A gap detection tactility test for sensory deficits associated with carpal tunnel syndrome

An automated gap detection tactility test was investigated for quantifying sensory deficits associated with carpal tunnel syndrome (CTS). The test, which involved sensing a tiny gap in an otherwise smooth surface by probing with the finger, had functional resemblance to many work-related tactile activities such as detecting scratches or surface defects. Gap detection thresholds were measured using the converging staircase method of limits paradigm. Sixteen normal subjects between 21 and 66 years of age were tested for studying important factors affecting gap detection thresholds. Actively probing with the index finger had a threshold almost an order of magnitude more sensitive (mean = 0·19mm, SD = 0·llmm) than passive touch (mean =1·63mm. SD = 0·62mm), which was similar to two-point discrimination. Average thresholds decreased by 24% as contact force increased from 25 to 75?g. Performance in this tactility test quickly stabilized and showed little learning effects over the period of the test, as evidenced by the lack of significant differences between six replicates. The results were highly repeatable. No significant threshold differences were observed between test and retest trials on different days, or between dominant and non-dominant hands. A contact force of 50?g was recommended as optimal for this test since it required moderate force but resulted in a smaller threshold compared with 25 or 75?g. A companion study was conducted using eight normal subjects and ten subjects diagnosed as having CTS. Average gap detection threshold, when finger probing was allowed, was 0·20?mm (SD = 0·11?min) for the normal subjects and increased two-fold to 0·40?mm (SD = 0·19?mm) for the CTS subjects. Average gap detection threshold, when the finger probing was not allowed, was 1·71?mm (SD = 0·53mm) for the normal subjects and increased by 48% to 2·53?mm (SD = 0·87?mm) for the CTS subjects. The results suggest that people suffering from CTS may experience similar functional deficits in daily living and work activities. The small inter-subject variability makes this test a candidate for having utility as a monitoring test for loss of cutaneous tactile sensitivity.     

Performance of multiclass BCMP model for computer system based on fuzzy set theory

The purpose of this paper is to combine the ability of fuzzy set to represent more realistic situations with the well-established traditional queueing system model problem. We are forced to employ subjective probabilities when there is no information about a model or some parameters of a model are vague. The information and data are very fuzzy, because they are frequently very little, 'and may be sometimes obtained from experts subjectively. We apply fuzzy set theory to the closed multiclass model with the fuzzy queues. thus, we represent the characteristic and performance of the closed multiclass model based on the proposed fuzzy set theory.     

A framework for linguistic relevance feedback in content-based image retrieval using fuzzy logic

We describe a new approach for exploiting relevance feedback in content-based image retrieval (CBIR). In our approach to relevance feedback we try to capture more of the users’ relevance judgments by allowing the use of natural language like comments on the retrieved images. Using methods from fuzzy logic and computational intelligence we are able to reflect these comments into new targets for searching the image database. Such enhanced information is utilized to develop a system that can provide more effective and efficient retrieval.     

A hybrid model for the prediction of the linguistic origin of surnames

The prediction of the linguistic origin of surnames is a basic functionality required in the design of high-quality multilanguage speech synthesizers. The assignment of a given string representing a surname to a specific language is typically based on a set of rules which can hardly be written in an explicit form. The approach we propose faces this problem combining a rule-based system with a module based on evidential reasoning and a module based on neural networks. The resulting hybrid system combines the different sources of information, merging both knowledge from experts on linguistics and knowledge automatically acquired using learning from examples. The system has been validated on a large database containing surnames belonging to four different languages, showing its effectiveness for real-world applications.     

A joint economic-lot-size model for purchaser and vendor in fuzzy sense

This paper investigates a group of computing schemas for joint economic lot size as fuzzy values of the economic lot size model for purchaser and vendor. We express the fuzzy order quantity/production lot size for the purchaser/vendor as the normal triangular fuzzy number (q₁, q₀, q₂) and then we solve the aforementioned optimization problem under the condition 0 < q₁ < q₀ < q₂. We find that, after defuzzification, the joint total relevant cost is slightly higher than in the crisp model.     

