An identification method of cashmere and wool by the two features fusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yaolin Zhu ◽  
Jiayi Huang ◽  
Tong Wu ◽  
Xueqin Ren
Keyword(s):  
Textile Industry ◽  
Texture Feature ◽  
Texture Features ◽  
Identification Accuracy ◽  
Impact Factors ◽  
Content Type ◽  
Identification Method ◽  
Feature Parameters ◽  
The Impact ◽  
Optimal Feature

PurposeThe purpose of this paper is to select the optimal feature parameters to further improve the identification accuracy of cashmere and wool.Design/methodology/approachTo increase the accuracy, the authors put forward a method selecting optimal parameters based on the fusion of morphological feature and texture feature. The first step is to acquire the fiber diameter measured by the central axis algorithm. The second step is to acquire the optimal texture feature parameters. This step is mainly achieved by using the variance of secondary statistics of these two texture features to get four statistics and then finding the impact factors of gray level co-occurrence matrix relying on the relationship between the secondary statistic values and the pixel pitch. Finally, the five-dimensional feature vectors extracted from the sample image are fed into the fisher classifier.FindingsThe improvement of identification accuracy can be achieved by determining the optimal feature parameters and fusing two texture features. The average identification accuracy is 96.713% in this paper, which is very helpful to improve the efficiency of detector in the textile industry.Originality/valueIn this paper, a novel identification method which extracts the optimal feature parameter is proposed.

Social impact of major sports events perceived by host community

2016 ◽  
Vol 17 (1) ◽  
pp. 78-91 ◽  
Author(s):  
Dongfeng Liu
Keyword(s):  
Social Impact ◽  
Host Community ◽  
Impact Factors ◽  
International Exchange ◽  
Infrastructure Development ◽  
Content Type ◽  
Sports Events ◽  
The Social ◽  
Local Residents ◽  
The Impact

Purpose – The purpose of this paper is to examine the social impact of major sports events perceived by host city residents using Shanghai as an example. Design/methodology/approach – Exploratory factor analysis based on 450 valid questionnaires. Findings – Research revealed six impact factors including four positive ones: “image and status,” “international exchange and cooperation,” “economic and tourism development,” and “infrastructure development.” In addition, two negative ones are also identified as “inconvenience of life” and “environment pollution and security concern.” Taken as a whole, the local residents in Shanghai have a relative positive perception of the impact of major sports events. Four out of six impact factors were significantly predictive of the attitude toward future bidding of major sports events. Originality/value – The existing literature mainly examined social impact of specific events through case study, and little is known about the overall perception of major sports events in general. Accordingly, this paper seeks to bridge the gap by taking an event portfolio approach using Shanghai as an example.

Journal influence in corporate research

2016 ◽  
Vol 42 (4) ◽  
pp. 376-389 ◽  
Author(s):  
Kenneth Borokhovich ◽  
Allissa Lee ◽  
Betty Simkins
Keyword(s):  
Social Sciences ◽  
Corporate Finance ◽  
Relative Influence ◽  
Impact Factors ◽  
Content Type ◽  
Journal Citation Reports ◽  
The Social ◽  
Total Influence ◽  
The Impact

Purpose – Studies of research influence commonly look at the overall field of finance. The purpose of this paper is to examine the sub-field of corporate finance at four different points in time to determine its evolution and range of influence, specifically focussing on the relative influence of seven leading journals. Design/methodology/approach – Not all articles appearing in the set of journals are in corporate finance. The authors examine each article published in the journals for four key periods and identify those that are corporate. The impact factors (IFs) published in the Journal Citation Reports (JCR) are for all articles appearing in a journal. The authors are interested only in the corporate articles, so the authors calculate separate corporate IFs based on the citations to the corporate articles using the JCR technique. Findings – The authors find a broad corporate research environment with influence that extends well beyond finance. The authors also find differences in the relative influence of the journals not only in their total influence, but in where the influence occurs outside finance and other business journals and even more broadly in the social sciences. Research limitations/implications – The exclusion of journals outside the seven selected may not uncover other areas where corporate finance articles impact research more broadly. Also, classification of articles is inherently subjective. Practical implications – The authors draw comparisons between journals and corporate finance topic areas; indicating the breadth and depth research in these areas attain. These results should prove beneficial to researchers in determining areas of influence for their work, consequently providing opportunities for additional exchanges of ideas resulting in better and more informed research in the overall social sciences. Further, our approach to analyzing journal influence could prove fruitful for additional research. Originality/value – The findings allow for a greater understanding of the influence of individual journals and their subsequent rankings by a number of different means. The authors propose that the means and measures employed here can lead to a greater understanding of how influential a journal really is. Further, the authors contend that the study provides comparisons of the scope and depth of influence for each journal in a way that could lead to new avenues of research.

