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"affiliation": "Beijing Institute of Technology, China",
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"abstract": "Objective: To study how to use virtual reality technology (VR) to improve the cognitive function of depressive patients. This study explored the process of the diagnosis, training and evaluation of cognitive impairment in patients with depression, and provided new training methods and research ideas for improving cognitive impairment and prognosis of patients with depression. Methods: 30 mild to moderate depression subjects were randomly assigned to the experimental group, and 32 mild to moderate depression subjects were randomly assigned to the control group. The subjects in the experimental group were trained and treated with immersion virtual reality system for six months, while the subjects in the control group did not receive any treatment. MoCA scale and corresponding diagnostic criteria were used to assess the cognitive baseline level before treatment and the cognitive improvement after treatment. Result: The subjects in this study had mild to moderate depression, and the depression was similar in different genders, professions and groups. Before the cognitive training, there was no difference in the scores of MoCA scale between the experimental group and the control group, t = 0.2, P = 0.84. After the cognitive training, there was a significant difference in the scores of MoCA scale between the experimental group and the control group, t = 4.36, P = 0.00. This shows that the cognitive function training and treatment based on VR technology can effectively improve the cognitive level of depressive patients. Conclusion: Using virtual reality technology to train the cognitive ability of mild to moderate depression subjects can significantly improve the cognitive ability of the subjects, which is worthy of clinical application and promotion.",
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"content": "Objective: To study how to use virtual reality technology (VR) to improve the cognitive function of depressive patients. This study explored the process of the diagnosis, training and evaluation of cognitive impairment in patients with depression, and provided new training methods and research ideas for improving cognitive impairment and prognosis of patients with depression. Methods: 30 mild to moderate depression subjects were randomly assigned to the experimental group, and 32 mild to moderate depression subjects were randomly assigned to the control group. The subjects in the experimental group were trained and treated with immersion virtual reality system for six months, while the subjects in the control group did not receive any treatment. MoCA scale and corresponding diagnostic criteria were used to assess the cognitive baseline level before treatment and the cognitive improvement after treatment. Result: The subjects in this study had mild to moderate depression, and the depression was similar in different genders, professions and groups. Before the cognitive training, there was no difference in the scores of MoCA scale between the experimental group and the control group, t = 0.2, P = 0.84. After the cognitive training, there was a significant difference in the scores of MoCA scale between the experimental group and the control group, t = 4.36, P = 0.00. This shows that the cognitive function training and treatment based on VR technology can effectively improve the cognitive level of depressive patients. Conclusion: Using virtual reality technology to train the cognitive ability of mild to moderate depression subjects can significantly improve the cognitive ability of the subjects, which is worthy of clinical application and promotion.",
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"normalizedAbstract": "Objective: To study how to use virtual reality technology (VR) to improve the cognitive function of depressive patients. This study explored the process of the diagnosis, training and evaluation of cognitive impairment in patients with depression, and provided new training methods and research ideas for improving cognitive impairment and prognosis of patients with depression. Methods: 30 mild to moderate depression subjects were randomly assigned to the experimental group, and 32 mild to moderate depression subjects were randomly assigned to the control group. The subjects in the experimental group were trained and treated with immersion virtual reality system for six months, while the subjects in the control group did not receive any treatment. MoCA scale and corresponding diagnostic criteria were used to assess the cognitive baseline level before treatment and the cognitive improvement after treatment. Result: The subjects in this study had mild to moderate depression, and the depression was similar in different genders, professions and groups. Before the cognitive training, there was no difference in the scores of MoCA scale between the experimental group and the control group, t = 0.2, P = 0.84. After the cognitive training, there was a significant difference in the scores of MoCA scale between the experimental group and the control group, t = 4.36, P = 0.00. This shows that the cognitive function training and treatment based on VR technology can effectively improve the cognitive level of depressive patients. Conclusion: Using virtual reality technology to train the cognitive ability of mild to moderate depression subjects can significantly improve the cognitive ability of the subjects, which is worthy of clinical application and promotion.",
