due to xylene ingestion were systematically examined to obtain the general risk levels through a fuzzy rule base. The ER and HR risk levels were divided into five categories of "low", "low-to-medium", "medium", "medium-to-high" and "high", respectively. The general risk levels included six categories ranging from "low" to "very high". The fuzzy membership functions of the related fuzzy events and the fuzzy rule base were established based on a questionnaire survey. Thus the IFSRA integrated fuzzy logic, expert involvement, and stochastic simulation within a general framework. The robustness of the modeling processes was enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. The developed IFSRA was applied to a petroleum-contaminated groundwater system in western Canada. Three scenarios with different environmental quality guidelines were analyzed, and reasonable results were obtained. The risk assessment approach developed in this study offers a unique tool for systematically quantifying various uncertainties in contaminated site management, and it also provides more realistic support for remediation-related decisions.

Fuzzy Logic↗
PubMed2005

A rehabilitation robot with force-position hybrid fuzzy controller: hybrid fuzzy control of rehabilitation robot.

The goal of this study was to design a robot system for assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The robot should be able to guide patient's wrist to move along planned linear or circular trajectories. A hybrid position/force controller incorporating fuzzy logic was developed to constrain the movement in the desired direction and to maintain a constant force along the moving direction. The controller was stable in the application range of movements and forces. Offline analyses of data were used to quantitatively assess the progress of rehabilitation. The results show that the robot could guide the upper limbs of subjects in linear and circular movements under predefined external force levels and apply a desired force along the tangential direction of the movements.

Computer-Aided Design↗
PubMed1995

Cross-linguistic comparisons in the integration of visual and auditory speech.

We examined how speakers of different languages perceive speech in face-to-face communication. These speakers identified synthetic unimodal and bimodal speech syllables made from synthetic auditory and visual five-step /ba/-/da/ continua. In the first experiment, Dutch speakers identified the test syllables as either /ba/ or /da/. To explore the robustness of the results, Dutch and English speakers were given a completely open-ended response task. Tasks in previous studies had always specified a set of alternatives. Similar results were found in the two-alternative and open-ended task. Identification of the speech segments was influenced by both the auditory and the visual sources of information. The results falsified an auditory dominance model (ADM) which assumes that the contribution of visible speech is dependent on poor-quality audible speech. The results also falsified an additive model of perception (AMP) in which the auditory and visual sources are linearly combined. The fuzzy logical model of perception (FLMP) provided a good description of performance, supporting the claim that multiple sources of continuous information are evaluated and integrated in speech perception. These results replicate previous results found with English, Spanish, and Japanese speakers. Although there were significant performance differences, the model analyses indicated no differences in the nature of information processing across language groups. The performance differences across languages were caused by information differences due to different phonologies in Dutch and English. These results suggest that the underlying mechanisms for speech perception are similar across languages.

Adult↗
PubMed2005

A fuzzy theory of cortical computation: neuropoietic engrams, fuzzy hypercubes, and the nature of consciousness.

We apply fuzzy logic to a theory of memory representation and computation in the human cerebral cortex. The theory termed neuropoiesis is based on the hypothetical transfer of mRNA polyribosomes from the post-synaptic dendritic spine of cortical pyramidal neurons to the presynaptic boutons of connecting axons through a hypothetical process termed retroduction. The net effect of this process is a vast increase in predicted memory storage. The fuzzification of memory engrams permits this expansion in memory capacity and requires multiplex signaling which, in turn, requires the formation of a spike packet whose length is determined by the EEG frequencies generated by the thalamo-cortical rhythm generators. The role of the EEG frequencies is to provide a wavelet-like transform of the multiplex signal which in turn determines the degree of data compression that is required for memory recruitment at a given level of vigilance during cortical computation. In this conceptual model cortical computation is hypothesized to be a form of cluster analysis that can be represented by a fuzzy hypercube in which each dimension of the unit hypercube represents an apical dendrite of a layer 5 pyramidal cell in a cortical fascicle. The tuftal area of the apical dendrite in cortical layer one corresponds to the MIN or zero point of the hypercube's dimension and the cell body in layer 5 corresponds to the MAX or one point of that dimension in the unit hypercube. The neuroanatomical location of synapses on the apical dendrites in the fascicle is mapped onto the fuzzy hypercube. These synapses form clusters composed of both bottom-up and top-down signals represented as metasynaptic fuzzy sets-as-points in the hypercube. Soft winner-take-all gating by inhibitory neurons is proposed to supply the only non-linear operation needed for cortical computation. Feed-forward inhibition is envisioned to play the decisive role of spicing or de-fuzzifying the output signal. Proper transmission of the multiplex signal that carries the fuzzy engram requires synchrony of neuronal firing. For this fuzzy cortical model, synchrony of firing, multiplex signaling, winner-take-all gating, and the known spectrum of EEG frequencies are all derivable from the fundamental mechanism termed synaptopoiesis as described in the theory of neuropoiesis. Finally, this theory predicts that the neural correlate of consciousness must include inhibitory subcortical connections and that its function is largely that of limiting coherence to a narrow range of cortical engrams.

Cerebral Cortex↗
PubMed2002

Modeling of dynamic cardiovascular responses during G-transition-induced orthostatic stress in pitch and roll rotations.

Dynamic and fuzzy models for a typical subject's cardiovascular response to the orthostatic stress have been developed based on experimental data. In our original study (Cheung et al., 1999), arterial blood pressure (BP) time-series data were obtained using a man-rated tilt table that applies gigahertz-acceleration transitions from +0.861 Gz [head-up (HU)] to -0.707 G [head-down (HD)] and back to +0.861 Gz (HU) using either pitch or roll rotations (Cheung et al., 1999). G transitions of different duration and onset rates are common in fighter maneuvers. Based on these data, two types of predictive models have been developed in this paper: 1) second-order discrete-time models that predict BP dynamics during pitch and roll rotations and 2) fuzzy logic models that predict important variations in a subject's cardiovascular dynamics induced by HU-to-HD and HD-to-HU transitions. These two types of models assist in providing an operationally important predictive view on the characteristics of BP responses to orthostatic stress induced by pitch and roll rotations of a fighter jet pilot. The new models are being currently utilized in the design of operational recommendations for more G-tolerant operational flight regimes (e.g., split-S tactical maneuver) than the ones currently in use for modern combat aircraft.

Acceleration↗