A review of soft consensus models in a fuzzy environment

In the consensus reaching processes developed in group decision making problems we need to measure the closeness among experts’ opinions in order to obtain a consensus degree. As it is known, to achieve a full and unanimous consensus is often not reachable in practice. An alternative approach is to use softer consensus measures, which reflect better all possible partial agreements, guiding the consensus process until high agreement is achieved among individuals. Consensus models based on soft consensus measures have been widely used because these measures represent better the human perception of the essence of consensus. This paper presents an overview of consensus models based on soft consensus measures, showing the pioneering and prominent papers, the main existing approaches and the new trends and challenges.     

A fuzzy model for supplier selection in quantity discount environments

Traditionally, supplier selection should simultaneously take into account numerous heterogeneous criteria, and then is a tedious task for the purchasing decision makers. It becomes especially complicated when quantity discounts are considered at the same time. Under such manner, most studies often formulate such a problem as a Multi-Objective Linear Programming (MOLP) problem, and then scale it down to a Mixed Integer Programming (MIP) problem to handle the inherited multi-objectives simultaneously. However, this approach often neglects to consider scaling and subjective weighting issues. In order to ease the problem mentioned above and to obtain a more reasonable compromise solution for allocating order quantities among suppliers with their quantity discount rate offered, the Analytical Hierarchy Process (AHP) and fuzzy compromise programming are introduced in this study. An illustrated example is presented to demonstrate the proposed model and to illuminate two kinds of attitudes for decision makers. The information from the experiments can be utilized further to explain the suppliers’ possible improvement and to help create win–win policies.     

Constructing and applying an improved fuzzy time series model: Taking the tourism industry for example

This study develops an improved fuzzy time series models for forecasting short-term series data. The forecasts were obtained by comparing the proposed improved fuzzy time series, Hwang’s fuzzy time series, and heuristic fuzzy time series. The tourism from Taiwan to the United States was used to build the sample sets which were officially published annual data for the period of 1991–2001. The root mean square error and mean absolute percentage error are two criteria to evaluate the forecasting performance. Empirical results show that the proposed fuzzy time series and Hwang’s fuzzy time series are suitable for short-term predictions.     

A combination of hidden Markov model and fuzzy model for stock market forecasting

This paper presents a novel combination of the hidden Markov model (HMM) and the fuzzy models for forecasting stock market data. In a previous study we used an HMM to identify similar data patterns from the historical data and then used a weighted average to generate a ‘one-day-ahead’ forecast. This paper uses a similar approach to identify data patterns by using the HMM and then uses fuzzy logic to obtain a forecast value. The HMM's log-likelihood for each of the input data vectors is used to partition the dataspace. Each of the divided dataspaces is then used to generate a fuzzy rule. The fuzzy model developed from this approach is tested on stock market data drawn from different sectors. Experimental results clearly show an improved forecasting accuracy compared to other forecasting models such as, ARIMA, artificial neural network (ANN) and another HMM-based forecasting model.     

A design for a class of nonlinear systems with delay time using fuzzy logic

 The goal of this paper is to design a controller for a class of nonlinear systems with delay time using fuzzy logic. The control scheme considered in this paper integrates a fuzzy component and a sliding control component. In the former, the fuzzy system can be considered as a universal approximator to approximate the unknown functions in plant. In the latter, a variable structure control with a sector guarantees the global stability of the closed-loop system when a variable, involving tracking error, travels outside of the sector. The adaptive laws to adjust the parameters in the system are developed based on the Lyapunov synthesis approach. It is shown that the proposed adaptive controller guarantees tracking error, between the outputs of the considered system and desired␣values, to be asymptotical in decay.     

