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检索条件"任意字段=2nd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2022"

共 201 条记录，以下是161-170 订阅

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Mechanisms of pacing during diastolic depolarization in a modeled sinoatrial node cell

Mechanisms of pacing during diastolic depolarization in a mo...

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international Biomedical Engineering Days

作者： S.S. Demir School of Biomedical Engineering University of Tennessee Memphis Memphis TN USA

Spontaneous pacing is an intrinsic property of the pacemaker cells in the sinoatrial node (SAN) of the heart. Models of the SAN cells have been developed over the last two decades to simulate the pacemaker activity. In this study, the most recent and biophysically detailed computational model of the SAN cell (S. Demir et al., Amer. J. Physiol, vol. 266, p. C832-52, 1994) was used to investigate the ionic properties that govern the diastolic depolarization phase of the action potential. The ionic mechanisms in diastole were investigated under both control and stimulated conditions. The control condition represents when the SAN cell is pacing spontaneously without any stimulus. The stimulated condition is when the pacing activity is perturbed by an electrical pulse. Phase-plane analysis techniques, illustrating the changes in current-voltage variables, were utilized in the computer simulations. The simulation data for the control and stimulated conditions demonstrate that the changes in I/sub K/, I/sub Naca/ and I/sub f/ during the first half of diastole, and changes in I/sub CaT/ and I/sub Na/ during the second half determine the diastolic depolarization rate and the occurrence of the next action potential.

关键词： Storage area networks Biomembranes Pacemakers computational modeling Electric variables control Calcium Voltage Circuits Pumps Independent component analysis

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Research Performance Evaluation of Chinese University Based on DEA

Research Performance Evaluation of Chinese University Based ...

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2017 2nd international conference on computational modeling, simulation and Applied Mathematics（CMSAM 2017)

作者： Si-cheng LI School of Economics and Management Huazhong Agricultural University

The university performance evaluation has been one of the difficult problems for a long time. And the university research performance evaluation is a difficulty of difficulties. Based on the practice experience of research performance evaluation from developed countries, this paper summarized and reflected on the main characteristics and shortcomings of Chinese university research performance evaluation indexes and methods, and built the measuring index system based on data envelopment analysis（DEA）. Then it put forward performance evaluation technology with the combination of principal component analysis（PCA） and DEA, trying to explore the evaluation mode of university research performance innovatively.

关键词： Chinese university Research performance evaluation Research assessment exercise（RAE） data envelopment analysis（DEA）

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Music Stream analysis for the Prediction of Song Popularity using Machine Learning and Deep Learning Approach

Music Stream Analysis for the Prediction of Song Popularity ...

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Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd international conference on

作者： Shruti Arora Rinkle Rani Nitin Saxena Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9781665499453

Music streaming services have increased in popularity due to ubiquitous demand of web and smart devices. Music lovers are inclined towards a variety of music styles and leave a different digital trail. The emotional responses that music can evoke in its listeners can result in both physiological adjustment and cognitive benefits. The world’s largest streaming service, Spotify, is the subject of this research. In this study, a model to predict whether a music would be featured on Hit-50 list of Spotify charts. This study employs the plotly for exploratory data analysis, offer descriptive statistical analysis, and compare machine learning approach with deep learning approach utilising Spotify data of eight months. Further, the study also identified several important features and correlation between them.

关键词： Deep learning Statistical analysis computational modeling Neural networks Music Predictive models data models

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Comparative analysis using Transfer Learning Models VGG16, Resnet 50 and Xception to Predict Pneumonia

Comparative Analysis using Transfer Learning Models VGG16, R...

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international conference on Innovative Sustainable computational Technologies (CISCT)

作者： Anil B. Desai D. R. Gangodkar Bhaskar Pant Kumud Pant Department of Computer Science & Engineering Graphic Era Deemed to be University & Graphic Era Hill University Dehradun U.K India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun U.K India Department of Biotechnology Graphic Era Deemed to be University Dehradun U.K India