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"abstract": "CAVE immersive system is a large VR system with high immersion and good interaction means. CAVE can integrate vision, touch, sound, etc., and is widely used in architecture display, equipment structure research, simulation training, scientific research and other fields with high requirements for space sense and visual effect. According to the structure and number of projection screens, the virtual environment in CAVE immersion system need to display multi screen images synchronously. But the existing multi screen display methods are too complex, and even require paid plugins. In order to solved this problem, a method of making CAVE immersive virtual environment based on unity 3D was proposed. This method was very simple and could create virtual reality applications suitable for various CAVE systems quickly.",
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"title": "2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
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"content": "During their geometry processing and transmission 3D meshes are subject to various visual processing operations like compression, watermarking, remeshing, noise addition and so forth. In this context it is indispensable to evaluate the quality of the distorted mesh, we talk here about the mesh visual quality (MVQ) assessment. Several works have tried to evaluate the MVQ using simple geometric measures, However this metrics do not correlate well with the subjective score since they fail to reflect the perceived quality. In this paper we propose a new objective metric to evaluate the visual quality between a mesh with a perfect quality called reference mesh and its distorted version. The proposed metric uses a chosen statistical distribution to extract parameters of two random variable sets, the first set is the dihedral angles related to the reference mesh, while the second set is the dihedral angles related to the distorted mesh. The perceptual distance between two meshes is computed as the Kullback-Leibler divergence between the two sets of variables. Experimental results from two subjective databases (LIRIS masking database and LIRIS/EPFL general purpose database) and comparisons with seven objective metrics cited in the state-of-the-art demonstrate the efficacy of the proposed metric in terms of the correlation to the mean opinion scores across these databases.",
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"abstract": "The goal of the objective image quality assessment is to quantitatively measure the image quality of a distorted image, which is close to the subjective image quality assessment such as the mean opinion score. The image quality assessment algorithms are generally classified into two methodologies: perceptual and structural. This paper proposes a structural information-based image quality assessment algorithm, in which LU factorization is used for representation of the structural information of an image. The proposed algorithm performs LU factorization of reference and distorted images, from which the distortion map is computed for measuring the quality of the distorted image. Finally, the proposed image quality metric is computed from the two-dimensional distortion map. Experimental results with the LIVE database images show the efficiency of the proposed method. In commercial systems, the proposed algorithm can be used for quality assessment of mobile contents and video coding, which effectively replaces the peak signal to noise ratio or the mean square error.",
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"content": "The goal of the objective image quality assessment is to quantitatively measure the image quality of a distorted image, which is close to the subjective image quality assessment such as the mean opinion score. The image quality assessment algorithms are generally classified into two methodologies: perceptual and structural. This paper proposes a structural information-based image quality assessment algorithm, in which LU factorization is used for representation of the structural information of an image. The proposed algorithm performs LU factorization of reference and distorted images, from which the distortion map is computed for measuring the quality of the distorted image. Finally, the proposed image quality metric is computed from the two-dimensional distortion map. Experimental results with the LIVE database images show the efficiency of the proposed method. In commercial systems, the proposed algorithm can be used for quality assessment of mobile contents and video coding, which effectively replaces the peak signal to noise ratio or the mean square error.",
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"affiliation": "Department of Electronic Engineering, Sogang Univ., C.P.O. Box 1142, Seoul 100-611, Korea",
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"abstract": "Image quality assessment (IQA) intended to assess the perceptual quality of images has been an essential problem in both human and machine vision. Recently, with the help of deep neural network (DNN), IQA algorithms can extract more valuable differences between the distorted and reference images than the traditional algorithms, and thus the performance of DNN-based algorithms is more satisfactory than that of previous algorithms. However, the accuracy for different distorted images preference rating of the existing DNN-based quality assessment methods will be decreased when multiple distorted images are quite similar to each other or to the reference image. To tackle this problem, we propose a focused feature differentiation network (FFDN) to highlight the feature maps with greater distorted and reference differentiation. Furthermore, we use the multi-scale feature fusion module to fuse the focused differentiation features at different scale receptive fields. To further improve the accuracy of our method, we predict the mean opinion score and differentiation score by stages and combine them with different self-learning weights. Finally, we convert the weighted score into different image preference degrees. Experimental results on the validation dataset of CLIC2022 and test dataset of CLIC2021 show that the accuracy of our model FFDN is higher than other excellent quality assessment methods.",
