Symmetry

Research

18 pages, 5299 KiB

Open AccessArticle

A Unified Multiple-Phase Fluids Framework Using Asymmetric Surface Extraction and the Modified Density Model

by Xiaokun Wang, Yanrui Xu, Xiaojuan Ban, Sinuo Liu and Yuting Xu

Symmetry 2019, 11(6), 745; https://doi.org/10.3390/sym11060745 - 2 Jun 2019

Cited by 1 | Viewed by 2571

Multiple-phase fluids’ simulation and 3D visualization comprise an important cooperative visualization subject between fluid dynamics and computer animation. Interactions between different fluids have been widely studied in both physics and computer graphics. To further the study in both areas, cooperative research has been [...] Read more.

Multiple-phase fluids’ simulation and 3D visualization comprise an important cooperative visualization subject between fluid dynamics and computer animation. Interactions between different fluids have been widely studied in both physics and computer graphics. To further the study in both areas, cooperative research has been carried out; hence, a more authentic fluid simulation method is required. The key to a better multiphase fluid simulation result is surface extraction. Previous works usually have problems in extracting surfaces with unnatural fluctuations or detail missing. Gaps between different phases also hinder the reality of simulation. In this paper, we propose a unified surface extraction approach integrated with a modified density model for the particle-based multiphase fluid simulation. We refine the original asymmetric smoothing kernel used in the color field and address a binary tree scheme for surface extraction. Besides, we employ a multiphase fluid framework with modified density to eliminate density deviation between different fluids. With the methods mentioned above, our approach can effectively reconstruct the fluid surface for particle-based multiphase fluid simulation. It can also resolve the issue of overlaps and gaps between different fluids, which has widely existed in former methods for a long time. The experiments carried out in this paper show that our approach is able to have an ideal fluid surface condition and have good interaction effects. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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19 pages, 3943 KiB

Open AccessArticle

Large-Scale Traffic Congestion Prediction Based on the Symmetric Extreme Learning Machine Cluster Fast Learning Method

by Yiming Xing, Xiaojuan Ban, Xu Liu and Qing Shen

Symmetry 2019, 11(6), 730; https://doi.org/10.3390/sym11060730 - 28 May 2019

Cited by 20 | Viewed by 4151

Abstract

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. [...] Read more.

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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18 pages, 4413 KiB

Open AccessArticle

Multimodal Emotion Recognition Using the Symmetric S-ELM-LUPI Paradigm

by Lingzhi Yang, Xiaojuan Ban, Michele Mukeshimana and Zhe Chen

Symmetry 2019, 11(4), 487; https://doi.org/10.3390/sym11040487 - 4 Apr 2019

Cited by 4 | Viewed by 2614

Abstract

Multimodal emotion recognition has become one of the new research fields of human-machine interaction. This paper focuses on feature extraction and data fusion in audio-visual emotion recognition, aiming at improving recognition effect and saving storage space. A semi-serial fusion symmetric method is proposed [...] Read more.

Multimodal emotion recognition has become one of the new research fields of human-machine interaction. This paper focuses on feature extraction and data fusion in audio-visual emotion recognition, aiming at improving recognition effect and saving storage space. A semi-serial fusion symmetric method is proposed to fuse the audio and visual patterns of emotional recognition, and a method of Symmetric S-ELM-LUPI is adopted (Symmetric Sparse Extreme Learning Machine-Learning Using Privileged Information). The method inherits the generalized high speed of the Extreme Learning Machine, and combines this with the acceleration in the recognition process by the Learning Using Privileged Information and the memory saving of the Sparse Extreme Learning Machine. It is a learning method, which improves the traditional learning methods of examples and targets only. It introduces the role of a teacher in providing additional information to enhance the recognition (test) without complicating the learning process. The proposed method is tested on publicly available datasets and yields promising results. This method regards one pattern as the standard information source, while the other pattern as the privileged information source. Each mode can be treated as privileged information for another mode. The results show that this method is appropriate for multi-modal emotion recognition. For hundreds of samples, the execution time is less than one percent seconds. The sparsity of the proposed method has the advantage of storing memory economy. Compared with other machine learning methods, this method is more accurate and stable. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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11 pages, 470 KiB