ISBN: (纸本)9781665474177

Pneumonia is common illness affecting 450 million persons every year and approximately 7% of the world population getting affected among all age groups. The existence of X-ray images is an age-old method in diagnosis of different diseases. Pneumonia is one such pulmonary disease, which can be predicted by manually examining the chest X-ray (CXR) images, but the manual inspection may have its own limitations. Developments in Computer Vision Techniques using image processing lack in predicting the disease accurately. Improvements based on deep learning Neural Network models also have challenges diagnosing disease to the nearest accuracy. Recent developments based on transfer learning techniques have shown promising results. However, many researchers have published their results after experimenting with the individual models and also using ensemble techniques with varying degree of accuracy. In this paper we try to make comparative analysis using deep learning models Visual Geometry Group (VGG16), Residual Networks (Resnet50) and Extreme Inception (Xception) based on transfer learning techniques for different parameter setup on different datasets and by applying data augmentation techniques for the same cycle of epochs. The analysis carried out would help to understand the models under consideration for their capability to learn and predict new image sets.

关键词： Deep learning Analytical models Visualization Pulmonary diseases computational modeling Transfer learning Predictive models

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Study of Enhanced Multi-spectral Remote-sensing-satellite Image Technology Based on Improved Retinex-Net

Study of Enhanced Multi-spectral Remote-sensing-satellite Im...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), international conference on

作者： Peng Liu Honggang Wang Jinpeng Li Wenjun Dong Jin Zhang Beijing Institute of Remote Sensing Information

Aimed at the problem that general Retinex-Net does not apply to multi-spectral remote-sensing-satellite image enhancement, by reference to the decomposition-enhancement network architecture of general Retinex-Net, an improved Retinex-Net image enhancement model was put forward, and is used for multi-spectral remote-sensing-satellite image processing. Firstly, a multi-spectral image was converted to an IHS image space, and decomposed into intensity, hue, and saturation components, and by using a decomposition network, the intensity component was decomposed into the two parts of illumination and reflectance. Secondly, in an enhancement network, the intensity component was corrected. Finally, the three components were fused and converted to the original multichannel color space. Experiments indicated that the improved algorithm in this paper produced a certain enhanced effect on multi-spectral remote-sensing-satellite images, but was affected by such factors as insufficient coverage types and scenarios of training sample sets. Its usability is still limited, and further experiments and researches still need to be carried out.

关键词： Training Reflectivity Image color analysis computational modeling Lighting data models Sensors

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Joint Extraction model of Chinese Recipe Entities and Relations Based on Adversarial Training and Cascaded Binary Tagging

Joint Extraction model of Chinese Recipe Entities and Relati...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), international conference on

作者： Chuanhao Lan Yongzhi Zhu Qian Zhang Yikai Cui Qinghang Mao School of Computer Science Qufu Normal University RiZhao China

The process of creating a knowledge base or knowledge graph relies heavily on the extraction of relational triples from unstructured text. However, the existing methods rarely address the Chinese multi-triple and overlapping triples. We adopt a cascade binary Tagging framework combined with adversarial training (BertAdvCasLSTM) to solve this problem. When the subject information and sentence features are fused, we use a Bi-LSTM network to further feature extraction of the fused sentence features, which improves the performance of the model. We also personally crafted the Recipe_Chinese dataset to ensure the model's extraction capabilities. On both the Recipe_Chinese and DuIE1.0 Chinese datasets, our suggested BertAdvCasLSTM model outperforms the baselines. In-depth experimental analysis is carried out on different cases where a piece of data contains multiple triples, and the performance is improved in most cases. The impact of adversarial training on the overall model performance is also examined.

关键词： Training Measurement Annotations computational modeling Knowledge based systems Knowledge graphs Tagging

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Impact of the Columbia supercomputer on NASA space and exploration missions

Impact of the Columbia supercomputer on NASA space and explo...

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IEEE international conference on Space Mission Challenges for Information Technology, SMC-IT

作者： R. Biswas D. Kwak C. Kiris S. Lawrence NASA Advanced Supercomputing Division NASA Ames Research Center Moffett Field CA USA Systems Analysis and Integration Branch NASA Ames Research Center Moffett Field CA USA

NASA's 10,240-processor Columbia supercomputer gained worldwide recognition in 2004 for increasing the space agency's computing capability ten-fold, and enabling U.S. scientists and engineers to perform significant, breakthrough simulations. Columbia has amply demonstrated its capability to accelerate NASA 's key missions in space operations, exploration systems, science, and aeronautics. Columbia is part of an integrated high-end computing (HEC) environment comprised of massive storage and archive systems, highspeed networking, high-fidelity modeling and simulation tools, application performance optimization, and advanced data analysis and visualization. In this paper, we illustrate the impact Columbia is having on NASA's numerous space and exploration applications, such as the development of the Crew Exploration and Launch Vehicles (CEV/CLV), effects of long-duration human presence in space, and damage assessment and repair recommendations for remaining shuttle flights. We conclude by discussing HEC challenges that must be overcome to solve space-related science problems in the future