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"content": "Image quality assessment (IQA) intended to assess the perceptual quality of images has been an essential problem in both human and machine vision. Recently, with the help of deep neural network (DNN), IQA algorithms can extract more valuable differences between the distorted and reference images than the traditional algorithms, and thus the performance of DNN-based algorithms is more satisfactory than that of previous algorithms. However, the accuracy for different distorted images preference rating of the existing DNN-based quality assessment methods will be decreased when multiple distorted images are quite similar to each other or to the reference image. To tackle this problem, we propose a focused feature differentiation network (FFDN) to highlight the feature maps with greater distorted and reference differentiation. Furthermore, we use the multi-scale feature fusion module to fuse the focused differentiation features at different scale receptive fields. To further improve the accuracy of our method, we predict the mean opinion score and differentiation score by stages and combine them with different self-learning weights. Finally, we convert the weighted score into different image preference degrees. Experimental results on the validation dataset of CLIC2022 and test dataset of CLIC2021 show that the accuracy of our model FFDN is higher than other excellent quality assessment methods.",
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"affiliation": "Xidian University,Xi’an,China",
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"affiliation": "Xidian University,Xi’an,China",
"fullName": "Yong Wang",
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{
"affiliation": "Xidian University,Xi’an,China",
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"affiliation": "Xidian University,Xi’an,China",
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"abstract": "In this work, we introduce Gradient Siamese Network (GSN) for image quality assessment. The proposed method is skilled in capturing the gradient features between distorted images and reference images in full-reference image quality assessment (IQA) task. We utilize Central Differential Convolution to obtain both semantic features and detail difference hidden in image pair. Furthermore, spatial attention guides the network to concentrate on regions related to image detail. For the low-level, mid-level, and high-level features extracted by the network, we innovatively design a multi-level fusion method to improve the efficiency of feature utilization. In addition to the common mean square error supervision, we further consider the relative distance among batch samples and successfully apply KL divergence loss to the image quality assessment task. We experimented the proposed algorithm GSN on several publicly available datasets and proved its superior performance. Our network won the second place in NTIRE 2022 Perceptual Image Quality Assessment Challenge track 1 Full-Reference [1].",
"abstracts": [
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"content": "In this work, we introduce Gradient Siamese Network (GSN) for image quality assessment. The proposed method is skilled in capturing the gradient features between distorted images and reference images in full-reference image quality assessment (IQA) task. We utilize Central Differential Convolution to obtain both semantic features and detail difference hidden in image pair. Furthermore, spatial attention guides the network to concentrate on regions related to image detail. For the low-level, mid-level, and high-level features extracted by the network, we innovatively design a multi-level fusion method to improve the efficiency of feature utilization. In addition to the common mean square error supervision, we further consider the relative distance among batch samples and successfully apply KL divergence loss to the image quality assessment task. We experimented the proposed algorithm GSN on several publicly available datasets and proved its superior performance. Our network won the second place in NTIRE 2022 Perceptual Image Quality Assessment Challenge track 1 Full-Reference [1].",
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"abstract": "Anthropometric body measurements are important for industrial design, garment fitting, medical diagnosis and ergonomics. A number of methods have been proposed to estimate the body measurements from images, but progress has been slow due to the lack of realistic and publicly available datasets. The existing works train and test on silhouettes of 3D body meshes obtained by fitting a human body model to the commercial CAESAR scans. In this work, we introduce the BODY-fit dataset that contains fitted meshes of 2,675 female and 1,474 male 3D body scans. We unify evaluation on the CAESAR-fit and BODY-fit datasets by computing body measurements from geodesic surface paths as the ground truth and by generating two-view silhouette images. We also introduce BODY-rgb - a realistic dataset of 86 male and 108 female subjects captured with an RGB camera and manually tape measured ground truth. We propose a simple yet effective deep CNN architecture as a baseline method which obtains competitive accuracy on the three datasets.",