scholarly journals Examining partial proportional odds model in analyzing severity of high-speed railway accident

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Wang ◽  
Yinghan Wang ◽  
Yichuan Peng ◽  
Jian John Lu
Keyword(s):  
High Speed ◽  
Proportional Odds Model ◽  
Impact Factors ◽  
Proportional Odds ◽  
High Speed Railway ◽  
Severity Level ◽  
Content Type ◽  
Factors Affecting ◽  
Partial Proportional Odds Model ◽  
The Impact

Purpose The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway. Design/methodology/approach A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption. Findings The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe. Originality/value The research results are very useful for mitigating the consequences of high-speed rail accidents.

The impact of organisational culture, organisational structure and technological infrastructure on process improvement through knowledge sharing

2019 ◽  
Vol 26 (6) ◽  
pp. 1443-1472
Author(s):  
Sergio J. Chión ◽  
Vincent Charles ◽  
José Morales
Keyword(s):  
Knowledge Management ◽  
Knowledge Sharing ◽  
Process Improvement ◽  
Textile Industry ◽  
Organisational Culture ◽  
Organisational Structure ◽  
Technology Infrastructure ◽  
Content Type ◽  
General Managers ◽  
The Impact

Purpose The purpose of this paper is to investigate the mediator role that knowledge sharing plays between organisational culture, organisational structure, and technology infrastructure and process improvement in a knowledge management context in manufacturing enterprises operating in the food, beverage and textile industry. Design/methodology/approach An empirical study is conducted with a sample of 200 food, beverage and textile companies. Data are obtained by means of a survey questionnaire applied to general managers in each of the sample firms. The impact of the factors organisational culture, organisational structure and technology infrastructure on process improvement via knowledge sharing is assessed. Structural equation modelling and maximum likelihood estimation are applied to find the direction and strength of the relationships. Findings The main findings indicate the significant relationships between knowledge sharing and process improvement, between organisational culture and knowledge sharing, and between organisational structure and knowledge sharing. The relationship between technology infrastructure and knowledge sharing is found not to be significant. Research limitations/implications The findings of the present study are limited to the food, beverage and textile industry. Future research could incorporate data from other manufacturing sectors or service companies. Practical implications This study provides practical guidance for general managers who wish to implement process improvement programmes. Originality/value Several authors have noted that there are few research studies concerning the interaction between each phase of knowledge management and total quality management practices. This study is interested in knowledge sharing and its impact on process improvement in a knowledge management context.

Impact of liquidity and financial leverage on firm’s profitability – an empirical analysis of the textile industry of Pakistan

2019 ◽  
Vol 23 (4) ◽  
pp. 291-305 ◽  
Author(s):  
Asif Hussain Samo ◽  
Hadeeqa Murad
Keyword(s):  
Textile Industry ◽  
Negative Impact ◽  
Negative Relationship ◽  
Secondary Data ◽  
Annual Data ◽  
Strong Impact ◽  
Financial Leverage ◽  
Content Type ◽  
The Impact ◽  
Textile Sector

Purpose This study aims to determine the impact of liquidity and financial leverage on the profitability, using a sample of 40 selected publicly quoted companies in the textile sector of the Pakistani economy. Design/methodology/approach Through quantitative approach, pooled panel regression and descriptive statistics models are used by taking annual data of Pakistan’s textile sectors from 2006 to 2016. Secondary data has been gathered from financial statements of the firms. Findings The results revealed that there is a positive relationship between liquidity and profitability and negative relationship between financial leverage and profitability. The results for liquidity measure CR revealed positive strong impact on ROA and the financial leverage measure D_E ratio showed negative but not strong impact on ROA. The other part of result concluded that there is a positive strong impact of C_R on ROE too and D_E has a negative impact on ROE. Research limitations/implications The results are showing the impact among these ratios for the textile sector of Pakistan only. Practical implications This study can help higher management of textile firms firm in decision-making stating clearly about how to perform well to enhance financial health of company, which can encourage investors to invest in companies having sound market standing. Originality/value This study takes the latest empirical data with different analysis technique.

