Gaoming Jiang

In this paper, a non-subsampled wavelet-based contourlet transform (NWCT) is applied in warp-knitted fabric defect segmentation. Compared with the traditional contourlet transform, wavelet transform takes the place of Laplacian pyramid in NWCT and the directional filter bank is non-subsampled. The wavelet transform with improved wavelet threshold is put to use, and the original fabric image can be decomposed into low-frequency approximate coefficient A and high-frequency detail coefficients V, H, and D. The high-frequency detail coefficients are processed by the non-subsampled directional filter bank to get directional sub-band coefficients. Afterward, the effective sub-band coefficients based on regional energy are chosen to reconstruct V, H, and D. And the reconstructed fabric image will be achieved by inverse non-subsampled wavelet-based contourlet transform. The adaptive threshold method and morphological processing are used to obtain the legible defect profile. The experiment demonstrates that NWCT can achieve the positive segmentation regarding the common defects, such as broken warp, width barrier, and oil, and has excellent performance on these directional defects and regional defects. It is acknowledged that NWCT will provide a new way to detect warp-knitted fabric defects automatically.

In order to create realistic loop primitives suitable for the fast computer-aided design (CAD) of the flat knitted fabric, we have a research on the geometric model of the loop as well as the variation of the loop surface. Establish the texture variation model based on the changing process from the normal yarn to loop that provides the realistic texture of the simulative loop. Then optimize the simulative loop based on illumination variation. This paper develops the computer program with the optimization algorithm and achieves the loop simulation of different yarns to verify the feasibility of the proposed algorithm. Our work provides a fast CAD of the flat knitted fabric with loop simulation, and it is not only more realistic but also material adjustable. Meanwhile it also provides theoretical value for the flat knitted fabric computer simulation.

Based on a further study on knitting and jacquard principles, this paper presents a mathematical design model to make computer-aided design of warp-knitted jacquard spacer fabrics more efficient. The mathematical model with matrix method employs three essential elements of chain notation, threading and Jacquard designing. With this model, the processing to design warp-knitted jacquard spacer fabrics with CAD software is also introduced. In this study, the sports shoes which have separated functional areas according to the feet structure and characteristics of movement are analysed. The results show the different patterns on Jacquard spacer fabrics that are seamlessly stitched with jacquard technics. The computer-aided design method of warp-knitted jacquard spacer fabrics is efficient and simple.

Intended to study the influence of different production parameters on sound absorption of activated carbon fiber felts, viscose-based activated carbon fiber felts acquired from different production parameters were prepared and taken to test the sound absorption coefficients in normal incidence by means of transfer function method, within an acoustic range of 250–6300 Hz in the impedance tube. Analysis was made to find the influence of carbonization temperature, carbonization rate, activation temperature, and activation time on sound absorption properties. Sound absorption coefficients at medium–low frequencies strikingly climbed with the increasing frequency, while fluctuating at high frequencies. Other production parameters remaining constant, sound absorption coefficients increased at medium–low frequencies with the rising carbonization temperature and the extended activation time, while subsequently decreased with the rising carbonization rate and carbonization temperature. At the same time, sound absorption coefficients decreased at high frequencies with the rising carbonization temperature and carbonization rate, while increased but later decreased with the rising activation temperature, and increased again with the added time in activation.

This paper is intended to study the influence of different factors on the sound absorption properties of composite structure with activated carbon fiber felts. Activated carbon fiber felts made from viscose fiber mats were prepared and later combined with perforated panels to form four different composite sound absorption structures. Based on the transfer function method, the impedance tube was used to test the sound absorption coefficients of composite structure in an acoustic range of 80–6300 Hz frequencies. Analysis was made to discuss the influence of such factors on the sound absorption properties as the position of activated carbon fiber felts, thickness, and air space. The results demonstrated that the composite structure displayed different sound absorption properties at different frequencies. Perforated panels played the dominant role in sound absorption by the occurrence of resonance at 80–3500 Hz frequencies, while porous materials contributed the most at 3500–6300 Hz frequencies. At 80–3500 Hz frequencies, the best performance could be observed in the third type of composite structure with changes in the position of activated carbon fiber; the first resonance frequency of the first type of composite structure and perforated panel structure was basically the same, and that of the remaining three types significantly shifted towards the low frequencies with the same scale. In smaller thickness range, with the increase in the thickness of activated carbon fiber felts, sound absorption coefficients of the first and second types of composite structure increased, the first resonance frequency of the first type showing no apparent shift towards the low frequencies compared with what was shown in the second type; but when the thickness arrived at 15.6 mm, sound absorption properties of the composite structure had similar traits to that performed by porous materials in an acoustic range of 80–6300 Hz frequencies. With the increase in the distance of air space, sound absorption properties were improving at 80–650 Hz frequencies but decreasing at 650–3500 Hz frequencies, the first resonance frequency moving towards the low frequencies. At 3500–6300 Hz frequencies, as the position of activated carbon fiber felts and the distance of air space varied, sound absorption coefficients were basically unchanged; while as thickness increased, sound absorption coefficients improved.

In this paper, we propose a new approach to intelligently segment jacquard warp-knitted fabric images by combining wavelet texture decomposition, multiresolution Markov random field (MRF) modeling and Bayesian parameter estimation. Firstly, we use a flat scanner to capture eight-bit grayscale images of jacquard fabrics and adopt the Gaussian low-pass filter to decrease pixel variation due to discordant light reflection arising from uneven jacquard fabric surface. To overcome the incapacity of single resolution, multiresolution wavelet texture decomposition is employed, inspired by human visual sense procedure. Next, both intra-scale and inter-scale information are taken into account by the MRF model, in which a modified feature field model of the MRF, containing spatial noise with zero-mean Gaussian distribution, is presented in light of the inherent characteristics of jacquard warp-knitted fabric image. Afterward, an adaptive weighting function is used to weaken the defect of the potential parameter set empirically during the process of parameter estimation and image segmentation. Experimental results, used to verify the performance of the proposed algorithm especially the main novelties, prove that the approach is feasible and applicable.