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"affiliation": "Tampere University,Computing Sciences,Finland",
"fullName": "Song Yan",
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"affiliation": "Tampere University,Computing Sciences,Finland",
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"abstract": "In this paper, we present a novel technique that enables capturing of detailed 3D models from flash photographs integrating shading and silhouette cues. Our main contribution is an optimization framework which not only captures subtle surface details but also handles changes in topology. To incorporate normals estimated from shading, we employ a mesh-based deformable model using deformation gradient. This method is capable of manipulating precise geometry and, in fact, it outperforms previous methods in terms of both accuracy and efficiency. To adapt the topology of the mesh, we convert the mesh into an implicit surface representation and then back to a mesh representation. This simple procedure removes self-intersecting regions of the mesh and solves the topology problem effectively. In addition to the algorithm, we introduce a hand-held setup to achieve multi-view photometric stereo. The key idea is to acquire flash photographs from a wide range of positions in order to obtain a sufficient lighting variation even with a standard flash unit attached to the camera. Experimental results showed that our method can capture detailed shapes of various objects and cope with topology changes well.",
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"abstract": "As an important numerical analysis method of rock mechanics, discontinuous deformation analysis (DDA) has been widely used in rock engineering. DDA has certain advantages such as the large time step and the large deformation, at the cost of relatively low computing efficiency. To address the efficiency bottleneck of DDA, this paper proposes a complete graphics processing unit (GPU)-based version. The entire DDA pipeline, involving contact detection, global matrix building, linear equation solving, and interpenetration checking, is restructured according to the GPU architecture to minimize data transmissions between the host and device. For the equation solver in DDA, a comparison study of the conjugate gradient method with different preconditioners, i.e., block Jacobi, symmetric successive over-relaxation (SSOR) approximate inverse, and ILU, is introduced first, and a novel sparse matrix-vector multiplication (SpMV) method, intended for the sparse block symmetry matrix with distinct features and which outperforms cuSPARSE by 2.8 times, is proposed as well. Schemes to solve memory write conflicts and branch divergences on the GPU are also introduced in contact detection, global matrix building, and interpenetration checking. For the stable analysis of a slope, the proposed GPU-based DDA with double precision achieved a speed-up rate that was 48.72 times higher than that of the original CPU-based serial implementation.",
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"abstract": "The purpose of this research is to analysis feature parameters of Suriashi (sliding gait motion in Japanese traditional performing arts and Japanese traditional martial arts) with multivariate data analysis and to discriminate Suriashi movement among other gait motions using SVM (Support Vector Machine). Experiments were carried out using motion capture on the Suriashi of Japanese Traditional Dance and Noh and ordinary human gait. We can analyze the principal parameter to distinguish the Suriashi in various gait motions by conducting PCA (Principal Components Analysis) and cluster analysis of parameters of gait motion. It was found that gait motion of Suriashi can be recognized by using SVM to discriminate among the different gait motions. In addition, it is expected that our research will help practitioners and masters of Japanese Traditional Dance and Noh with Suriashi training through giving new information on Suriashi movements.",
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"abstract": "Image motion blur and defocus blur often occur when there is a relative motion between the imaging camera and the detected object. These two blurs will degrade the image quality and will also decrease the subsequent pattern recognition accuracy. In this paper, we propose a robust weed recognition scheme using the low quality color weed images with the above-mentioned image blurs. The proposed scheme consists of three steps. First, image matte is used to segment the soil and the plant. Second, a generative learning method is introduced in the training step to simulate blurred images by controlling blur parameters. Finally, weed recognition is performed by using the blurred color information based on the subspace method. We have experimentally proved that the effective use of image blurs improves the recognition accuracy of camera-captured weeds.",
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"abstract": "A motion-blur image has special blurred information which can be dealt to estimate its blurred direction. There are several methods to identify the direction such as based on directional derivation and weighted average [1]. The blurred image can't recognized smoothly and timely in the area of industrial machine vision. This paper provides an effective pretreatment method to indentify the motion-blur direction related to the high frequency of the image. This method satisfies the high-speed requirements in industrial machine vision. Find the interval of the direction, then used the optimized method based on direction of differential coefficient to find the direction of the motion-blur image. The merit of this method is increased the convergence speed and save the time complexity.",
