A Novel Method for Evaluation of the Flow Field Effects on Mean Drop Size in a Multiphase CFB

2015 ◽  
Vol 4 (2) ◽  
pp. 13-36
Author(s):  
Ali Akbar Jamali ◽  
Shahrokh Shahhosseini ◽  
Yaghoub Behjat
Keyword(s):  
Drop Size ◽  
Orifice Diameter ◽  
Injection Velocity ◽  
Structural Parameter ◽  
Liquid Drops ◽  
Riser Reactor ◽  
Proposed Model ◽  
Liquid Injection ◽  
Novel Method ◽  
Current Flows

Prompt evaporation of injected liquid drops near the injectors locating in the FCC unit riser reactor has considerable impacts on the gas–solid mixing phenomena. To investigate influencing various parameters on the injected liquid species in the riser, conservation equations are primarily needed. A novel model to predict droplet mean diameter (DMD) due to computing penetration depth of the jet flowing through the riser was proposed. The proposed model is able to indirect predict DMD based on direct computation of spray tip penetration (STP). The model has been validated by some empirical correlations. In this study, influencing gas superficial velocity, liquid injection velocity, jet angle and nozzle diameter on DMD were investigated. The results for both concurrent and counter-current flows showed that the decrease of jet angle and injection velocity improves DMD. In addition, increasing orifice diameter (as a structural parameter) arising mean drop size can decline performance of atomizing. It also displayed close agreement between the model predictions and experimental data. In this work, the measurement error associated with STP was determined up to 2.7 mm, and the mean relative error with respect to detecting STP is 4.3%.

scholarly journals Study Rheological Behavior of Polymer Solution in Different-Medium-Injection-Tools

Polymers ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 319 ◽  
Author(s):  
Bin Huang ◽  
Xiaohui Li ◽  
Cheng Fu ◽  
Ying Wang ◽  
Haoran Cheng
Keyword(s):  
Molecular Weight ◽  
Polymer Solution ◽  
Oil Recovery ◽  
Injection Pressure ◽  
Injection Rate ◽  
Injection Velocity ◽  
Structural Parameter ◽  
Local Perturbation ◽  
Polymer Injection ◽  
Single Pipe

Previous studies showed the difficulty during polymer flooding and the low producing degree for the low permeability layer. To solve the problem, Daqing, the first oil company, puts forward the polymer-separate-layer-injection-technology which separates mass and pressure in a single pipe. This technology mainly increases the control range of injection pressure of fluid by using the annular de-pressure tool, and reasonably distributes the molecular weight of the polymer injected into the thin and poor layers through the shearing of the different-medium-injection-tools. This occurs, in order to take advantage of the shearing thinning property of polymer solution and avoid the energy loss caused by the turbulent flow of polymer solution due to excessive injection rate in different injection tools. Combining rheological property of polymer and local perturbation theory, a rheological model of polymer solution in different-medium-injection-tools is derived and the maximum injection velocity is determined. The ranges of polymer viscosity in different injection tools are mainly determined by the structures of the different injection tools. However, the value of polymer viscosity is mainly determined by the concentration of polymer solution. So, the relation between the molecular weight of polymer and the permeability of layers should be firstly determined, and then the structural parameter combination of the different-medium-injection-tool should be optimized. The results of the study are important for regulating polymer injection parameters in the oilfield which enhances the oil recovery with reduced the cost.

scholarly journals A Novel Method to Detect Inner Emotion States of Human using Artificial Neural Networks

2020 ◽  
Vol 8 (6) ◽  
pp. 5820-5825
Keyword(s):  
Physiological Signals ◽  
Classification Rate ◽  
Discrete Emotion ◽  
Novel Approach ◽  
Proposed Model ◽  
Depression Detection ◽  
Novel Method ◽  
Artificial Neural ◽  
Ann Classifier ◽  
Performance Rate

Human computer interaction is a fast growing area of research where in the physiological signals are used to identify human emotion states. Identifying emotion states can be done using various approaches. One such approach which gained interest of research is through physiological signals using EEG. In the present work, a novel approach is proposed to elicit emotion states using 3-D Video-audio stimuli. Around 66 subjects were involved during data acquisition using 32 channel Enobio device. FIR filter is used to preprocess the acquired raw EEG signals. The desired frequency bands like alpha, delta, beta and theta are extracted using 8-level DWT. The statistical features, Hurst exponential, entropy, power, energy, differential entropy of each bands are computed. Artificial Neural network is implemented using Sequential Keras model and applied on the extracted features to classify in to four classes (HVLA, HVHA, LVHA and LVLA) and eight discrete emotion states like clam, relax, happy, joy, sad, fear, tensed and bored. The performance of ANN classifier found to perform better for 4- classes than 8-classes with a classification rate of 90.835% and 74.0446% respectively. The proposed model achieved better performance rate in detecting discrete emotion states. This model can be used to build applications on health like stress / depression detection and on entertainment to build emotional DJ.

