Endpoint Prediction of EAF Based on Multiple Support Vector Machines

The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

Extracorporeal organ support for critically ill patients:Overcoming the past,achieving the maximum at present,and redefining the future

Extracorporeal organ support(ECOS)has made remarkable progress over the last few years.Renal replacement therapy,introduced a few decades ago,was the first available application of ECOS.The subsequent evolution of ECOS enabled the enhanced support to many other organs,including the heart[veno-arterial extracorporeal membrane oxygenation(ECMO),slow continuous ultrafiltration],the lungs(veno-venous ECMO,extracorporeal carbon dioxide removal),and the liver(blood purification techniques for the detoxification of liver toxins).Moreover,additional indications of these methods,including the suppression of excessive inflammatory response occurring in severe disorders such as sepsis,coronavirus disease 2019,pancreatitis,and trauma(blood purification techniques for the removal of exotoxins,endotoxins,or cytokines),have arisen.Multiple organ support therapy is crucial since a vast majority of critically ill patients present not with a single but with multiple organ failure(MOF),whereas,traditional therapeutic approaches(mechanical ventilation for acute respiratory failure,antibiotics for sepsis,and inotropes for cardiac dysfunction)have reached the maximum efficacy and cannot be improved further.However,several issues remain to be clarified,such as the complexity and cost of ECOS systems,standardization of indications,therapeutic protocols and initiation time,choice of the patients who will benefit most from these interventions,while evidence from randomized controlled trials supporting their use is still limited.Nevertheless,these methods are currently a part of routine clinical practice in intensive care units.This editorial presents the past,present,and future considerations,as well as perspectives regarding these therapies.Our better understanding of these methods,the pathophysiology of MOF,the crosstalk between native organs resulting in MOF,and the crosstalk between native organs and artificial organ support systems when applied sequentially or simultaneously,will lead to the multiplication of their effects and the minimization of complications arising from their use.

Strength failure of spatial reticulated structures under multi-support excitation

Under strong earthquakes, long-span spatial latticed structures may collapse due to dynamic instability or strength failure. The elasto-plastic dynamic behaviors of three spatial latticed structures, including two double-layer cylindrical shells and one spherical shell constructed for the 2008 Olympic Games in Beijing, were quantitatively examined under multi-support excitation （MSE） and uniform support excitation （USE）. In the numerical analyses, several important parameters were investigated such as the peak acceleration and displacement responses at key joints, the number and distribution of plastic members, and the deformation of the shell at the moment of collapse. Analysis results reveal the features and the failure mechanism of the spatial latticed structures under MSE and USE. In both scenarios, the double-layer reticulated shell collapses in the ＂overflow＂ mode, and the collapse is governed by the number of invalid plastic members rather than the total number of plastic members, beginning with damage to some of the local regions near the supports. By comparing the numbers and distributions of the plastic members under MSE to those under USE, it was observed that the plastic members spread more sufficiently and the internal forces are more uniform under MSE, especially in cases of lower apparent velocities in soils. Due to the effects of pseudo-static displacement, the stresses in the members near the supports under MSE are higher than those under USE.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Multi-Dimension Support Vector Machine Based Crowd Detection and Localisation Framework for Varying Video Sequences

In this paper, we propose a novel method for anomalous crowd behaviour detection and localization with divergent centers in intelligent video sequence through multiple SVM (support vector machines) based appearance model. In multi-dimension SVM crowd detection, many features are available to track the object robustly with three main features which include 1) identification of an object by gray scale value, 2) histogram of oriented gradients (HOG) and 3) local binary pattern (LBP). We propose two more powerful features namely gray level co-occurrence matrix (GLCM) and Gaber feature for more accurate and authenticate tracking result. To combine and process the corresponding SVMs obtained from each features, a new collaborative strategy is developed on the basis of the confidence distribution of the video samples which are weighted by entropy method. We have adopted subspace evolution strategy for reconstructing the image of the object by constructing an update model. Also, we determine reconstruction error from the samples and again automatically build an update model for the target which is tracked in the video sequences. Considering the movement of the targeted object, occlusion problem is considered and overcome by constructing a collaborative model from that of appearance model and update model. Also if update model is of discriminative model type, binary classification problem is taken into account and overcome by collaborative model. We run the multi-view SVM tracking method in real time with subspace evolution strategy to track and detect the moving objects in the crowded scene accurately. As shown in the result part, our method also overcomes the occlusion problem that occurs frequently while objects under rotation and illumination change due to different environmental conditions.

Strength behavior and collapse of spatial-reticulated structures under multi-support excitation

Under strong shocks,long-span spatial-latticed structures may collapse due to dynamic instability or strength failure.The elasto-plastic dynamic behaviors of three spatiallatticed structures,including two double-layer cylindrical shells and a spheri-cal shell used for the 2008 Olympic Games in Beijing,were quantitatively examined under multi-support excitation(MSE) and uniform support excitation(USE).Numerical analyses described several important parameters such as the peak acceleration and displacement responses at key joints,the number and distribution of plastic elements,and the deformation of the shell at the moment of collapse.Results of the analysis revealed the features and the failure mechanism of the spatial-latticed structures under MSE and USE.In both scenarios,the double-layer reticulated shell collapsed in the "overflow" mode,collapse was govrned by the number of invalid plastic elements rather than the total number of plastic elements,and the collapse of the structure began with damage to certain local regions near the supports.By comparing the numbers and distributions of the plastic members under MSE to those under USE,it was observed that the plastic members spread more sufficiently and the internal forces were more uniform under MSE,especially for lower apparent velocities in soils.Due to the effects of pseudo-static displacement,the stresses in members near supports under MSE were higher than those under USE.These regions are prone to failure during earthquakes and deserve special attention in the seismic design of reticulated structures.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） strategy, is used as the descriptor selection and model development method. Then, the support vector machine （SVM） and multiple linear regression （MLR） model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.