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"abstract": "To analyze large amounts of numerical data, one of the most useful approaches is to use scientific visualization to transform them into graphical images. Flow visualization as one of the challenging topics has played important roles in oceanic data analysis. There are many techniques have been presented in the past decade, but most of them can't get high performance to visualize large-scale flow data in real time. To deduce the computational complexity brought by large flow dataset, feature-based expression will be a helpful way. However, how to get the result images quickly without costing much time for feature extraction and analysis is a very important problem to deal. Based on the common characteristic of flow and the unchangeable scale feather of spiral line, we present a new distributing strategy which needn't locate feature points very accurately and didn't rely on the type of feature fields. The visualization procedure not only can straight forward automatically but also can be changed with user's interactive command. The flow data obtained from the South Sea of China was verified and simulated. The result shows that this method using spiral strategy not templates to setting the seeds to emphasize the interesting fields is much faster and flexible, especially in large-scale flow data visualization.",
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"content": "To analyze large amounts of numerical data, one of the most useful approaches is to use scientific visualization to transform them into graphical images. Flow visualization as one of the challenging topics has played important roles in oceanic data analysis. There are many techniques have been presented in the past decade, but most of them can't get high performance to visualize large-scale flow data in real time. To deduce the computational complexity brought by large flow dataset, feature-based expression will be a helpful way. However, how to get the result images quickly without costing much time for feature extraction and analysis is a very important problem to deal. Based on the common characteristic of flow and the unchangeable scale feather of spiral line, we present a new distributing strategy which needn't locate feature points very accurately and didn't rely on the type of feature fields. The visualization procedure not only can straight forward automatically but also can be changed with user's interactive command. The flow data obtained from the South Sea of China was verified and simulated. The result shows that this method using spiral strategy not templates to setting the seeds to emphasize the interesting fields is much faster and flexible, especially in large-scale flow data visualization.",
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"abstract": "Method of Computational Fluid Dynamics (CFD) was applied to numerical simulation of gases' thermal and flow fields of Metal Organic Chemical Vapor Deposition (MOCVD) reactor which grows high-efficiency three-junction GaInP/GaAs/Ge tandem solar cells. Virtual Reality (VR) technology was applied to visualization of numerical simulation of gas's thermal and flow fields of MOCVD reactor. The results of numerical simulation provide optimization of processing parameters in MOCVD reactor under a certain conditions, providing rational suggestion for optimization design in size of the substrate in the reactor. The results of visualization truly and intuitively display distributing situation of gas's temperature field and velocity field in MOCVD reactor, providing further optimizations of processing parameters of GaInP thin film grown by MOCVD with theoretical basis.",
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"normalizedAbstract": "There are various scenarios where finding the most similar expression is the requirement rather than classifying one into discrete, pre-defined classes, for example, for facial expression transfer and facial expression based automatic album generation. This paper proposes a novel method for finding the most similar facial expression. Instead of the regular L2 norm distance, we investigate the use of the Structural SIMilarity (SSIM) metric for similarity comparison as a distance metric in a nearest neighbour unsupervised algorithm. The feature vectors are generated using Active Appearance Models (AAM). We also demonstrate how this technique can be extended and used for finding corresponding facial expression images across two or more subjects, which is useful in applications such as facial animation and automatic expression transfer. Person-independent facial expression performance results are shown on the Multi-PIE, FEEDTUM and AVOZES databases. We also compare the performance of the SSIM metric versus other distance metrics in a nearest neighbour search for finding the most similar facial expression to a given image.",