Open AccessArticle

Symmetric Face Normalization

by Ya Su, Zhe Liu and Xiaojuan Ban

Symmetry 2019, 11(1), 96; https://doi.org/10.3390/sym11010096 - 16 Jan 2019

Cited by 5 | Viewed by 3579

Abstract

Image registration is an important process in image processing which is used to improve the performance of computer vision related tasks. In this paper, a novel self-registration method, namely symmetric face normalization (SFN) algorithm, is proposed. There are three contributions in this paper. [...] Read more.

Image registration is an important process in image processing which is used to improve the performance of computer vision related tasks. In this paper, a novel self-registration method, namely symmetric face normalization (SFN) algorithm, is proposed. There are three contributions in this paper. Firstly, a self-normalization algorithm for face images is proposed, which normalizes a face image to be reflection symmetric horizontally. It has the advantage that no face model needs to be built, which is always severely time-consuming. Moreover, it can be considered as a pre-processing procedure which greatly decreases the parameters needed to be adjusted. Secondly, an iterative algorithm is designed to solve the self-normalization algorithm. Finally, SFN is applied to the between-image alignment problem, which results in the symmetric face alignment (SFA) algorithm. Experiments performed on face databases show that the accuracy of SFN is higher than 0.95 when the translation on the x-axis is lower than 15 pixels, or the rotation angle is lower than 18°. Moreover, the proposed SFA outperforms the state-of-the-art between-image alignment algorithm in efficiency (about four times) without loss of accuracy. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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13 pages, 15575 KiB

Open AccessArticle

Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

by Boyuan Ma, Xiaojuan Ban, Haiyou Huang, Yulian Chen, Wanbo Liu and Yonghong Zhi

Symmetry 2018, 10(4), 107; https://doi.org/10.3390/sym10040107 - 13 Apr 2018

Cited by 76 | Viewed by 8379

Abstract

Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using an Al–La alloy. Our work makes [...] Read more.

Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using an Al–La alloy. Our work makes three key contributions. (1) We train a deep convolutional neural network based on DeepLab to achieve image segmentation and have significant results. (2) We adopt a local processing method based on symmetric overlap-tile strategy which makes it possible to analyze the microscopic images with high resolution. Additionally, it achieves seamless segmentation. (3) We apply symmetric rectification to enhance the accuracy of results with 3D information. Experimental results showed that our method outperforms existing segmentation methods. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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13 pages, 358 KiB

Open AccessArticle

Cooperative Secret Sharing Using QR Codes and Symmetric Keys

by Yang-Wai Chow, Willy Susilo, Joseph Tonien, Elena Vlahu-Gjorgievska and Guomin Yang

Symmetry 2018, 10(4), 95; https://doi.org/10.3390/sym10040095 - 4 Apr 2018

Cited by 24 | Viewed by 6332

Abstract

Secret sharing is an information security technique where a dealer divides a secret into a collection of shares and distributes these to members of a group. The secret will only be revealed when a predefined number of group members cooperate to recover the [...] Read more.

Secret sharing is an information security technique where a dealer divides a secret into a collection of shares and distributes these to members of a group. The secret will only be revealed when a predefined number of group members cooperate to recover the secret. The purpose of this study is to investigate a method of distributing shares by embedding them into cover Quick Response (QR) codes in a secure manner using cryptographic keys. The advantage of this approach is that the shares can be disseminated over public channels, as anyone who scans the QR codes will only obtain public information. Only authorized individuals who are in possession of the required keys will be able to recover the shares. This also means that when group members cooperate to recover a secret, the group can determine the presence of an illegitimate participant if the person does not produce a valid share. This study proposes a protocol for accomplishing this and discusses the underlying security of the protocol. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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16 pages, 6088 KiB

Open AccessArticle

Fluid-Solid Boundary Handling Using Pairwise Interaction Model for Non-Newtonian Fluid

by Xiaokun Wang, Xiaojuan Ban, Runzi He, Di Wu, Xing Liu and Yuting Xu

Symmetry 2018, 10(4), 94; https://doi.org/10.3390/sym10040094 - 3 Apr 2018

Cited by 8 | Viewed by 5526

Abstract

In order to simulate fluid-solid boundary interaction for non-Newtonian Smoothed Particle Hydrodynamics (SPH) fluids, we present a steady and realistic fluid-solid boundary handling method using symmetrical interaction forces. Firstly, we use the improved SPH method to model the non-Newtonian fluid. Secondly, the density [...] Read more.

