Jin Fujiang

The study of detect the density of knitted fabric online based on the image

The measurement to the density of knitted fabric by person was time-consuming and discontinuous, which cannot satisfy the demand for controlling the density of knitted fabric online. In the paper, the method to detect the density of knitted fabric online based on the image is put forward. The image of knitted fabrics was dealt with the method of dynamic binarization algorithms. The yarns which can represent the vertical coils of knitted fabrics are extracted base on the texture of knitted fabrics; the number of yarns in the image can be counted. Then the coils are transformed to be regular geometry based on its shapes and get the number of coils. The density of knitted fabric can be calculated based on these data. At last, the result of experiments shows that this method is effective.

Density detection for knitted fabric based on image space domain method

In textile processing, the density of knitted fabric is mostly measured offline by using weft mirrors, which is time-consuming and discontinuous. Meanwhile, offline measurement cannot meet the requirements for online control of the processing. An image based method is proposed in this paper to deal with the online detection of the density of knitted fabric. First, by binarizing and filtering the image of fabric, the clear texture of the fabric can be achieved. After extracting the yarns based on the feature of the texture, the Hough transform is used to recognize the extracted yarns. Then the horizontal density of the fabric can be calculated. Next, through analyzing the geometric features of the fabrics coils, the mean distance between each two coils can be obtained. Then the vertical density of the fabric can also be calculated. From the horizontal and the vertical density, the general density of the detected fabric is obtained easily. At last, the results of experiments demonstrate the effectiveness of the proposed method.

Fault-tolerant control of nonlinear systems with nonlinearly parameterized fuzzy approximators

A novel adaptive fuzzy control method is proposed in this paper for a class of nonlinear disturbed systems which are subject to both lock-in-place and loss of effectiveness actuator faults. Nonlinearly parameterized fuzzy systems are employed to approximate the unknown functions in the control design, and additional control is included to deal with the approximation error as well as the external disturbance. The proposed control method can tolerate the occurred actuator faults adaptively such that the closed-loop stability of the controlled system and satisfactory output tracking to the given reference can be achieved.

The study for multi-variables inference control system of delignification reaction

The digester process model is analyzed by using the delignification kinetics theory, which according to the demand of cleaner production to degrade residual alkaline concentration and pollutant emitted. A multi-variable inference control algorithm based on soft sensing approach is designed, in which the active alkaline concentration and kappa number is used as control variable. The result of multi-variable inference control system, from actual continuous kraft cooking, is shown that a mass of residual alkaline concentration and the dosage of alkaline can be decreased, and the aim of cleaner production and protecting environmental was attained.