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Tactile perception plays a critical role in the interaction of humans and environment. It begins with the mechanical stimulation induced by friction and is processed in the somatosensory cortex. To quantify the tactile perceptions of textile fabrics, the mechanical properties of fabrics and the features extracted from the friction and vibration signals were correlated with the subjective sensation rated by questionnaires. Meanwhile, the technique of functional magnetic resonance imaging (fMRI) was used to identify the brain areas responsible for the tactile perception of textile fabrics. The results showed that during the tactile perception of textile fabrics, the coefficient of friction increased with the increasing normal load, indicating that the deformation mechanism of skin was relevant to the friction of skin against fabrics. The features of spectral centroid (SC), coefficient of friction, and diameter and critical buckling force of fiber had a strong correlation with the perceived fineness, slipperiness, and prickliness of fabrics, respectively. The postcentral gyrus, supramarginal gyrus, and precentral gyrus, with the corresponding functional regions of the primary somatosensory cortex (SI), secondary somatosensory cortex (SII), primary motor cortex (MI), and secondary motor cortex (MII), were involved with the perceptions of fabric textures. The fiber properties and fabric surface structures that caused the multidimensional feelings tended to induce the large area, intensity, and percent signal change (PSC) of brain activity. This study is meaning for evaluating the tactile stimulation of textile fabrics and understanding the cognitive mechanism in the tactile perception of textile fabrics.

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In this paper, an hybrid search algorithm, called TS&QQ, is proposed to solve the job shop scheduling problem. The algorithm combines tabu search and quasi physics and quasi-human method. It uses tabu search to find the feasible solution and used quasi-physics and quasi human method to trap from the local optimal, called off trap strategy. This algorithm has been tested on many common problem benchmarks with various sizes.Computational experiments show that this algorithm is efficient heuristic algorithm for the problem and is better than BQ&Q , HA and IQ&Q.