In order to simulate fluid-solid boundary interaction for non-Newtonian Smoothed Particle Hydrodynamics (SPH) fluids, we present a steady and realistic fluid-solid boundary handling method using symmetrical interaction forces. Firstly, we use the improved SPH method to model the non-Newtonian fluid. Secondly, the density of boundary particle is created into the calculation of fluid-solid interaction forces. Besides, we apply friction conditions to constrain the fluid particles at the boundary. Finally, we apply the predictive-corrective scheme to correct the density deviation and improve boundary computing efficiency. The experiment confirms the feasibility for the interaction between non-Newtonian fluid and solid objects with this method. At the same time, it reflects the viscous characteristics and ensures the physical properties of non-Newtonian fluid. In addition, compared to existing methods, this method is more stable and easier to implement. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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20 pages, 4520 KiB

Open AccessArticle

A Symmetric Particle-Based Simulation Scheme towards Large Scale Diffuse Fluids

by Sinuo Liu, Xiaojuan Ban, Ben Wang and Xiaokun Wang

Symmetry 2018, 10(4), 86; https://doi.org/10.3390/sym10040086 - 29 Mar 2018

Cited by 3 | Viewed by 4919

Abstract

We present a symmetric particle simulation scheme for diffuse fluids based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) model. In our method, the generation of diffuse particles is determined by the entropy of fluid particles, and it is calculated by the velocity difference [...] Read more.

We present a symmetric particle simulation scheme for diffuse fluids based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) model. In our method, the generation of diffuse particles is determined by the entropy of fluid particles, and it is calculated by the velocity difference and kinetic energy. Diffuse particles are generated near the qualified diffuse particle emitters whose diffuse material generation rate is greater than zero. Our method fits the laws of physics better, as it abandons the common practice of adding diffuse materials at the crest empirically. The coupling between diffuse materials and fluid is a post-processing step achieved by the velocity field, which enables the avoiding of the time-consuming process of cross finding neighbors. The influence weights of the fluid particles are assigned based on the degree of coupling. Therefore, it improved the accuracy of the diffuse particle position and made the simulation results more realistic. The approach is appropriate for large scale diffuse fluid, as it can be easily integrated in existing SPH simulation methods and the computational overhead is negligible. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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19 pages, 3004 KiB

Open AccessArticle

Enabling Symmetric Collaboration in Public Spaces through 3D Mobile Interaction

by Mayra Donaji Barrera Machuca, Winyu Chinthammit, Weidong Huang, Rainer Wasinger and Henry Duh

Symmetry 2018, 10(3), 69; https://doi.org/10.3390/sym10030069 - 16 Mar 2018

Cited by 4 | Viewed by 4622

Abstract

Collaboration has been common in workplaces in various engineering settings and in our daily activities. However, how to effectively engage collaborators with collaborative tasks has long been an issue due to various situational and technical constraints. The research in this paper addresses the [...] Read more.

Collaboration has been common in workplaces in various engineering settings and in our daily activities. However, how to effectively engage collaborators with collaborative tasks has long been an issue due to various situational and technical constraints. The research in this paper addresses the issue in a specific scenario, which is how to enable users to interact with public information from their own perspective. We describe a 3D mobile interaction technique that allows users to collaborate with other people by creating a symmetric and collaborative ambience. This in turn can increase their engagement with public displays. In order to better understand the benefits and limitations of this technique, we conducted a usability study with a total of 40 participants. The results indicate that the 3D mobile interaction technique promotes collaboration between users and also improves their engagement with the public displays. Full article

(This article belongs to the Special Issue Symmetry in Cooperative Applications III)

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