Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userStudy on fibers opening using image analysis technique
دراسة تفتيح الشعيرات باستخدام تقنية تحليل الصورة
1452
0 10
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تعتمد الطرق الحديثة لقياس درجة التفتيح على تقنية تحميل الصورة و مقارنة النتائج مع قياسات درجة التفتيح بالطريقة التقليدية. كما تم البحث في تأثير العوامل المتغيرة لعملية التفتيح على درجة التفتيح, و تأثير درجة تفتيح الألياف على جودة أشرطة السحب و الخيوط الناتجة. و وجد أن السرعة العالية لاسطوانات التفتيح و الزوايا الكبيرة لقضبان التصفية تحسن من عملية تفتيح الشعيرات, لكنها تؤدي إلى انخفاض قليل في جودة الشاشة المسرحة. و مع تزايد درجات تفتيح الشعيرات ستتزايد قوة الشد و استطالة القطع للخيوط الناتجة بشكل أولي ثم تتناقص, في حين يبقى التشعر ثابتاً دون أي تغيير. و بالتالي فإن الشعيرات ذات درجات التفتيح العالية تُنتج خيوط أكثر انتظام و بأقل عدد من العيوب المتشكلة.
A newly developed methodology based on image analysis technique has been used to measure fiber openness and the results are compared with those of the traditional method of measurement of fiber openness. The role of different opening process parameters in deciding the openness of fibers obtained by image analysis technique is found to be much higher than that obtained by beaker test method. It is found that the higher speed of opening rollers and higher angle of grid bars improve fiber openness appreciably, but with little deterioration in fiber quality. The higher fiber openness ultimately results into more regular and less imperfect yarn. With the increase in fiber openness, yarn strength and yarn breaking elongation increase initially and then decrease. Yarn hairiness almost unchanged with the increase in fiber openness.
References used
Bad bade P R, Design and Development of Fiber Openness Tester Based on Image Processing Technology, New Delhi, 2007
Bostock W, Freeman S M, Shorter S A & William T C, J Text Inst, 46(1998)23-34
Ishtiaque S M, Das A & Chouhary S, Indian J Fiber Text Res, 28(2003) 399-401
Ishtiaque S M,R M, Das A & Chouhary S, Indian J Fiber Text Res, 28(2003) 404-410
Kato M & Yoshida K, J , 41(2001)905-910
rate research
Read More
تأتي أهمية البحث حيث إنه يدرس موضوع الرضا الوظيفي في أحد أهم القطاعات المساهمة بالتنمية في دولة
نامية و هو القطاع المصرفي السوري الخاص.
ستقوم الباحثة بالتطرق لموضوع الرضا الوظيفي لدى العاملين في البنك العربي سورية -
باعتباره متغير تابع.
A simple and low cost thin layer chromatographic (TLC)-image analysis method was developed for rapid determination and quantification of monosodium glutamate (MSG) in some food samples. Chromatographic separation of MSG was achieved on silica gel TLC
plates, using n-butanol:glacial acetic acid:water (5:3:1, v/v/v) as the mobile phase and ninhydrin for spot detection. Image analysis of the scanned TLC plate was performed to quantify the amount of MSG, the method was validated and found to be accurate specific reliable and convenient for the analysis of MSG in some food sample.
This paper presents an algorithm for designing a system that classifies standard
human facial expressions which are fear, disgust, sad , surprise, anger, happiness, and the
normal expression . The facial expression that is presented in the input im
age of the system
can be classified depending on extracting appearance features then, it is entered into
neural network to complete the classification process using Matlab as a programming
language.
Multiple stages completed the work, which are, (collection images, pre-processing of
the images, feature extraction, training neural network, classification and testing). Our
system has been able to achieve the highest rating when the expression of anger reached
100 %, while the lowest rating was at the expression of sad by 30%.
This paper presents an algorithm for designing a system that classifies standard human facial expressions which are fear , disgust , sad , surprise , Anger , happiness , natural expression . The facial expression that is presented in the input image
of the system can be classified depending on extracting appearance features , then they entered into neural network to complete the classification process using Matlab as a programming language.
The basic step in this algori thm is determining the number of clusters
(K) then calculating the distance between each cluster
center and the elements of images to join each element
to the closest cluster depending on threshold distance.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