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"abstract": "Automatic face age estimation is challenging due to its complexity owing to genetic difference, behavior and environmental factors, the dynamics of facial aging between different individuals, etc. In this work we propose to fuse the global facial feature extracted from Active Appearance Model (AAM) and the local facial features extracted from Local Binary Pattern (LBP), as the representation of faces. Furthermore, we introduce an advanced age estimation system combining feature fusion and model selection schemes such as Least Angle Regression (LAR) and sequential approaches. Due to the fact that different facial feature representations may come with various types of measurement scales, we compare multiple normalization schemes for both facial features. We demonstrate that the feature fusion with model selection can achieve significant improvement in age estimation over single feature representation alone. Our experiment on multi-ethnicity UIUC-PAL database suggests that age estimation with feature fusion and model selection outperforms the single feature, or the full feature model.",
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"affiliation": "University of North Carolina Wilmington",
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"abstract": "Implicit large eddy simulation (ILES) computations have been performed for canonical model problems associated with flexible, flapping-wing micro air vehicles. This computationally intensive approach, which is able to directly model laminar/transitional/turbulent flowfields, requires the use of the best high performance computational platforms available. Computations are first performed for an SD7003 airfoil section at αo=4° plunging with reduced frequency k=3.93 and amplitude ho=0.05. For Rec=4×104, the dynamic-stall vortex system is laminar at inception, but experiences an abrupt breakdown associated with the onset of spanwise instability effects. The aerodynamics solver is then coupled with a nonlinear finite element solver to compute the flow over a flexible membrane wing. A description of the unsteady fluid/structure interaction for α=14° is presented indicating a close coupling between the unsteady flow behavior and the structural response. Good agreement of the computed results with available experimental measurements is shown for both problems considered.",
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"content": "Implicit large eddy simulation (ILES) computations have been performed for canonical model problems associated with flexible, flapping-wing micro air vehicles. This computationally intensive approach, which is able to directly model laminar/transitional/turbulent flowfields, requires the use of the best high performance computational platforms available. Computations are first performed for an SD7003 airfoil section at αo=4° plunging with reduced frequency k=3.93 and amplitude ho=0.05. For Rec=4×104, the dynamic-stall vortex system is laminar at inception, but experiences an abrupt breakdown associated with the onset of spanwise instability effects. The aerodynamics solver is then coupled with a nonlinear finite element solver to compute the flow over a flexible membrane wing. A description of the unsteady fluid/structure interaction for α=14° is presented indicating a close coupling between the unsteady flow behavior and the structural response. Good agreement of the computed results with available experimental measurements is shown for both problems considered.",
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"title": "High-Fidelity Computations for Flexible Micro Air Vehicle Applications",
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"abstractUrl": "/proceedings-article/hpcmp-ugc/2010/06017995/12OmNvAiSFV",
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{
"proceeding": {
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"title": "High Performance Computing and Grid in Asia Pacific Region, International Conference on",
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"article": {
"id": "12OmNxzMnPS",
"doi": "10.1109/HPC.1997.592184",
"title": "Computational Method for the Prediction of Dynamic Response of Long-Span Bridges due to Unsteady Wind Load",
"normalizedTitle": "Computational Method for the Prediction of Dynamic Response of Long-Span Bridges due to Unsteady Wind Load",
"abstract": "A two-step computational method is proposed to predict the dynamic response of the long-span bridge structures by unsteady wind loads due to vortex-shedding. Computational fluid dynamics analysis using two-dimensional model of the bridge deck section are carried out to evaluate the unsteady wind loads on the bridge deck. Three-dimensional dynamic analysis of the bridge under unsteady wind loads are followed to investigate vortex-excited oscillations of the bridge. For an economical application of the computational method proposed, an approximate calculation method of unsteady wind loads is presented. Reasonable agreements are obtained between the predictions by the computational analyses and the existing wind tunnel measurements, and between the prediction by unsteady wind loads calculated through computational fluid dynamics and the prediction by approximated unsteady wind loads.",
"abstracts": [
{
"abstractType": "Regular",
"content": "A two-step computational method is proposed to predict the dynamic response of the long-span bridge structures by unsteady wind loads due to vortex-shedding. Computational fluid dynamics analysis using two-dimensional model of the bridge deck section are carried out to evaluate the unsteady wind loads on the bridge deck. Three-dimensional dynamic analysis of the bridge under unsteady wind loads are followed to investigate vortex-excited oscillations of the bridge. For an economical application of the computational method proposed, an approximate calculation method of unsteady wind loads is presented. Reasonable agreements are obtained between the predictions by the computational analyses and the existing wind tunnel measurements, and between the prediction by unsteady wind loads calculated through computational fluid dynamics and the prediction by approximated unsteady wind loads.",
