Forced Circulation (FC) crystallizer plays a key role in a Zero Liquid Discharge desalination process. The hydrodynamics of an FC crystallizer involves a complex turbulent flow field. Computational ...Fluid Dynamics as a powerful method for simulating flow hydrodynamics suffers from high computational cost. In contrast, the compartmental method has widely been used in practical applications. This method does not have the challenge of high computational cost, but it may give unrealistic results since the hydrodynamics of the flow is not taken into account. In the present work, a hybrid compartmental-CFD model, as a powerful method, is utilized. In this model, a simple CFD simulation is made at a relatively low cost. The CFD results are then translated into several proper parameters to modify the compartmental model. Results show that taking 3D hydrodynamic effects into account in the compartmental model, after 15E4 seconds confrontation between growth and attrition mechanisms, finally narrower distribution with a 37% decrease in Sauter diameter is obtained. Furthermore, boiling zone is intensely susceptible to particle segregation, preventing the presence of particles above 583 μm. In addition, mixing zone with moderate volume fraction and supersaturation plays a significant role in crystallization, contributing 54% of the total mass transfer.
•A hybrid compartmental-CFD model, as a powerful method, was utilized.•The CFD results were translated into proper parameters for compartmental model.•3D hydrodynamic effects were taken into account in the compartmental model•A 37% decrease in Sauter diameter was obtained compared to the conventional model.•Boiling zone is intensely susceptible to particle segregation.
The compound kriging-based importance sampling (IS) strategy is proposed for the efficient estimation of failure probability of systems with multiple failure modes. The proposed method is based on ...the IS probability density function of each failure mode constructed by the kriging model, where the probabilistic classification function is treated as a surrogate model for the actual failure indicator function. The whole algorithm of the proposed method can be divided into two stages. First, the kriging model is constructed to estimate the component augmented failure probabilities and obtain quasi-optimal IS samples. Secondly, the constructed kriging model is further refined based on these quasi-optimal IS samples to estimate the correction factor. Finally, the system failure probability is estimated by the product of the component augmented failure probabilities and the correction factor. The system reliability analysis results of the presented examples illustrate the feasibility of the proposed method.
This study shows that a generalized fuzzy
c
-means (gFCM) clustering algorithm, which covers both standard and exponential fuzzy
c
-means clustering, can be constructed if a given fuzzified function, ...its derivative, and its inverse derivative can be calculated. Furthermore, our results show that the fuzzy classification function for gFCM exhibits a behavior similar to that of both standard and exponential fuzzy
c
-means clustering.
This study presents a generalized Tsallis entropy-based fuzzy
c
-means (GTFCM) clustering algorithm. Furthermore, the results of this study show that the behavior of GTFCM, at an infinity point of ...the fuzzy classification function, is similar to that of some conventional clustering algorithms. This result implies that such behavior is determined by a certain part of the GTFCM objective function.
The fault identification process in transmission systems involves three functions: discrimination, classification and phase selection. The current study classifies the methods that applied for each ...function. Moreover, this study introduces criticism and assessment study that helps the power system protection engineer to choose the best fault identification scheme at responsible indices. Investigated solutions for the drawbacks appeared with the previous methods are suggested. This study also proposes sensitive and automated fault identification scheme to solve the existing challenges such as high-impedance faults (HIFs), non-linear modelling of arcing etc. Several simulation studies are employed using alternative transients program/electromagnetic transient program (ATP/EMTP) package on a sample 500 kV test system to ensure the performances of the proposed scheme compared with the previous methods. Simulation results concluded that: the proposed identification scheme has the ability to discriminate correctly between HIF and low-impedance faults using current signal captured from one end only. Moreover, the proposed scheme alleviates perfectly the problems associated with load variations by adaptive threshold settings and reduces the impacts on the environmental and external phenomena.
Support vector machines (SVMs) are one of the most representative shallow network models and have good generalisation abilities in small data sets. In this Letter, a new classification method based ...on the deep structure and least squares SVM (LSSVM) is proposed. For large-scale data sets, the method builds the structures of a multi-layer SVM. Using edge detection and the K-means algorithm, the sample set is compressed into a smaller sample set, which is used to train the LSSVM model of each layer and the discriminant classification function is obtained. Finally, this method is applied to UCI data sets and compared with several density-dependent quantised LSSVM methods and other methods. The experimental results show that the method has good performance in solving the large-scale data set classification problem.
The lack of universally accepted diagnostic criteria and the high rate of psychiatric comorbidity make it difficult to diagnose Fetal Alcohol Spectrum Disorder (FASD). In an effort to improve the ...diagnosis of FASD, the current study aimed to identify a neurodevelopmental profile that is both sensitive and specific to FASD.
A secondary analysis was conducted on data obtained from the Canadian component of the World Health Organization International Study on the Prevalence of FASD. Data on neurodevelopmental status and behavior were derived from a battery of standardized tests and the Child Behavior Checklist for 21 children with FASD, 28 children with other neurodevelopmental disorders, and 37 typically developing control children, aged 7 to 11 years. Two latent profile analyses were performed to derive discriminative profiles: i) children with FASD compared with typically developing control children, and ii) children with FASD compared with typically developing control children and children with other neurodevelopmental disorders. The classification function of the resulting profiles was evaluated using the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Confidence intervals (CIs) were approximated using 10,000 bootstrapped samples.
The neurodevelopmental profile of FASD tested consisted of impairments in perceptual reasoning, verbal comprehension, visual-motor speed and motor coordination, processing speed (nonverbal information), attention and executive function, visuospatial processing, and language, in combination with rule-breaking behavior and attention problems. When children with FASD were compared with typically developing control children, a 2-class model fit the data best and resulted in a sensitivity of 95.2% (95% CI: 84.2-100.0%), specificity of 89.2% (95% CI: 78.4-97.5%), PPV of 83.3% (95% CI: 66.7-96.2%), and NPV of 97.1% (95% CI: 90.3-100.0%). When children with FASD were compared with typically developing control children and children with other neurodevelopmental disorders, the neurodevelopmental profile correctly identified only 56.9% (95% CI: 45.1-69.2%) of typically developing children and children with other neurodevelopmental disorders as not having FASD, and thus the profile was found not to be specific to children with FASD.
The findings question the uniqueness of children with FASD with respect to their neurodevelopmental impairments and behavioral manifestations.
A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and ...Memetic algorithm as final classifier for detection of abnormal traffic. One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments. We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.
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