Predicting the voluntary donation to online content creators

2020 ◽  
Vol 120 (10) ◽  
pp. 1941-1957
Author(s):  
Futao Zhao ◽  
Zhong Yao
Keyword(s):  
Prediction Model ◽  
Performance Metrics ◽  
Impact Factors ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Modal Particles ◽  
Interpretive Analysis ◽  
Text Content ◽  
The Impact

PurposeThe purpose of this paper is to identify the impact factors that might influence audiences' voluntary donation to content creators on the online platforms, and to build an effective prediction model by considering both content and creator-related features.Design/methodology/approachThis study collected the real-world data of content consumption from Xueqiu.com and extracted both content and creator characteristics from the data set. The best donation prediction model based on such features was determined by evaluating four prevalent classifiers with various performance metrics. Furthermore, three feature selection methods were applied to validate the robustness of the constructed model, and then the predictability of different feature groups was examined. Finally, we conducted an interpretive analysis to identify relatively important predictors.FindingsThe experimental results show that the random classifier with all extracted features outperformed other built models and achieved excellent performance, indicating the usefulness of these factors in predicting the donations. Moreover, the predictability of content features was demonstrated to be relatively better than that of creator ones. Finally, several particularly important predictors were identified such as the number of modal particles in the article.Originality/valueThis study is among the first to investigate what factors might drive customers' voluntary donation to content contributors on social websites. Different from previous studies focusing on live video streaming, we expand the research vision by examining the donations to user-generated text content, calling for attention to other important topics in the burgeoning industry.

Adaptive Texture Feature Extraction with Application to Ultrasonic Image Analysis

1998 ◽  
Vol 20 (2) ◽  
pp. 132-148 ◽  
Author(s):  
H.J. Huisman ◽  
J.M. Thijssen
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Texture Features ◽  
Small Data ◽  
Data Sets ◽  
Large Set ◽  
Texture Feature Extraction ◽  
Feed Forward Neural Network ◽  
Feature Extraction Method ◽  
Optimal Feature

Computer texture analysis methods use texture features that are traditionally chosen from a large set of fixed features known in literature. These fixed features are often not specifically designed to the problem at hand, and as a result they may have low discriminative power, and/or may be correlated. Increasing the number of selected fixed features is statistically not a good solution in limited data environments such as medical imaging. For that reason, we developed an adaptive texture feature extraction method (ATFE) that extracts a small number of features that are tuned to the problem at hand. By using a feed-forward neural network, we ensure that even nonlinear relations are captured from the data. Using extensive, repeated synthetic ultrasonic images, we compared the performance of ATFE with the optimal feature set. We show that the ATFE method is capable of robust operation on small data sets with a performance close to that of the optimal feature set. Another experiment confirms that our ATFE is capable of capturing nonlinear relations from the dataset. We conclude that our method can improve performance in practical, limited dataset situations where an optimal fixed feature set can be hard to find.

scholarly journals Texture Analysis Based on Pi-Rads 4/5-Scored MRI Images Combined with Machine Learning to Distinguish Benign Lesions from Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Qi Zhou ◽  
Huming Yin ◽  
Xiaojie Ang ◽  
Yu Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Clinical Indicators ◽  
Model Based ◽  
Feature Parameters ◽  
Auc Value ◽  
Malignant Prostate

