K/S value prediction of cotton fabric using PSO-LSSVM

2020 ◽  
Vol 90 (23-24) ◽  
pp. 2581-2591
Author(s):  
Chengbing Yu ◽  
Ziwei Xi ◽  
Yilin Lu ◽  
Kaixin Tao ◽  
Zhong Yi
Keyword(s):  
Cotton Fabric ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Reactive Dye ◽  
Cotton Fabrics ◽  
Support Vector ◽  
Process Conditions ◽  
Value Prediction ◽  
Dyeing Process ◽  
Important Index

Cotton is one of the world’s most common natural clothing materials. It is dyed mainly using the exhaustion, cold pad-batch, and pad-dry-pad-steam dyeing methods. The K/S value, an important index for measuring the depth of color, of cotton fabric dyed with reactive dyes is greatly influenced by various factors of the dyeing process. In this study, three models were developed incorporating least squares support vector machine (LSSVM) to predict the K/S values of dyed cotton fabrics, while particle swarm optimization (PSO) was applied to optimize and tune the parameters of the LSSVM model (PSO-LSSVM). Model inputs include dye concentration and process conditions, which are both easily obtainable variables. The K/S values from the PSO-LSSVM model are consistent with actual measured K/S values of dyed cotton fabrics. Moreover, a comparison among PSO-LSSVM, LSSVM and back propagation neural network results shows the superiority of the PSO-LSSVM approach. Results of this work indicate that a PSO-LSSVM model is a powerful tool for predicting the K/S value in cotton fabric dyed with reactive dye and thus a means to improve production processes and reduce costs.

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

scholarly journals Combination of wet fixation and drying treatments to improve dye fixation onto spray-dyed cotton fabric

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lina Lin ◽  
Wenju Zhu ◽  
Cong Zhang ◽  
Md. Yousuf Hossain ◽  
Zubair Bin Sayed Oli ◽  
...  
Keyword(s):  
Cotton Fabric ◽  
Reactive Dye ◽  
Fixation Time ◽  
Drying Process ◽  
Combined Treatments ◽  
Fixation Rate ◽  
Dyeing Process ◽  
Textile Dyeing ◽  
Color Uniformity ◽  
Four Levels

AbstractThe conventional dyeing process requires a substantial amount of auxiliaries and water, which leaches hazardous colored effluents to the environment. Herein, a newly developed sustainable spray dyeing system has been proposed for cotton fabric in the presence of reactive dyes, which has the potential to minimize the textile dyeing industries environmental impact in terms of water consumption and save significant energy. The results suggest that fresh dye solution can be mixed with an alkali solution before spray dyeing to avoid the reactive dye hydrolysis phenomenon. After that, drying at 60–100 °C, wet fixation treating for 1–6 min, and combined treatments (wet fixation + drying) were sequentially investigated and then dye fixation percentages were around 63–65%, 52–70%, and above 80%, respectively. Following this, fixation conditions were optimized using L16 orthogonal designs, including wet fixation time, temperature, dye concentration, and pH with four levels where the “larger-the-better” function was selected to maximize the dye fixation rate. Additionally, the color uniformity and wash and rubbing fastnesses were at an acceptable level when both treatments were applied. Finally, the dyes were hydrolyzed after wet fixation, and the hydrolysis percentages were enhanced after the drying process.

scholarly journals HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 660 ◽  
Author(s):  
Fang Liu ◽  
Liubin Li ◽  
Yongbin Liu ◽  
Zheng Cao ◽  
Hui Yang ◽  
...  
Keyword(s):  
Kernel Method ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Industrial Applications ◽  
Performance Degradation ◽  
Support Vector ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

Study on the Enzyme Desizing Process for Cotton Fabric Using Ultrasonication

2011 ◽  
Vol 331 ◽  
pp. 261-264 ◽  
Author(s):  
Qi Ming Zhao ◽  
Shan Yan Zhang
Keyword(s):  
Cotton Fabric ◽  
Ph Value ◽  
Orthogonal Experiment ◽  
Research Result ◽  
Strength Loss ◽  
Cotton Fabrics ◽  
Optimum Process ◽  
Process Conditions ◽  
Fabric Strength ◽  
Enzyme Dosage

The auxiliary devices of ultrasonic treatment was designed and manufactured. The cotton fabric was desized using 2000L desizing enzyme with the conventional enzyme desizing process and ultrasonic enzyme desizing process respectively. Through the orthogonal experiment, the optimum process conditions of conventional enzyme desizing process and ultrasonic enzyme desizing process were determined. For the conventional enzyme desizing process, the optimized desizing conditions of cotton fabrics were: desizing enzyme dosage was 1.5g/l, temperature was 80°C, PH value was 6, and time was 60mins. The optimum process conditions of ultrasonic enzyme desizing process were: desizing enzyme dosage was 1.5g/l, temperature was 50°C, PH value was 6 and time was 45minutes. The research result indicates that, under the same desizing condition, ultrasonication can improve the desizing percentage and whiteness of cotton fabric, but the fabric strength loss increases slightly. And for the same required desizing percentage, the ultrasonic enzyme desizing process saved time and reduced the temperature of experiments compared with traditional enzyme desizing process

scholarly journals Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

2018 ◽  
Vol 8 (9) ◽  
pp. 1632 ◽  
Author(s):  
Zahra Rezaei ◽  
Ali Selamat ◽  
Arash Taki ◽  
Mohd Mohd Rahim ◽  
Mohammed Abdul Kadir ◽  
...  
Keyword(s):  
Vulnerable Plaque ◽  
Plaque Rupture ◽  
Back Propagation ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Relevant Information ◽  
Back Propagation Neural Network ◽  
Common Mechanism ◽  
Support Vector ◽  
Virtual Histology

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

scholarly journals An Estimation Model on Electricity Consumption of New Metro Stations

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhao Yu ◽  
Yun Bai ◽  
Qian Fu ◽  
Yao Chen ◽  
Baohua Mao
Keyword(s):  
Interior Design ◽  
Back Propagation ◽  
Electricity Consumption ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Estimation Model ◽  
Metro Station ◽  
New Energy ◽  
Passenger Volume ◽  
Major Factors

Electricity consumption of metro stations increases sharply with expansion of a metro network and this has been a growing cause for concern. Based on relevant historical data from existing metro stations, this paper proposes a support vector regression (SVR) model to estimate daily electricity consumption of a newly constructed metro station. The model considers some major factors influencing the electricity consumption of metro station in terms of both the interior design scheme of a station (e.g., layout of the station and allocation of facilities) and external factors (e.g., passenger volume, air temperature and relative humidity). A genetic algorithm with five-fold cross-validation is used to optimize the hyper-parameters of the SVR model in order to improve its accuracy in estimating the electricity consumption of a metro station (ECMS). With the optimized hyper-parameters, results from case studies on the Beijing Subway showed that the estimating accuracy of the proposed SVR model could reach up to 95% and the correlation coefficient was 0.89. It was demonstrated that the proposed model could outperform the traditional methods which use a back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations.

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