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}
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"normalizedAbstract": "A two-step computational method is proposed to predict the dynamic response of the long-span bridge structures by unsteady wind loads due to vortex-shedding. Computational fluid dynamics analysis using two-dimensional model of the bridge deck section are carried out to evaluate the unsteady wind loads on the bridge deck. Three-dimensional dynamic analysis of the bridge under unsteady wind loads are followed to investigate vortex-excited oscillations of the bridge. For an economical application of the computational method proposed, an approximate calculation method of unsteady wind loads is presented. Reasonable agreements are obtained between the predictions by the computational analyses and the existing wind tunnel measurements, and between the prediction by unsteady wind loads calculated through computational fluid dynamics and the prediction by approximated unsteady wind loads.",
"fno": "79010419",
"keywords": [
"Long Span Bridge",
"Vortex Excited Oscillation",
"Vortex Shedding",
"Turbulent Flow",
"Computational Method"
],
"authors": [
{
"affiliation": "Systems Engineering Research Institute",
"fullName": "Jae Seok Lee",
"givenName": "Jae Seok",
"surname": "Lee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Systems Engineering Research Institute",
"fullName": "Sangsan Lee",
"givenName": "Sangsan",
"surname": "Lee",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "1997-04-01T00:00:00",
"pubType": "proceedings",
"pages": "419",
"year": "1997",
"issn": null,
"isbn": "0-8186-7901-8",
"notes": null,
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{
"proceeding": {
"id": "12OmNyaXPPN",
"title": "2009 Second International Conference on Information and Computing Science",
"acronym": "icic",
"groupId": "1002818",
"volume": "4",
"displayVolume": "0",
"year": "2009",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNy4IEY3",
"doi": "10.1109/ICIC.2009.401",
"title": "Study on Numerical Simulation of Single-Phase Injection Device Flow Flied",
"normalizedTitle": "Study on Numerical Simulation of Single-Phase Injection Device Flow Flied",
"abstract": "The injection device is widely used in various industrial fields. The liquid-liquid single-phase flow flied of injection device was simulated with computational fluid dynamics (CFD) software FLUENT based on standard K-epsiv two-equation turbulent mode. The results showed that: the fluid movement disorder at the entrance of mixing chamber and is even more confusion when into the interior with the phenomenon of vortex separation and fluid corner separation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The injection device is widely used in various industrial fields. The liquid-liquid single-phase flow flied of injection device was simulated with computational fluid dynamics (CFD) software FLUENT based on standard K-epsiv two-equation turbulent mode. The results showed that: the fluid movement disorder at the entrance of mixing chamber and is even more confusion when into the interior with the phenomenon of vortex separation and fluid corner separation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The injection device is widely used in various industrial fields. The liquid-liquid single-phase flow flied of injection device was simulated with computational fluid dynamics (CFD) software FLUENT based on standard K-epsiv two-equation turbulent mode. The results showed that: the fluid movement disorder at the entrance of mixing chamber and is even more confusion when into the interior with the phenomenon of vortex separation and fluid corner separation.",
"fno": "3634d358",
"keywords": [
"Boundary Layer Turbulence",
"Computational Fluid Dynamics",
"Jets",
"Mixing",
"Navier Stokes Equations",
"Vortices",
"Numerical Simulation",
"Single Phase Injection Device Flow Field",
"Liquid Liquid Single Phase Flow Flied",
"Computational Fluid Dynamics",
"FLUENT CFD Software",
"Kappa Minus Epsilon Two Equation Turbulent Mode",
"Fluid Movement Disorder",
"Mixing Chamber Entrance",
"Vortex Separation",
"Fluid Corner Separation",
"Numerical Simulation",
"Equations",
"Computational Fluid Dynamics",
"Viscosity",
"Chemical Engineering",
"Chemical Technology",
"Chemical Industry",
"Fluid Flow",
"Computational Modeling",
"Software Standards",
"Liquid Liquid Injection Device",
"Computational Fluid Dynamics CFD",
"Single Phase Flow Flied",
"Numerical Simulation"
],
"authors": [
{
"affiliation": "Sch. of Chem. Eng., Hebei Univ. of Technol., Tianjin, China",
"fullName": "Wen-yi Chen",
"givenName": "Wen-yi",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Peking University",
"fullName": "Yi-ran An",
"givenName": "Yi-ran",
"surname": "An",
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},
{
"affiliation": "Tianjin University",
"fullName": "Nan Jiang",
"givenName": "Nan",
"surname": "Jiang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Sch. of Chem. Eng., Hebei Univ. of Technol., Tianjin, China",
"fullName": "Qing-hui Yuan",
"givenName": "Qing-hui",
"surname": "Yuan",
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}
],
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"pubDate": "2009-05-01T00:00:00",
"pubType": "proceedings",
"pages": "358-361",
"year": "2009",
"issn": "2160-7443",
"isbn": "978-0-7695-3634-7",
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