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Results for “Fuzzy Logic”

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PubMed2000

A property concept frame representation for flexible image-content retrieval in histopathology databases.

In histopathology databases, images descriptions are collections of properties provided by experts. Image content retrieval implies comparison of such properties. The objective of this work is to enrich the traditional attribute-value representation of properties in order to take into account the polymorphism and subjectivity of properties and to manage the comparison process. In this paper we define a property concept frame (PCF) representation based on fuzzy logic to handle both representation and comparison. Seven quantifiable morphological characteristics were selected from histopathological reports to illustrate the variety of fuzzy predicates and linguistic terms in properties. The PCF representation has been tested in the context of breast pathology. It is concluded that the PCF representation provides a unification scheme to retrieve in images morphological characteristics that are described in different ways. It may enhance the relevancy of applications in various contexts such as image content-based retrieval or case-based reasoning from images.

Breast↗
PubMed1998

[Study on comprehensive assessment in selection of special work personnel].

A comprehensive assessment method combining fuzzy set theory with traditional approaches is introduced. Method of index assessment and comprehensive assessment along with a fuzzy assessment model are presented. As an example, three fuzzy algorithms are used to assess 6 candidates. Calculated results show that the combination of fuzzy logic and experience of experts is feasible and can deal with unprecise information more effectively as well as the aggregation of fuzzy information and precise data. The new method described in this paper is accurate and the speed of decision-making is much improved as compared with the conventional approaches.

Aerospace Medicine↗
PubMedDate not supplied

Fuzzy methods in tremor assessment, prediction, and rehabilitation.

Tremor is a disabling condition for a large segment of population, mainly elderly. To the present date, there are no adequate tools to diagnose and help rehabilitation of subjects with tremor, but recently there is a tremendous surge of interest in the research in the field. We report on the use of fuzzy methods in applications for rehabilitation, namely in tremor diagnosing and control. We synthesize our results regarding the characterization of the tremor by means of nonlinear dynamics techniques and fuzzy logic, and the prediction of tremor movements in view of rehabilitation purposes. Based on linear and nonlinear analysis of tremor, and using fuzzy aggregation, the fusing of tremor parameters in global functional disabling factors is proposed. Nonlinear dynamic parameters, namely correlation dimension and Lyapunov exponent is used in order to improve the assessment of tremor. The benefits of the fuzzy fused tremor parameters rely on more complete and accurate assessment of the functional impairment and on improved feedback for rehabilitation, based on the fused parameters of the tremor. Further steps in rehabilitation may require external muscular control. In turn, the control of tremor by electrical stimulation requires movement prediction. Several neural and neuro-fuzzy predictors are compared and a neuro-fuzzy predictor is presented, allowing us five-step ahead prediction, with an RMS error of the order of 10%. The benefits of the neuro-fuzzy predictor are good prediction capability, versatility, and apparently a high robustness to individual variations of the tremor. The reported research, which extended over several years and included development of sensors, equipment, and software, has been aimed to development of products. The results may also open new ways in tremor rehabilitation.

Aged↗
PubMed2003

Fuzzy modeling of skin permeability coefficients.

PURPOSE: The purpose of this work was to determine whether a new modeling methodology using fuzzy logic c an predict skin permeability coefficients that are given compound descriptors that have been proven to affect percutaneous penetration. METHOD: Three fuzzy inference models were developed using subtractive clustering to define natural structures within the data and assign subsequent rules. The numeric parameters describing the rules were refined through the use of an Adaptive Neural Fuzzy Inference System implemented in MatLab. Each model was evaluated using the entire data set. Then predicted outputs were compared to the published experimental data. RESULTS: All databases produced fuzzy inference models that successfully predicted skin permeability coefficients, with correlation coefficients ranging from 0.83 to 0.97. The lowest correlation coefficient resulted from a model using log octanol/water partition coefficient and molecular weight as inputs with two input membership functions evaluated by two fuzzy rules. T he correlation coefficient of 0.97 occurred when log octanol/water partition coefficient and hydrogen bond donor activity were used as inputs with three input membership functions evaluated by three fuzzy rules. CONCLUSIONS: Fuzzy rule-based models are a realistic and promising tool that can be used to successfully model and predict skin permeability coefficients as well as or better than previous algorithms with fewer inputs.