关键词： Space exploration Supercomputers NASA Space missions computational modeling Space shuttles Acceleration Computer networks High performance computing Analytical models

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Research on a Multi-project Scheduling Optimization Algorithm with Resource Time-Windows

Research on a Multi-project Scheduling Optimization Algorith...

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2017 2nd international conference on computational modeling, simulation and Applied Mathematics（CMSAM 2017)

作者： Song-lin LI Xing-ye DONG Sheng HAN Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong University

A resource-constrained multi-project scheduling problem with resource time-windows model is established aimed at minimizing the makespan of multi-project. In terms of solution method, a heuristic algorithm based on Serial Scheduling Generated Scheme is designed to generate an initial solution and a tabu search algorithm is proposed to optimize the solution. Experimental results show that the proposed model is feasible and the proposed method is effective.

关键词： Multi-project Scheduling Problem Time-windows Tabu Search

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The Hemodynamic Effects analysis of the New CPR Technique-Electro Ventilation Double Pump CPR: Studies in the Computer Model

The Hemodynamic Effects Analysis of the New CPR Technique-El...

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international conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Xiaoming Wu Xu Lin Yanru Zhang Qiong Liu Hengxin Yuan School of Computer Science and EngineeringSouth China University of TechnologyGuangzhou P.R.China School of Computer Science and Engineering South China University of Technology Guangzhou P.R. China South China University of Technology Guangzhou Guangdong CN School of Bioscience and BioengineeringSouth China University of TechnologyGuangzhou P.R. China Institute of Biomedical EngineeringSun Yat-Sen University Medical CollegeSun Yat-Sen UniversityGuangzhou P.R. China

To study the hemodynamic effects during human cardiac arrest and compare the new cardiopulmonary resuscitation (CPR) technique- electro ventilation double pump CPR (EDCPR) with standard CPR by building a computer model of human circulation. Developing a computer model of circulatory system; performing the effects of Standard CPR and EDCPR to the model respectively; comparing the computer simulating results. Enhanced external countpulsation (EECP) got the better hemodynamic effects than standard CPR And it was in accordance with the results of parallel animal experiments and the data published before. The developed model can successfully describe the effects of different CPR techniques to the circulation system during cardiac arrest.

关键词： Hemodynamics Ventilation Humans Cardiac arrest Cardiology Circulatory system Standards development computational modeling Computer simulation Standards publication

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Optimization of Agricultural Planting Resource Allocation Based on Particle Swarm Optimization and Monte Carlo simulation

Optimization of Agricultural Planting Resource Allocation Ba...

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Electrical, Automation and Computer Engineering (ICEACE), international conference on

作者： Lipeng Xie Hao Wang Kaiwen Li Sichuan Institute of Technology Deyang China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper aims to optimize the allocation of agricultural planting resources and enhance agricultural production efficiency and economic benefits through data preprocessing, outlier detection, linear analysis, and a multi-variable linear programming optimization model. Initially, we preprocessed the data, including handling missing values and data visualization, to ensure data quality and the effectiveness of the models. During the data preprocessing phase, we employed the 3σ principle and boxplots for outlier detection and utilized scatter plots and the Pearson correlation coefficient to analyze the linear relationships between variables. Subsequently, we established a multi-variable linear programming model that addresses the uncertainties in expected sales volume, planting costs, yields per acre, and sales prices through Monte Carlo simulation and Particle Swarm Optimization (PSO). The objective of the model is to maximize the total revenue from crops while meeting planting and growth constraints. Through the iterative process of the Particle Swarm Optimization algorithm, we obtained the optimal planting plan and generated the corresponding convergence chart. Ultimately, this paper provides data-driven decision support for crop planting planning from 2024 to 2030.

关键词： Adaptation models Monte Carlo methods Uncertainty Biological system modeling computational modeling data preprocessing Crops Linear programming Resource management Particle swarm optimization

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