Objective Evaluation of Seam Pucker Using Artificial Intelligence Part II: Method of Evaluating Seam Pucker

1999 ◽  
Vol 69 (11) ◽  
pp. 835-845 ◽  
Author(s):  
Chang Kyu Park ◽  
Tae Jin Kang
Keyword(s):  
Artificial Intelligence ◽  
Objective Evaluation ◽  
Seam Pucker

Investigation of interactions between fabric performance, sewing process parameters and seam pucker of shirt fabric

2021 ◽  
Vol 16 ◽  
pp. 155892502110203
Author(s):  
Daoling Chen ◽  
Pengpeng Cheng ◽  
Yonggui Li
Keyword(s):  
Regression Analysis ◽  
Process Parameters ◽  
Linear Regression Analysis ◽  
Objective Evaluation ◽  
Multiple Linear Regression Analysis ◽  
Complex Dynamic ◽  
Dynamic Interactions ◽  
Sewing Thread ◽  
Sewing Threads ◽  
Seam Pucker

Seam pucker is a common problem in sewing. It not only affects the appearance of product, but also affects product performance. The purpose of this study is to quantify the complex dynamic interactions between fabric performance, sewing process parameters and seam pucker. In order to solve the problem of shirt seam pucker, this study selected four kinds of shirt fabrics, three kinds of polyester sewing threads, three kinds of stitch density and four kinds of seam types for experiments. Through unitary regression analysis, the subjective and objective evaluation results are consistent. Further analysis the results of objective experiment revealed that fabric performances, seams type, sewing thread and stitch densities all have impact on seam pucker. Meanwhile also find out the sewing process parameters for the four fabrics when the seam shrinkage’s were smallest, so it’s helpful for the apparel enterprises to improve seam quality. Multiple linear regression analysis of experimental results show that fabric performances has the greatest influence on seam pucker, thickness, weight and warp density of fabric properties significantly affect seam pucker. And as the breaking elongation of sewing thread increases, seam pucker also increases. Stitch densities and seam type has the least affected on seam pucker, they affect the seam pucker by changing the extension of stitch and thickness of fabric at the seam, respectively. Seam type has greater impact on fabrics that are prone to seam pucker, seam type T1 get larger seam shrinkage than T4. Finally, the complex dynamic interactions was quantified and expressed through mathematical models.

Objective Evaluation of Seam Pucker Using Complex Wavelet and Fractal Dimension of Textural Image

2010 ◽  
Vol 44-47 ◽  
pp. 3464-3468
Author(s):  
Wan Qing Song ◽  
Jing Zhang
Keyword(s):  
Pattern Recognition ◽  
Fractal Dimension ◽  
Euclidean Distance ◽  
Objective Evaluation ◽  
Image Texture ◽  
Image Process ◽  
Standard Template ◽  
Complex Wavelet ◽  
Garment Manufacturing ◽  
Seam Pucker

The 2-D dual-tree complex wavelet and fractal dimension of image texture is proposed to objective evaluation of seam pucker for the garment manufacturing. Because the complex 2-D dual-tree DWT also gives rise to wavelets in six distinct directions, extract feature of seam pucker is advantage over 2-D wavelet which only has four distinct directions. In terms of the theory of pattern recognition in an image process, Euclidean distance between seam pucker of sample clothes and standard template classified into five classes (AATCC method) on seam pucker is computed. Thus, automatic and objective class evaluation of seam pucker is realized, a practice example proves the method boosts the degree of accuracy of inspector than other methods.

scholarly journals Artificial Intelligence-Based Segmentation of Residual Tumor in Histopathology of Pancreatic Cancer after Neoadjuvant Treatment

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5089
Author(s):  
Boris V. Janssen ◽  
Rutger Theijse ◽  
Stijn van Roessel ◽  
Rik de Ruiter ◽  
Antonie Berkel ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pancreatic Cancer ◽  
Adjuvant Treatment ◽  
Neoadjuvant Treatment ◽  
Residual Tumor ◽  
Objective Evaluation ◽  
Tumor Burden ◽  
Tumor Segmentation ◽  
Segmentation Accuracy ◽  
Test Sets

Background: Histologic examination of resected pancreatic cancer after neoadjuvant therapy (NAT) is used to assess the effect of NAT and may guide the choice for adjuvant treatment. However, evaluating residual tumor burden in pancreatic cancer is challenging given tumor response heterogeneity and challenging histomorphology. Artificial intelligence techniques may offer a more reproducible approach. Methods: From 64 patients, one H&E-stained slide of resected pancreatic cancer after NAT was digitized. Three separate classes were manually outlined in each slide (i.e., tumor, normal ducts, and remaining epithelium). Corresponding segmentation masks and patches were generated and distributed over training, validation, and test sets. Modified U-nets with varying encoders were trained, and F1 scores were obtained to express segmentation accuracy. Results: The highest mean segmentation accuracy was obtained using modified U-nets with a DenseNet161 encoder. Tumor tissue was segmented with a high mean F1 score of 0.86, while the overall multiclass average F1 score was 0.82. Conclusions: This study shows that artificial intelligence-based assessment of residual tumor burden is feasible given the promising obtained F1 scores for tumor segmentation. This model could be developed into a tool for the objective evaluation of the response to NAT and may potentially guide the choice for adjuvant treatment.

Objective Evaluation of Seam Pucker

1992 ◽  
Vol 4 (5) ◽  
pp. 24-33 ◽  
Author(s):  
Shigeru Inui ◽  
Atsuo Shibuya
Keyword(s):  
Objective Evaluation ◽  
Seam Pucker

scholarly journals Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks with a Purpose-Built Automated Evaluation

2018 ◽  
Author(s):  
Ezekiel Victor ◽  
Zahra M. Aghajan ◽  
Amy R. Sewart ◽  
Ray Christian
Keyword(s):  
Artificial Intelligence ◽  
Mental Health Professionals ◽  
Objective Evaluation ◽  
Ease Of Use ◽  
Diagnostic Tools ◽  
Human Intervention ◽  
Symptoms Of Depression ◽  
Automated Evaluation ◽  
Preventative Intervention ◽  
Accuracy And Precision

Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the technology fueled by ML has the potential to detect, and substantially improve treatment of complex mental disorders such as depression. We developed a framework capable of detecting depression with minimal human intervention: AiME (Artificial Intelligence Mental Evaluation). AiME consists of a short human-computer interactive evaluation and artificial intelligence, namely deep learning, and can predict whether the participant is depressed or not with satisfactory performance. Due to its ease of use, this technology can offer a viable tool for mental health professionals to identify symptoms of depression, thus enabling a faster preventative intervention. Furthermore, it may alleviate the challenge of interpreting highly nuanced physiological and behavioral biomarkers of depression by providing a more objective evaluation.

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