Color prediction model for hybrid multifilament fabric

In order to facilitate the design of a hybrid filament before spinning, a k-m (Kubelka-Munk) iteration model was proposed, which was based on the calculation method for reflectance of a translucent object and needed to be used in conjunction with a fabric model that can reflect the arrangement order of monofilaments. Therefore, the model can not only calculate the color of each point on the fabric surface, but also the mixed color of the fabric. Twenty fabrics with five different blending ratios of black monofilaments and white monofilaments, four multifilament fineness and three fabric weave types were woven. The relationship between the gray distribution of all points on the fabric surface captured by the camera in a DigiEye colorimeter and calculated by the k-m iteration model was analyzed, and the color difference between the mixed color of the fabric tested by the Datacolor spectrophotometer and that calculated by the k-m iteration model was calculated. The results show that the intersection distance and Pearson correlation coefficient between the gray histogram of the photographed fabric image and that of the calculated fabric image were 0.79 and 0.89, respectively. The average color difference obtained by the k-m iteration model was 0.92 Color Measurement Committee (2:1) units, which was best compared with the calculation results of other models. By discussing the fabric structure parameters causing the lightness difference, it was concluded that the calculated lightness was smaller than the measured lightness difference for fabric with a longer float length, smaller multifilament fineness and a larger black monofilament blending ratio.

Iris Location Algorithm Based on Union-Find-Set and Block Search

In view of the problem of unstable recognition effect and low robustness of a traditional iris location algorithm, an iris location algorithm based on union-find-set and block search is proposed. Firstly, the inner circle of the iris is roughly positioned by the method of retrieval, and then, the Hough transform is used to accurately locate the pupil. After that, the convolution operation is used to roughly locate the outer circle, and then, the original image is partitioned to search. And the grayscale change in the gray histogram of the screenshot is observed to accurately locate the outer circle. The obtained iris and the iris obtained by the traditional localization algorithm are processed by the same iris recognition algorithm. The results show that the proposed image is more effective in image recognition and has good robustness.

Adaptive Extraction Method Based on Time-Frequency Images for Fault Diagnosis in Rolling Bearings of Motor

In order to diagnose the faults of rolling bearings in motors via time-frequency analysis of bearing vibration signals quickly, this paper puts forward a method of extracting the main components from time-frequency images. A threshold is adaptively determined based on the gray histogram feature of the time-frequency images obtained from the vibration signals of the motor rolling bearings. Then, a mask template is generated by the threshold and a binarization processing. Based on a multiplication operation between the mask template and the original time-frequency image, the signal component with low energy in the time-frequency image is filtered out, and only the main components with high energy is remained for fault diagnosis, which is convenient for the subsequent identification of the faults for motor rolling bearings. The main components in the time-frequency images can be retained adaptively with the thresholds determined by the time-frequency images themselves.

A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy.

Physical Simulation of Solid Liquid Mixture Stability in Molten Aluminum

Mixing behaviour is a very important process for preparation of aluminum foam by foaming in melt route due to the dispersion of foaming agent, thickening agent or stabilizing agent being added into the liquid metal. However, some unfavourable factors, such as active chemical properties, easy oxidation and evaporation, no fixed shape and opacification of liquid metal at high temperature, result in it is very difficult to observe the melt stirring and changing process. The mixing behaviour of molten aluminium matrix composite was simulated on the basis of water-based system. In addition to visual observation of the mixing effect, gray histogram was introduced to analyse the dispersion of solid particles in aqueous solution quantificationally. The whole experimental procedures were recorded by MV-Capture industrial camera and the intercepted image information was converted to digital information, i.e. standard deviation originated from the gray histogram. The results showed that the stabilization of aqueous solution can be characterized by the obtained digital information and the physical simulation can provide guidance for the fabrication of metal foams.

Research on recognition of coal and gangue based on image processing

In order to avoid the waste of water resources and environmental pollution caused by separating coal and gangue in the traditional methods, a novel method based on image processing is proposed in this paper. Firstly the image of coal or gangue is preprocessed. Then the mean value of gray histogram is extracted which serves as the statistical feature value to initially recognize coal and gangue. Then the textural feature is extracted from the image which is based on an adaptive window of texture analysis. The adaptive window size is determined by the contrast texture feature parameter. The adaptive window of texture analysis can improve the discriminability of coal and gangue. This method not only considers the image’s gray feature but also utilizes the image’s spatial information, so the recognition precision is improved. This method provides new ideas for dry separation technology.