A fuzzy stochastic multi-criteria model for the selection of urban pervious pavements

Multi-criteria decision making methods (MCDM) have been widely used throughout the last years to assist project contractors in selection processes related to the construction field. Sustainable urban drainage systems (SUDS) are an especially suitable discipline to implement these techniques, since they involve important impacts on each branch of sustainability: economy, environment and society. Considering that pervious pavements constitute an efficient solution to manage urban stormwater runoff as a source control system, this paper presents a multi-criteria approach based on the Integrated Value Model for Sustainable Assessments (MIVES) method to facilitate their proper selection. Given the lack of accurate information to shape the behavior of the alternatives regarding some of the criteria defining the decision-making environment, a series of variables are modeled by executing stochastic simulations based on the Monte Carlo methods. Additionally, a group of ten experts from various sectors related to water management was requested to provide their opinions about the importance of the set of selected criteria, according to the comparison levels of the Analytic Hierarchy Process (AHP). These judgments are converted into triangular fuzzy numbers, in order to capture the vagueness that human attitude entails when making judgments. A case of study in which the three major types of pervious pavements (porous asphalt, porous concrete and interlocking concrete pavers) are evaluated is presented to demonstrate the potential of the model.     

A credibility-based fuzzy location model with Hurwicz criteria for the design of distribution systems in B2C e-commerce

Facility location problem is one of the most critical elements in the design of distribution systems, and numerous studies have focused on this issue. However, facility location theory and guidelines for B2C firms are sparse. In this paper, with regard to the customer characteristics peculiar to B2C e-commerce and the turbulence of the competitive market, a new fuzzy location model is proposed to optimize the distribution system design in B2C e-commerce. The model adopts a hierarchical agglomerative clustering method to classify customers and estimate the fuzzy delivery cost. At the same time, due to the turbulence of competitive market, both market supply and customer demand are treated as fuzzy variables in the model. Afterward, the credibility measure and Hurwicz criterion are introduced to convert the model into a crisp one which has NP-hard complexity. In order to solve the crisp model, an improved genetic algorithm with particle swarm optimization is developed. Finally, the computational results of some numerical examples are used to illustrate the application and performance of the proposed model and algorithm.     

A genetic fuzzy expert system for automatic question classification in a competitive learning environment

Intelligent tutoring systems are efficient tools to automatically adapt the learning process to the student’s progress and needs. One of the possible adaptations is to apply an adaptive question sequencing system, which matches the difficulty of the questions to the student’s knowledge level. In this context, it is important to correctly classify the questions to be presented to students according to their difficulty level. Many systems have been developed for estimating the difficulty of questions. However the variety in the application environments makes difficult to apply the existing solutions directly to other applications. Therefore, a specific solution has been designed in order to determine the difficulty level of open questions in an automatic and objective way. This solution can be applied to activities with special temporal and running features, as the contests developed through QUESTOURnament, which is a tool integrated into the e-learning platform Moodle. The proposed solution is a fuzzy expert system that uses a genetic algorithm in order to characterize each difficulty level. From the output of the algorithm, it defines the fuzzy rules that are used to classify the questions. Data registered from a competitive activity in a Telecommunications Engineering course have been used in order to validate the system against a group of experts. Results show that the system performs successfully. Therefore, it can be concluded that the system is able to do the questions classification labour in a competitive learning environment.     

On linguistic approximation in the frame of fuzzy logic deduction

 This paper presents a new linguistic approximation algorithm and its implementation in the frame of fuzzy logic deduction. The algorithm presented is designed for fuzzy logic deduction mechanism implemented in Linguistic Fuzzy Logic Controller (LFLC).     

A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process

The fuzzy analytic hierarchy process (FAHP) has been used to solve various multi-criteria decision-making problems where trapezoidal type-1 fuzzy sets are utilized in defining decision-makers’ linguistic judgment. Previous theories have suggested that interval type-2 fuzzy sets (IT2 FS) can offer an alternative that can handle vagueness and uncertainty. This paper proposes a new FAHP characterized by IT2 FS for linguistic variables. Differently from the typical FAHP, which directly utilizes trapezoidal type-1 fuzzy numbers, this method introduces IT2 FS to enhance judgment in the fuzzy decision-making environment. This new model includes linguistic variables in IT2 FS and a rank value method for normalizing upper and lower memberships of IT2 FS. The proposed model is illustrated by a numerical example of work safety evaluation. Comparable results are also presented to check the feasibility of the proposed method. It is shown that the ranking order of the proposed method is consistent with the other two methods despite difference in weight priorities.     