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

scholarly journals A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Zhijian Wang ◽  
Likang Zheng ◽  
Wenhua Du ◽  
Wenan Cai ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Weight Coefficient ◽  
Convergence Condition ◽  
Intelligent Fault Diagnosis ◽  
Wind Turbine Gearbox ◽  
Time Frequency ◽  
Proposed Model ◽  
Novel Method ◽  
Relationship Of

In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

Microblog Sentiment Analysis Using User Similarity and Interaction-Based Social Relations

2020 ◽  
Vol 17 (3) ◽  
pp. 39-55
Author(s):  
Chuanmin Mi ◽  
Xiaoyan Ruan ◽  
Lin Xiao
Keyword(s):  
Sentiment Analysis ◽  
Social Relations ◽  
Rapid Development ◽  
Support Vector ◽  
Sina Weibo ◽  
User Similarity ◽  
Proposed Model ◽  
Novel Method ◽  
Polarity Classification ◽  
Similarity Information

With the rapid development of information technology, microblog sentiment analysis (MSA) has become a popular research topic extensively examined in the literature. Microblogging messages are usually short, unstructured, contain less information, creating a significant challenge for the application of traditional content-based methods. In this study, the authors propose a novel method, MSA-USSR, in which user similarity information and interaction-based social relations information are combined to build sentiment relationships between microblogging data. They make use of these microblog–microblog sentiment relations to train the sentiment polarity classification classifier. Two Sina-Weibo datasets were utilized to verify the proposed model. The experimental results show that the proposed method has a better sentiment classification accuracy and F1-score than the content-based support vector machine (SVM) method and the state-of-the-art supervised model known as SANT.

Design optimization of high-precision aerostatic equipment based on orifice restriction

2019 ◽  
Vol 233 (10) ◽  
pp. 3459-3474
Author(s):  
Tao Lai ◽  
Xiaoqiang Peng ◽  
Junfeng Liu ◽  
Chaoliang Guan ◽  
Xiaogang Chen ◽  
...  
Keyword(s):  
Finite Difference Method ◽  
High Precision ◽  
Structural Parameter ◽  
Error Feedback ◽  
Aerostatic Bearing ◽  
Proposed Model ◽  
Gas Gap ◽  
Processing Difficulty ◽  
Optimum Working ◽  
Good Agreement

The aerostatic lubrication model with orifice restriction is built based on finite difference method. The model is solved by combination of flux-error feedback and optimization of grids parameter. The stiffness of aerostatic bearing can be improved by reducing the diameter of the orifice, but the optimum working gas gap is reduced and the processing difficulty of surface throttle is improved. The experiments of load and stiffness are carried out on the slider (50 × 50 mm) with the diameter of orifice at 50 µm. The experimental results and theoretical calculation are in good agreement; thus, the model is verified. The structural parameter of two, three, and four orifice gas-bearings is optimized, respectively, based on the proposed model, and the optimum positions of the orifices are obtained. According to the results, the aerostatic bearing guideways, made up of optical material (K9), are manufactured by some optical ways, and the lubrication of the small gas gap is guaranteed; meanwhile, the straightness accuracy of the aerostatic bearing guideways is 0.1 µm/200 mm. The analysis result verifies that the calculation method and the aerostatic lubrication model are significant to the design of high-precision aerostatic equipment.