Fuzzy Logic↗
PubMed1997

Feedback control in anaesthesia.

This review considers the some of the methods of automatic control which are usable in medicine. The features of each type of control system are explained and the advantages and disadvantages summarised. The author has attempted to maintain a balance between what is possible with the excellence of modern engineering, and what is feasible in the clinical area, and practical when working with patients. The problem of lack of knowledge of an individual patient is emphasised, as is the potential of fuzzy logic methods in the future.

Anesthesiology↗
PubMed1999

Self-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery.

Self-learning fuzzy logic control has the important property of accommodating uncertain, nonlinear, and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time without the need for detailed process modeling. In this study we utilize temporal knowledge of generated rules to improve control performance. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block of patients in the operating theater where the patient response exhibits high nonlinearity and individual patient dose requirements may vary fivefold during an operating procedure. We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performance of a self-learning fuzzy controller in this application. Using a T1 setpoint of 10% of baseline in 10 patients undergoing general surgery, we found a mean T1 error of 0.28% (SD = 0.39%) while accommodating a 0.25 to 0.38 mg/kg/h range in the mean atracurium infusion rate. This result compares favorably with more complex and computationally intensive model-based control strategies for atracurium infusion.

Algorithms↗
PubMed1999

Diagnostic monitoring in anaesthesia using fuzzy trend templates for matching temporal patterns.

A technique based on the concept of a 'fuzzy trend template' has been developed to identify characteristic patterns in multiple time-series. The method has its foundation in fuzzy logic and allows for the intuitive and transparent description of 'templates', which preserve nuances of vagueness, temporal relationships and quantitative descriptors. Evaluation of fuzzy trend templates can provide both belief and plausibility information for use in diagnostic applications. The technique has been applied to the diagnosis of specific problems in anaesthesia and has demonstrated sensitivity and specificity of 95 and 65%, respectively. Evaluation of fuzzy trend templates is, computationally, relatively efficient and has allowed a real-time implementation. The technique has the potential to be useful in any domain that requires temporal pattern recognition based on linguistic rules.

Anesthesia↗
PubMed2005

Comparison of trend detection algorithms in the analysis of physiological time-series data.

This paper presents a comparative performance analysis of various trend detection methods developed using fuzzy logic, statistical, regression, and wavelet techniques. The main contribution of this paper is the introduction of a new method that uses noise rejection fuzzy clustering to enhance the performance of trend detection methodologies. Furthermore, another contribution of this work is a comparative investigation that produced systematic guidelines for the selection of a proper trend detection method for different application requirements. Examples of representative physiological variables considered in this paper to examine the trend detection algorithms are: 1) blood pressure signals (diastolic and systolic); and 2) heartbeat rate based on RR intervals of electrocardiography signal. Furthermore, synthetic physiological data intentionally contaminated with various types of real-life noise has been generated and used to test the performance of trend detection methods and develop noise-insensitive trend-detection algorithms.

Algorithms↗
PubMed2006

Assessing water quality in rivers with fuzzy inference systems: a case study.

In recent years, fuzzy-logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental problems. In the present study, a methodology based on fuzzy inference systems (FIS) to assess water quality is proposed. A water quality index calculated with fuzzy reasoning has been developed. The relative importance of water quality indicators involved in the fuzzy inference process has been dealt with a multi-attribute decision-aiding method. The potential application of the fuzzy index has been tested with a case study. A data set collected from the Ebro River (Spain) by two different environmental protection agencies has been used. The current findings, managed within a geographic information system, clearly agree with official reports and expert opinions about the pollution problems in the studied area. Therefore, this methodology emerges as a suitable and alternative tool to be used in developing effective water management plans.

Environmental Monitoring↗
PubMed2005

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results.

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Adult↗
PubMed1997

Medical diagnosis, diagnostic spaces, and fuzzy systems.