Abstract Background: To extract the texture features of Apparent Diffusion Coefficient (ADC) images in Mp-MRI and build a machine learning model based on radiomics texture analysis to determine its ability to distinguish benign from prostate cancer (PCa) lesions using PI-RADS 4/5 score.Materials and methods: First, use ImageJ software to obtain texture feature parameters based on ADC images; use R language to standardize texture feature parameters, and use Lasso regression to reduce the dimensionality of multiple feature parameters; then, use the feature parameters after dimensionality reduction to construct image-based groups. Learn R-Logistic, R-SVM, R-AdaBoost to identify the machine learning classification model of prostate benign and malignant nodules. Secondly, the clinical indicators of the patients were statistically analyzed, and the three clinical indicators with the largest AUC values were selected to establish a classification model based on clinical indicators of benign and malignant prostate nodules. Finally, compare the performance of the model based on radiomics texture features and clinical indicators to identify benign and malignant prostate nodules in PI-RADS 4/5.Results: The experimental results show that the AUC of the R-Logistic model test set is 0.838, which is higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-Logistic classification model formula is: Y_radiomics=9.396-7.464*median ADC-0.584 *kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the 3 indicators with the highest discrimination efficiency are PSA, Fib, LDL-C, and the corresponding C-Logistic classification model formula is: Y_clinical =-2.608 +0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set is 0.860, which is smaller than the training set R-Logistic classification model AUC value of 0.936.Conclusion: The machine learning classifier model is established based on the texture features of radiomics. It has a good classification performance in identifying benign and malignant nodules of the prostate in PI-RADS 4/5. This has certain potential and clinical value for patients with prostate cancer to adopt different treatment methods and prognosis.

Are rules of origin protective measures under preferential trade agreements?

2016 ◽  
Vol 20 (1) ◽  
pp. 74-96
Author(s):  
Young-Han Kim ◽  
Eui-Hyun Ha
Keyword(s):  
Textile Industry ◽  
Trade Agreement ◽  
Coordination Mechanism ◽  
Intermediate Goods ◽  
Protective Effects ◽  
Rules Of Origin ◽  
Preferential Trade ◽  
Content Type ◽  
The Usa ◽  
The Impact

Purpose – Rules of origin (ROOs) are often cited as major trade barriers even after tariff barriers are removed with the formation of preferential trade agreement (PTA) as shown in a survey result that a large number South Korean firms in the textile industry give up utilizing tariff-free exports to the USA after the bilateral Free Trade Agreement (FTA) due to ROOs. The purpose of this paper is to examine the impact of ROOs on the equilibrium FTA regime and the welfare effects. Design/methodology/approach – The authors determine the impact of ROOs on the equilibrium FTA regime based on an oligopolistic model where there are asymmetry in production technologies of intermediate goods and the capacity of outsourcing intermediate goods. Findings – The authors demonstrate that ROOs are used as a protective trade policy against the FTA member country with an outsourcing option for technologically dominant intermediate goods. Practical implications – The non-cooperative features of ROOs found in this paper necessitates the introduction of an international coordination mechanism to avoid the prisoners’ dilemma-type implementation of ROOs. Originality/value – This paper provides a theoretical frame to analyze the protective effects of ROOs under PTAs.

Temperature rise characteristic of MR fluid in a multi-disc MR clutch under slip condition

2015 ◽  
Vol 67 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Daoming Wang ◽  
Youfu Hou ◽  
Zuzhi Tian ◽  
Qingrui Meng
Keyword(s):  
Temperature Field ◽  
Temperature Distribution ◽  
Temperature Rise ◽  
Slip Condition ◽  
Impact Factors ◽  
Gap Size ◽  
Mr Fluid ◽  
Content Type ◽  
Heat Transfer Theory ◽  
The Impact

Purpose – This study aims to reveal the temperature rise characteristic of magnetorheological (MR) fluid in a multi-disc MR clutch under slip condition, including the temperature distribution regularity and the impact factors. Design/methodology/approach – Three-dimensional transient heat conduction equation for the MR fluid in the working gap was derived based on the heat transfer theory. Then, numerical simulation was conducted to analyze the temperature field of MR fluid. Furthermore, an experimental study was performed to explore the temperature distribution of the MR fluid in radial and circumferential directions, as well as the effects of disc groove, slip power and gap size on temperature rise characteristic of the MR fluid. Findings – The results show that temperature appears to be largest in the center of the working gap and the temperature difference increases with the slip time. However, the temperature field in a circumferential direction is basically the same, but it presents slightly lower in the groove area. The temperature of the MR fluid increases linearly with the slip time and the rise rate increases with the slip power. Moreover, the temperature rise value decreases with the increase of gap size. Originality/value – In this paper, the temperature gradients, both in radial and circumferential directions, are experimentally measured going beyond the estimation by computer simulations. In addition, the factors that influence the temperature rise characteristic of MR fluid were fully analyzed. The results could provide a reliable basis for the development of cooling technology for high-power MR devices.

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