Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence...Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence matrix,twenty-two texture features were extracted from the images of coal and rock.Data dimension of the feature space reduced to four by feature selection,which was according to a separability criterion based on inter-class mean difference and within-class scatter.The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm.In the optimized low-dimensional feature space,the coal–rock classifer was set up using the fsher discriminant method.Using the 10-fold cross-validation technique,the performance of the classifer was evaluated,and an average recognition rate of 94.12%was obtained.The results of comparative experiments show that the identifcation performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.展开更多
With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out ...With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals（SDNN） significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future.展开更多
The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability ...The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.展开更多
Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In ord...Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.展开更多
AIM: To investigate the correlation between autoimmune thyroid diseases (ATDs) and the prevalence of Cag-A positive strains of Helicobacter pylori (H. pylori) in stool samples. METHODS: We investigated 112 consecutive...AIM: To investigate the correlation between autoimmune thyroid diseases (ATDs) and the prevalence of Cag-A positive strains of Helicobacter pylori (H. pylori) in stool samples. METHODS: We investigated 112 consecutive Caucasian patients (48 females and 4 males with Graves' disease and 54 females and 6 males with Hashimoto' s thyroiditis HT), at their first diagnosis of ATDs. We tested for H. pylori in stool samples using an amplified enzyme immunoassay and Cag-A in serum samples using an enzyme-linked immunoassay method (ELISA). The results were analyzed using the two-sided Fisher' s exact test and the respective odds ratio (OR) was calculated. RESULTS: A marked correlation was found between the presence of H. pylori (P ≤ 0.0001, OR 6.3) and, in particular, Cag-A positive strains (P ≤ 0.005, OR 5.3)in Graves' disease, but not in Hashimoto's thyroiditis, where we found only a correlation with Cag-A strains (P ≤ 0.005, OR 8.73) but not when H. pylori was present. CONCLUSION: The marked correlation between H. pylori and Cag-A, found in ATDs, could be dependent on the different expression of adhesion molecules in the gastric mucosa.展开更多
基金the National Natural Science Foundation of China(No.51134024/E0422)for the financial support
文摘Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence matrix,twenty-two texture features were extracted from the images of coal and rock.Data dimension of the feature space reduced to four by feature selection,which was according to a separability criterion based on inter-class mean difference and within-class scatter.The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm.In the optimized low-dimensional feature space,the coal–rock classifer was set up using the fsher discriminant method.Using the 10-fold cross-validation technique,the performance of the classifer was evaluated,and an average recognition rate of 94.12%was obtained.The results of comparative experiments show that the identifcation performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.
基金supported by the National Basic Research Program of China (No. 2010CB734104)
文摘With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals（SDNN） significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future.
基金Project (50934006) supported by the National Natural Science Foundation of ChinaProject (2010CB732004) supported by the National Basic Research Program of ChinaProject (CX2011B119) supported by the Graduated Students’ Research and Innovation Fund Project of Hunan Province of China
文摘The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.
基金Supported by the National Basic Research Program of China （2013CB733600）, the National Natural Science Foundation of China （21176073）, the Doctoral Fund of Ministry of Education of China （20090074110005）, the Program for New Century Excellent Talents in University （NCET-09-0346）, Shu Guang Project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.
文摘AIM: To investigate the correlation between autoimmune thyroid diseases (ATDs) and the prevalence of Cag-A positive strains of Helicobacter pylori (H. pylori) in stool samples. METHODS: We investigated 112 consecutive Caucasian patients (48 females and 4 males with Graves' disease and 54 females and 6 males with Hashimoto' s thyroiditis HT), at their first diagnosis of ATDs. We tested for H. pylori in stool samples using an amplified enzyme immunoassay and Cag-A in serum samples using an enzyme-linked immunoassay method (ELISA). The results were analyzed using the two-sided Fisher' s exact test and the respective odds ratio (OR) was calculated. RESULTS: A marked correlation was found between the presence of H. pylori (P ≤ 0.0001, OR 6.3) and, in particular, Cag-A positive strains (P ≤ 0.005, OR 5.3)in Graves' disease, but not in Hashimoto's thyroiditis, where we found only a correlation with Cag-A strains (P ≤ 0.005, OR 8.73) but not when H. pylori was present. CONCLUSION: The marked correlation between H. pylori and Cag-A, found in ATDs, could be dependent on the different expression of adhesion molecules in the gastric mucosa.