The complexity and uncertainty of diagnostic information makes the diagnostic process difficult to learn, teach, and practice. Fuzzy logic methods, used successfully with complex industrial control problems, may be appropriate to model the range of uncertainties found in medical diagnostic information. A fuzzy systems model for use with diagnostic and other medical decisions is described. Combining a state space view of an animal in which each dimension of the space represents a variable of the animal with a fuzzy sets representation of the variables and states of the animal leads to a fuzzy systems model, which can be used to successfully diagnose disease. Partitioning the multidimensional state space of the animal into healthy and specific disease regions provides a diagnostic space for evaluating the health of the animal. When an input vector representing the variables of a sick animal is entered into the system, the model can provide a diagnosis and, potentially, a prognosis for that animal. The model can be implemented on a desktop computer for convenient use, and it provides a helpful geometric interpretation of the concepts of "diagnosis" and "prognosis" for teaching diagnostic reasoning. The fuzzy systems approach has advantages that are unavailable in other methods. The capability of fuzzy systems to act as universal approximators allows them to accommodate complex, nonlinear, imprecise, and even conflicting relationships to provide accurate knowledge representation. With these advantages over standard rule-based methods of modeling the medical diagnostic process, fuzzy expert systems have broad potential for use in medicine and warrant further study to determine their application and possible limitations.

Animal Diseases↗
PubMed1997

Self-learning fuzzy control of atracurium-induced neuromuscular block during surgery.

Self-learning fuzzy logic control has the important property of accommodating uncertain, non-linear and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time, without the need for detailed process modelling. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block (NMB) of patients in the operating theatre. Here, the patient response exhibits high non-linearity, and individual patient dose requirements can vary five-fold during an operating procedure. A portable control system was developed to assess the clinical performance of a simplified self-learning fuzzy controller in this application. A Paragraph (Vital Signs) NMB device monitored T1, the height of the first twitch in a train-of-four nerve stimulation mode. Using a T1 setpoint = 10% of baseline in ten patients undergoing general surgery, a mean T1 error of 0.45% (SD = 0.44%) is found while a 0.13-0.70 mg k-1 h-1 range in the mean atracurium infusion rate is accommodated. The result compares favourably with more complex and computationally-intensive model-based control strategies for the infusion of atracurium.

Atracurium↗
PubMed2003

Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations.

This paper applies fuzzy SDT (signal detection theory) techniques, which combine fuzzy logic and conventional SDT, to empirical data. Two studies involving detection of aircraft conflicts in air traffic control (ATC) were analysed using both conventional and fuzzy SDT. Study 1 used data from a preliminary field evaluation of an automated conflict probe system, the User Request Evaluation Tool (URET). The second study used data from a laboratory controller-in-the-loop simulation of Free Flight conditions. Instead of assigning each potential conflict event as a signal (conflict) or non-signal, each event was defined as a signal (conflict) to some fuzzy degree between 0 and 1 by mapping distance into the range [0, 1]. Each event was also given a fuzzy membership, [0, 1], in the set 'response', based on the perceived probability of a conflict or on the colour-coded alert severity. Fuzzy SDT generally reduced the computed false alarm rate for both the human and machine conflict systems, partly because conflicts just outside the conflict criterion used in conventional SDT, were defined by fuzzy SDT as a signal worthy of some attention. The results illustrate the potential of fuzzy SDT to provide, especially in exploratory data analysis, a more complete picture of performance in aircraft conflict detection and many other applications. Alternative analytic methods also using fuzzy SDT concepts are discussed.

Aircraft↗
PubMedDate not supplied

A new sodium-nitroprusside-infusion controller for the regulation of arterial blood pressure.

