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International Journal of Modeling Simulation and Scientific Computing最新文献

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Fuzzy approach to solve Interval Valued Transportation Problem and comparison of the effectiveness of different fuzzy numbers 区间值运输问题的模糊求解方法及不同模糊数的有效性比较
IF 1.2 Q3 Mathematics Pub Date : 2022-06-24 DOI: 10.1142/s1793962323500253
Monika Bisht, R. Dangwal
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引用次数: 0
A framework for development of digital twin industrial robot production lines based on a mechatronics approach 基于机电一体化方法的数字孪生工业机器人生产线开发框架
IF 1.2 Q3 Mathematics Pub Date : 2022-06-24 DOI: 10.1142/s1793962323410258
Yuan Zhao, Yongkui Liu, Jian Feng, Jinwei Guo, Lin Zhang
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引用次数: 1
A Parkinson's disease diagnosis approach for non-equilibrium gait data 基于非平衡步态数据的帕金森病诊断方法
IF 1.2 Q3 Mathematics Pub Date : 2022-06-24 DOI: 10.1142/s1793962323500241
Sha Wang
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引用次数: 0
Error analysis and numerical solution of generalized Benjamin-Bona-Mahony-Burgers equation using 3-scale Haar wavelets 广义Benjamin-Bona-Mahony-Burgers方程的三尺度Haar小波误差分析及数值解
IF 1.2 Q3 Mathematics Pub Date : 2022-06-18 DOI: 10.1142/s179396232350023x
S. Shukla, M. Kumar
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引用次数: 1
Research on the Effect of Thickness Difference of Weld Seam on Strip Breaking During Rolling in PL-TCM PL-TCM轧制过程中焊缝厚度差对带钢断裂影响的研究
IF 1.2 Q3 Mathematics Pub Date : 2022-06-17 DOI: 10.1142/s1793962323500228
Zedong Wu, Xiaochen Wang, Quan Yang, Dong Xu, Yun Zhou, Jianwei Zhao, Dan Chen, Jingdong Li
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引用次数: 0
Bio-Inspired Computing with Magnetic Skyrmions Using Deep Learning 使用深度学习的磁Skyrmions的生物启发计算
IF 1.2 Q3 Mathematics Pub Date : 2022-05-27 DOI: 10.1142/s1793962323500198
B. Prashanth, Mohammed Riyaz Ahmed
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引用次数: 0
Chaos in a nonautonomous model for the impact of media on disease outbreak 媒介对疾病爆发影响的非自治模型中的混沌
IF 1.2 Q3 Mathematics Pub Date : 2022-05-27 DOI: 10.1142/s1793962323500204
Rakesh Medda, Pankaj Tiwari, Samares Pal
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引用次数: 1
Moving scene-based video segmentation using fast convolutional neural network integration of VGG-16 net deep learning architecture 基于运动场景的视频分割采用快速卷积神经网络集成VGG-16网深度学习架构
IF 1.2 Q3 Mathematics Pub Date : 2022-05-24 DOI: 10.1142/s1793962323410143
G. Balachandran, J. Venu Gopala Krishnan
Video and object segmentation are considered significant research topics in image-video processing. The techniques and mathematical models involved in detecting and segmenting objects employ several modules of different high-level approaches developed for video analysis, object extraction, classification, and recognition. Moving object detection is important in various applications like video surveillance, moving object tracking. This paper proposes video segmentation of moving scene using fast convolutional neural network with VGG-16 net architecture which improves the accuracy. This developed method based on CNN sparsely represents foreground, background, and segmentation mask, which is used in reconstructing the original images. The feed-forward network-trained videos are applied for object detection in a single image with co-segmentation approach where videos or image collections are required as the input. The segmentation is performed through comparative analysis of real-time DAVIS dataset. The results of the experiment show the efficiency of this proposed method which is tested and compared with the existing techniques such as convolution neural network, [Formula: see text]-nearest neighbors, and artificial neural network by the parameters, namely accuracy, precision, recall, and F1-Score. The proposed technique has been improved in terms of accuracy by 97.8%, precision by 94%, recall by 87.9%, and F1-Score by 83.8%.
视频和目标分割是图像视频处理领域的重要研究课题。检测和分割对象的技术和数学模型采用了为视频分析、对象提取、分类和识别开发的不同高级方法的几个模块。运动目标检测在视频监控、运动目标跟踪等应用中具有重要意义。本文提出了基于VGG-16网络结构的快速卷积神经网络对运动场景进行视频分割,提高了分割精度。该方法基于CNN稀疏表示前景、背景和分割掩码,用于重建原始图像。前馈网络训练的视频应用于单个图像的目标检测,其中需要视频或图像集合作为输入。通过对实时DAVIS数据集的对比分析进行分割。实验结果表明了该方法的有效性,并通过准确率、精密度、召回率、F1-Score等参数与卷积神经网络、[公式:见文]-最近邻、人工神经网络等现有方法进行了测试和比较。准确率提高97.8%,精密度提高94%,召回率提高87.9%,F1-Score提高83.8%。
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引用次数: 0
Measurement and Simulation of Non-Premixed Flames -- A Selected Review 非预混火焰的测量与模拟——综述
IF 1.2 Q3 Mathematics Pub Date : 2022-05-13 DOI: 10.1142/s1793962323500149
L. Zhou, L. Hu
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引用次数: 0
A Theoretical Approach to the Computational Complexity Measure of Abstract DEVS Simulators 抽象DEVS模拟器计算复杂度度量的理论方法
IF 1.2 Q3 Mathematics Pub Date : 2022-05-13 DOI: 10.1142/s1793962323500137
Yoro Diouf, Oumar Y. Maiga, M. Traoré
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引用次数: 0
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International Journal of Modeling Simulation and Scientific Computing
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