A fuzzy multi-objective two-stage DEA model for evaluating the performance of US bank holding companies

This paper investigates the association between the performance of bank holding companies (BHCs) and their intellectual capital (IC). We start from constructing an innovation ratio two-stage DEA model and then applies fuzzy multiple objective programming approaches to calculate the efficiency score. This model provides a common scale for comparing performance, increases the discriminating power, and simplifies the calculation process. The links between IC and the BHCs’ performance are also investigated by means of the truncated-regression model, and a positive relationship between them is found. The decision-making matrix combined with an efficiency improvement map proposed in this study can clearly define the benchmark that can be emulated by inefficient BHCs and help BHC managers to develop appropriate strategies needed to enhance their overall efficiency.     

A fuzzy analysis of country-size argument for the Feldstein-Horioka puzzle

This study aims to analyze the effect of country size, represented by relative Gross National Products (GNP), on the association between domestic investment and saving, using data from a panel of 21 OECD countries. The countries are clustered into four groups with respect to their relative country sizes with an application of Fuzzy c-Means clustering technique. The novelty of this approach is that it is an unsupervised method that generates membership values between zero and one instead of binary values that take values of zero or one only. In addition to this it has the advantages of its tolerance to imprecise data and the ease of understanding. The results show that the saving retention coefficients are greater for larger countries except for the cluster which contains the largest country. Thus, this work presents only partial evidence that the country size affects the relationship of domestic saving and investment.     

A new approach to fuzzy estimation of Takagi-Sugeno model and its applications to optimal control for nonlinear systems

An efficient approach is presented to improve the local and global approximation and modelling capability of Takagi-Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy. The main problem is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the use of the T-S method because this type of membership function has been widely used during the last two decades in the stability, controller design and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S method with optimized performance in approximating nonlinear functions. A simple approach with few computational effort, based on the well known parameters’ weighting method is suggested for tuning T-S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method developed here in control applications. Illustrative examples of an inverted pendulum and Van der Pol system are chosen to evaluate the robustness and remarkable performance of the proposed method and the high accuracy obtained in approximating nonlinear and unstable systems locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the algorithm.     

A biomedical system based on fuzzy discrete hidden Markov model for the diagnosis of the brain diseases

Because it is a non-invasive, easy to apply and reliable technique, transcranial doppler (TCD) study of the adult intracerebral circulation has increased enormously in the last 10 years. In this study, a biomedical system has been implemented in order to classify the TCD signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. The diseases were investigated cerebral aneurysm, brain hemorrhage, cerebral oedema and brain tumor. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, the linear predictive coding analysis and cepstral analysis were applied in order to extract the cepstral and delta-cepstral coefficients in frame level as feature vectors. In the classification stage, discrete hidden Markov model (DHMM) based methods were used. In order to avoid loosing information due to vector quantization and to increase the classification performance, a fuzzy approach based similarity was applied to implement the DHMM. The performance of the proposed Fuzzy DHMM (FDHMM) was compared with some methods such as DHMM, artificial neural network (ANN), neuro-fuzzy approaches and obtained better classification performance than these methods.     

A linguistic approach for the control of information flow in abattlefield environment

We developed three linguistic statements to describe user information desires in a battlefield information environment. These rules are based on end-user interest in each track report generated from radars across the battlefield. Along with these rules of user interest, a linguistic statement describing communications systems capabilities at each node was created. These linguistic statements were converted to fuzzy variables and these variables were used as network control devices in a simulation model. The model results show that effective communications control can be exercised by these simple rules