Automatic optimization of the infusion rate of sodium nitroprusside (SNP) is achieved by an integrated hardware-software closed-loop controller implemented as a small bedside device. A microprocessor-based blood pressure monitor controls an infusion pump. The shape of the arterial pressure wave is digitally sampled; its analysis incorporates artifact-detection and -rejection routines. The implemented algorithm applies a rule-based closed-loop control that incorporates fuzzy logic. The system models the decision-making ability of the expert, instead of trying to model the patient's physiologic dynamics. Several internal fuzzy-state variables are defined to achieve a clear understanding of mean arterial pressure (MAP) evolution with time. The system performance is very robust, employing, under all possible situations, a sort of "common sense." A clinical trial of the controller was conducted in 60 patients requiring vasodilation therapy for systemic arterial hypertension following cardiac surgery, 20 who had conventional manual control by an experienced nursing staff and 40 who had automated closed-loop control. The first 240 minutes of the postoperative period were closely watched, taking into account 1) the percentage of time during which MAP was within the 10-mmHg wide frame above the target pressure (target gap); 2) the mean difference of pressure values that crossed the boundary of the target gap; 3) the mean SNP-infusion rate. With automatic control, the time mean arterial pressure values were located in the target gap during the first hour amounted to 72.8 +/- 6.7%, vs 51.2 +/- 10.3% in the manual-control group. In the second hour, it was 79.3 +/- 2.5% vs 67.4 +/- 11.7% (p < 0.005).(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed2004

Control methods and the performance of the robotic testing system for human musculoskeletal joints.

Biomechanical testing of human musculoskeletal joints not only requires qualified testing machines and devices, but also needs an excellent control method to obtain better experimental results. In this paper, we take the human functional spinal unit (FSU) as an example to study how to improve the performance of the robotic testing system. First, the mechanical characteristics of FSU are described and the simplified model (rigid body-spring system) for the specimen is given. Because the location of the center of rotation (COR) of the specimen affects the performance of the system, a comprehensive analysis on the location of COR is carried out. The performance of the robotic testing system can also be improved through the improvement of the control method. Two control methods have been proposed in this paper: one is the improved hybrid control, the other is fuzzy logic control.

Algorithms↗
PubMed2006

A water quality monitoring network design methodology for the selection of critical sampling points: Part I.

The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.

Environmental Monitoring↗
PubMed2002

Fuzzy modelling for selecting headgear types.

The purpose of this study was to develop a computer-assisted inference model for selecting appropriate types of headgear appliance for orthodontic patients and to investigate its clinical versatility as a decision-making aid for inexperienced clinicians. Fuzzy rule bases were created for degrees of overjet, overbite, and mandibular plane angle variables, respectively, according to subjective criteria based on the clinical experience and knowledge of the authors. The rules were then transformed into membership functions and the geometric mean aggregation was performed to develop the inference model. The resultant fuzzy logic was then tested on 85 cases in which the patients had been diagnosed as requiring headgear appliances. Eight experienced orthodontists judged each of the cases, and decided if they 'agreed', 'accepted', or 'disagreed' with the recommendations of the computer system. Intra-examiner agreements were investigated using repeated judgements of a set of 30 orthodontic cases and the kappa statistic. All of the examiners exceeded a kappa score of 0.7, allowing them to participate in the test run of the validity of the proposed inference model. The examiners' agreement with the system's recommendations was evaluated statistically. The average satisfaction rate of the examiners was 95.6 per cent and, for 83 out of the 85 cases, 97.6 per cent. The majority of the examiners (i.e. six or more out of the eight) were satisfied with the recommendations of the system. Thus, the usefulness of the proposed inference logic was confirmed.

Adolescent↗
PubMed1998

Multiple-drug hemodynamic control using fuzzy decision theory.

A fuzzy-logic-based, automated drug-delivery system has been developed and validated on a nonlinear canine circulatory model for managing hemodynamic states. This controller features: 1) a fuzzy decision analysis module for patient status determination by assessing cardiac index, systemic vascular resistance index, and pulmonary vascular resistance index and 2) a fuzzy hemodynamic management module utilizing dopamine, phenylephrine, nitroprusside, and nitroglycerin for regulating mean arterial pressure, mean pulmonary arterial pressure, and cardiac output. A rule-based drug delivery scheduling program has been devised and incorporated to execute the therapeutic strategy as recommended by the decision analysis module. Compared to the existing controllers, this system is able to achieve a faster response time with a more secured and effective regulation. The simulation results have demonstrated the feasibility of the decision analysis process for automated management of the arterial and venous circulation with an expanded arsenal of pharmacological agents.

Animals↗