Fuzzy Logic Programming and Fuzzy Control

We show that it is possible to base fuzzy control on fuzzy logic programming. Indeed, we observe that the class of fuzzy Herbrand interpretations gives a semantics for fuzzy programs and we show that the fuzzy function associated with a fuzzy system of IF-THEN rules is the fuzzy Herbrand interpretation associated with a suitable fuzzy program.     

Measurement and comparative analysis of driver’s perception–reaction time to green phase at the intersections with and without a countdown timer

This paper proposes a method based on digital image processing for measuring drivers’ perception–reaction time (PRT) to the green phase. The detection of the onset of a green signal was based on the RGB color model. The detection of the start of the vehicle was based on the frame difference. The driver’s perception–reaction time was equal to the difference between the frame of the onset of the green signal and the frame of the start of the vehicle divided by the video frame rate. Drivers’ PRT with and without a countdown timer were comparatively analyzed. First, the means and variances of drivers’ PRT with a timer and without a timer were compared. Second, Normal, Lognormal, Gamma and Weibull distributions were used to fit the PRT data. Third, the fuzzy c-means clustering was utilized for PRT classification and comparison. The results show that the method for measuring drivers’ PRT based on digital image processing was effective. The drivers’ PRT was decreased from 2.12 s to 1.48 s with countdown signals. Weibull distribution appeared to best fit the PRT with No-Timer and Lognormal distribution appeared to best fit the PRT with Timer. The PRT data were classified into three groups: “Fast”, “Moderate” and “Slow”. Clustering centers without Timer were 1.16 s, 2.47 s and 3.98 s respectively. Clustering centers with Timer were 0.95 s, 1.64 s and 2.70 s respectively. The clustering centers can be regarded as reference values of drivers’ starting response for microscopic traffic simulation software.     

A latent class distance association model for cross‐classified data with a categorical response variable

In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low‐dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented.     

The effects of bilingual language proficiency on recall accuracy and semantic clustering in free recall output: evidence for shared semantic associations across languages

Two experiments investigated how well bilinguals utilise long-standing semantic associations to encode and retrieve semantic clusters in verbal episodic memory. In Experiment 1, Spanish-English bilinguals (N?=?128) studied and recalled word and picture sets. Word recall was equivalent in L1 and L2, picture recall was better in L1 than in L2, and the picture superiority effect was stronger in L1 than in L2. Semantic clustering in word and picture recall was equivalent in L1 and L2. In Experiment 2, Spanish-English bilinguals (N?=?128) and English-speaking monolinguals (N?=?128) studied and recalled word sequences that contained semantically related pairs. Data were analyzed using a multinomial processing tree approach, the pair-clustering model. Cluster formation was more likely for semantically organised than for randomly ordered word sequences. Probabilities of cluster formation, cluster retrieval, and retrieval of unclustered items did not differ across languages or language groups. Language proficiency has little if any impact on the utilisation of long-standing semantic associations, which are language-general.     

飞行员情境认知的模糊综合评判

借用环境科学中环境质量评价的概念和方法,建立了飞行员情境认知的多级模糊综合评判模型,并用该模型对飞行员的情境认知进行了综合评判。研究表明,用模糊综合评判方法评价飞行员的情境认知可以提供真实、全面的飞行认知信息,为飞行员的训练有效性及安全性提供可靠依据。     

A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery

The clustering of hyperspectral images is a challenging task because of the high dimensionality of the data. Sparse subspace clustering (SSC) algorithm is one of the popularly used clustering algorithm for high dimensionality data. However, SSC has not fully used the spectral and spatial information during similarity matrix construction based on single sparse representation coefficient for hyperspectral Imagery (HSI) clustering. In this paper, two novel similarity matrix construction methods named as Cosine-Euclidean similarity matrix (abbreviated as CE) and Cosine-Euclidean dynamic weighting similarity matrix (abbreviated as CEDW) are proposed for HSI clustering. They can combine the high spectral information and rich spatial information. Firstly, CE utilizes the cosine similarity of spectral information based on overall sparse representation vectors and classical Euclidean distance of spatial information to construct a novel similarity matrix. Secondly, inheriting CE merits, dynamic weighting adjustment method is introduced to CEDW for some external influence factors to the HSI information. Several experiments on HSI demonstrated that the proposed algorithms are effective for HSI clustering.     

Affinity propagation: An exemplar-based tool for clustering in psychological research

Affinity propagation is a message-passing-based clustering procedure that has received widespread attention in domains such as biological science, physics, and computer science. However, its implementation in psychology and related areas of social science is comparatively scant. In this paper, we describe the basic principles of affinity propagation, its relationship to other clustering problems, and the types of data for which it can be used for cluster analysis. More importantly, we identify the strengths and weaknesses of affinity propagation as a clustering tool in general and highlight potential opportunities for its use in psychological research. Numerical examples are provided to illustrate the method.     

A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data

Abstract

Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this article, a latent block distance-association model is proposed to apply block clustering to the outcomes of two categorical variables while the cluster centers are represented in a low dimensional space in terms of a distance-association model. This model is particularly useful for contingency tables in which both the rows and the columns are characterized as profiles of sets of response variables. The parameters are estimated under a Poisson sampling scheme using a generalized EM algorithm. The performance of the model is tested in a Monte Carlo experiment, and an empirical data set is analyzed to illustrate the model.     

Coherence and correspondence criteria for rationality: experts' estimation of risks of sexually transmitted infections

The aim of this study is to examine both coherence and correspondence criteria for rationality in experts' judgments of risk. We investigated biases in risk estimation for sexually transmitted infections (STIs) predicted by fuzzy‐trace theory, i.e., that specific errors would occur despite experts' knowledge of correct responses. One hundred twenty professionals with specific knowledge of STI risks in adolescents were administered a survey questionnaire to test predictions concerning: knowledge deficits (producing underestimation of risks); gist‐based representation of risk categories (producing overestimation of condom effectiveness); retrieval failure for risk knowledge (producing lower risk estimates); and processing interference in combining risk estimates (producing biases in post‐test diagnosis of infection). Retrieval was manipulated by asking estimation questions that “unpacked” the STI category into infection types or did not specify infection types. Other questions differentiated processing biases from knowledge deficits or retrieval failure by directly providing requisite knowledge. Experts' knowledge of STI transmission and infection risks was verified empirically. Nevertheless, under predictable conditions, they misestimated risk, overestimated the effectiveness of condoms, and also suffered from processing biases. When questions provided better retrieval supports (unpacked format), risk estimates improved. Biases were linked to gist representations, retrieval failures, and processing errors, as opposed to knowledge about STIs. Results support fuzzy‐trace theory's dual‐process assumptions that different types of errors are dissociated from one another, and separate failures of coherence and correspondence among the same sample of experts. Copyright © 2005 John Wiley & Sons, Ltd.