A Novel Method of Visual Attention for Targets Detection

2013 ◽  
Vol 347-350 ◽  
pp. 3764-3768 ◽  
Author(s):  
Zhuo Zhang ◽  
Xin Nan Fan ◽  
Xue Wu Zhang ◽  
Hai Yan Xu ◽  
Min Li
Keyword(s):  
Visual Attention ◽  
Information Extraction ◽  
Feature Space ◽  
Gain Factor ◽  
Top Down ◽  
Bottom Up ◽  
Attention Model ◽  
Proposed Model ◽  
Novel Method ◽  
Statistical Prior

Inspired by the research of human visual system in neuroanatomy and psychology, the paper proposes a two-way collaborative visual attention model for target detection.In this new method , bottom-up attention information cooperates with top-down attention information to detect a target rapidly and accuractly. Firstly,the statistical prior knowledge of target and background is applied to optimize bottom-up attention information in different feature space and scale space.Secondly, after the SNR of salience difference between target and interference is computed ,the bottom-up gain factor is obtained.Thirdly, the gain factor is applied to adjust bottom up attention information extraction and then to maximize the salience contrast of target and background.Finally, target is detected by adjusted saliency.Experimental results shows that the proposed model in this paper can improve the real-time capability and reliability of target detection.

Structured Pyramidal Neural Networks

2018 ◽  
Vol 28 (05) ◽  
pp. 1750021 ◽  
Author(s):  
Alessandra M. Soares ◽  
Bruno J. T. Fernandes ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Processing Time ◽  
Receptive Fields ◽  
Memory Capacity ◽  
The Other ◽  
Support Vector ◽  
Proposed Model ◽  
Novel Method ◽  
Original Approach

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

A Joint Model to Identify and Align Bilingual Named Entities

2013 ◽  
Vol 39 (2) ◽  
pp. 229-266 ◽  
Author(s):  
Yufeng Chen ◽  
Chengqing Zong ◽  
Keh-Yih Su
Keyword(s):  
Supervised Learning ◽  
Integrated Model ◽  
Named Entities ◽  
Relative Improvement ◽  
Proposed Model ◽  
Alignment Task ◽  
Novel Method ◽  
Consistency Constraint ◽  
Chinese And English

In this article, an integrated model is derived that jointly identifies and aligns bilingual named entities (NEs) between Chinese and English. The model is motivated by the following observations: (1) whether an NE is translated semantically or phonetically depends greatly on its entity type, (2) entities within an aligned pair should share the same type, and (3) the initially detected NEs can act as anchors and provide further information while selecting NE candidates. Based on these observations, this article proposes a translation mode ratio feature (defined as the proportion of NE internal tokens that are semantically translated), enforces an entity type consistency constraint, and utilizes additional new NE likelihoods (based on the initially detected NE anchors). Experiments show that this novel method significantly outperforms the baseline. The type-insensitive F-score of identified NE pairs increases from 78.4% to 88.0% (12.2% relative improvement) in our Chinese–English NE alignment task, and the type-sensitive F-score increases from 68.4% to 83.0% (21.3% relative improvement). Furthermore, the proposed model demonstrates its robustness when it is tested across different domains. Finally, when semi-supervised learning is conducted to train the adopted English NE recognition model, the proposed model also significantly boosts the English NE recognition type-sensitive F-score.

Accurate and Interpretable Factorization Machines

2019 ◽  
Vol 33 ◽  
pp. 4139-4146 ◽  
Author(s):  
Liang Lan ◽  
Yu Geng
Keyword(s):  
Scoring System ◽  
Time Complexity ◽  
Feature Representation ◽  
Feature Mapping ◽  
Feature Interactions ◽  
Proposed Model ◽  
Novel Method ◽  
Ranking Problems ◽  
New Feature ◽  
Low Dimensional

Factorization Machines (FMs), a general predictor that can efficiently model high-order feature interactions, have been widely used for regression, classification and ranking problems. However, despite many successful applications of FMs, there are two main limitations of FMs: (1) FMs consider feature interactions among input features by using only polynomial expansion which fail to capture complex nonlinear patterns in data. (2) Existing FMs do not provide interpretable prediction to users. In this paper, we present a novel method named Subspace Encoding Factorization Machines (SEFM) to overcome these two limitations by using non-parametric subspace feature mapping. Due to the high sparsity of new feature representation, our proposed method achieves the same time complexity as the standard FMs but can capture more complex nonlinear patterns. Moreover, since the prediction score of our proposed model for a sample is a sum of contribution scores of the bins and grid cells that this sample lies in low-dimensional subspaces, it works similar like a scoring system which only involves data binning and score addition. Therefore, our proposed method naturally provides interpretable prediction. Our experimental results demonstrate that our proposed method efficiently provides accurate and interpretable prediction.

Sign in / Sign up

Export Citation Format

Share Document