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检索条件"任意字段=2019 International Conference on Modeling, Simulation and Big Data Analysis（MSBDA 2019）"

共 1293 条记录，以下是61-70 订阅

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Structural Monitoring of an Aerial Tramway System During Operation: modeling and simulation Strategy with Experimental data Validation 9th

Structural Monitoring of an Aerial Tramway System During Ope...

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9th international conference on Experimental Vibration analysis for Civil Engineering Structures, EVACES 2021

作者： Bécu, Hugo Lamarque, Claude-Henri Ture Savadkoohi, Alireza Gillard, Michel Bottollier, Christophe University of Lyon ENTPE LTDS UMR CNRS 5513 Vaulx-en-Velin France DCSA Ingénieur Conseil Meylan France

ISBN: (纸本)9783030932350

The present study aims at building and calculating a global static model of a long-distance single span aerial tramway with non-symmetric tracks and anchored cables at extremities. From a design engineering point of view, aerial cable transport system is a complex structure involving several theoretical and practical issues due to diversity of phenomena at stake. Hence, appropriate framework is required to have relevant results according to the aim of the study. Influence of structural parameters modification occurring during system operation is analyzed by a static and quasistatic calculations. It provides information on parametric evolution through cable length unwound at drive bull-wheel as a previous step of dynamics study with a cable velocity. Hypotheses are discussed and modelling choices are confirmed by a comparison with experimental data measured on a real system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cables

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Safety Risk analysis of Small and Medium-sized Airports Based on Improved Bayesian Network Model

Safety Risk Analysis of Small and Medium-sized Airports Base...

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Information Technology,big data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Xiuting Zhou Weidong Liang Haofeng Wang China Academy of Civil Aviation Science and Technology Beijing China Engineering and Technical Research Center of Civil Aviation Safety Analysis and Prevention of Beijing Beijing China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In 2023, China has 259 transportation airports (excluding Hong Kong, Macao, and Taiwan), with 221 being small and medium-sized, handling less than 10 million passengers annually. These airports face shortages of personnel and equipment, complex operating environments, and limited management capabilities, making them vulnerable in civil aviation. This paper analyzes unsafe events at these airports from 2019 to 2023, focusing on factors such as timing, causes, and types of events. Utilizing Bayesian networks, the study infers uncertain information, predicting variable probability distributions under incomplete data. An improved Bayesian model assesses risk, tests correlations between various influencing factors, such as event types and consequences, and predicts various scenarios. Based on these findings, targeted safety suggestions are proposed for small and medium-sized airports, aiming to enhance their safety management and providing valuable insights for future improvements.

关键词： Analytical models Atmospheric modeling Transportation Safety management Airports Cognition Probability distribution Bayes methods Timing Risk analysis

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A Cross-Cultural Comparative Study on the Startup Discourse in 2000-2019 between United States and China 15

A Cross-Cultural Comparative Study on the Startup Discourse ...

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15th international conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Jang, Jaeyeon Kim, Byungjun Lee, Kyeo Re Kim, Jang Hyun Sungkyunkwan Univ Dept Interact Sci Seoul South Korea

ISBN: (纸本)9781665423182

This essay explores significant cultural differences on startup discourses between United States and China by demonstrating a big data analysis through Structural Topic modeling. Results and findings were interpreted with Hofstede's cultural dimensions, indicating that the conventional theory of social science was applicable when it comes to explaining technological subjects

关键词： Cultural Difference Hofstede's dimensions Startups big data analysis Time Series

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Application of Time Series analysis Methods to Assess the impact of COVID-19 on Carbon Dioxide Emission Reduction in Connecticut

Application of Time Series Analysis Methods to Assess the im...

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2022 IEEE international conference on big data, big data 2022

作者： Kiernan, Katherine A. Wu, Hao Mugno, Ray Pang, Yulei Southern Connecticut State University Department of Mathematics United States Southern Connecticut State University Department of Computer Science United States

ISBN: (纸本)9781665480451

The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic's short-term example. © 2022 IEEE.

关键词： COVID-19

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analysis on Risk Evolution Mechanism and Emergency Management Ability of Public Health Emergencies in Colleges and Universities under The Background of Risk Society 3

Analysis on Risk Evolution Mechanism and Emergency Managemen...

引用

2023 3rd international conference on big data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Dong, Yu School of Management Science and Engineering Shandong Technology and Business University Shandong Yantai China

ISBN: (纸本)9798400716478

COVID-19 has caused great losses to people's life safety and property, and has seriously affected the teaching order of colleges and universities. In order to better prevent public health emergencies in risk society, based on the method of system dynamics, this paper constructs the diffusion evolution model of public health emergencies in colleges and universities against the background of the COVID-19 outbreak at the end of 2019. Vensim software simulation shows that the infection rate plays a decisive role in the evolution of COVID-19, and reducing the infection rate can effectively prevent the large-scale spread of the epidemic. In order to further enhance the emergency management capacity of universities for public health emergencies, M university was selected as the research object. The weight of the indicators for the emergency management capacity of universities was determined through Analytic Hierarchy Process, and the fuzzy comprehensive evaluation method was used to assess the risk of the emergency management capacity of universities for public health emergencies. Measures were proposed to improve the emergency management capacity of universities for public health emergencies. © 2023 ACM.

关键词： Risk assessment

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Reliability analysis of Francis Turbine Cracking Using Gamma Frailty Model and Censored Historical Maintenance data 19

Reliability Analysis of Francis Turbine Cracking Using Gamma...

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19th international conference on Software Technologies, ICSOFT 2024

作者： Tshibangu, Théophile Mbuyi Ngoma, Guyh Dituba Gagnon, Martin Carle, Sébastien School of Engineering University of Quebec in Abitibi-Témiscamingue 445 Boulevard de l’Université Rouyn-NorandaJ9X 5E4 Canada Hydro-Québec’s Research Institute 1800 boulevard Lionel-Boulet VarennesQCJ3X 1S1 Canada Hydro-Québec 1095 Rue Saguenay Rouyn-NorandaQCJ9X 5B5 Canada

ISBN: (纸本)9789897587061

All over the world, the need for electrical energy has increased dramatically, forcing hydroelectric power plants to operate under non-standard conditions. This leads to premature fatigue cracking and consequently to multiples crack inspections. In this research, a probabilistic model is developed based on frailty and censoring. The model takes advantage of the use of a Non-Homogeneous Poisson Process (NHPP) because turbine runners are considered as repairable parts. We develop the marginal likelihood expression incorporating frailty effect using gamma frailty distribution and we use the stochastic gradient descent (SGD) algorithm to obtain the optimal parameters. Furthermore, instead of considering the frailty effect z as a random variable, we decide to derive its expression from the individual unconditional likelihood function that has been also optimized. Finally, we compare reliability and cumulative hazard functions between family members. We then confirm the results obtained by comparing reliability between two families that behaved differently. Results shows that frailty effect, that is fonction of failure statuses and individual final time of observation for a specific component has played an impor tant role in differentiating heterogeneity among groups of the same family. Reliability curves clearly demonstrate heterogeneity within and between families. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Stochastic systems

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Identifying Complicated Contagion Scenarios from Cascade data 23

Identifying Complicated Contagion Scenarios from Cascade Dat...

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29th ACM SIGKDD conference on Knowledge Discovery and data Mining (KDD)

作者： Harrison, Galen Aljundi, Amro Alabsi Chen, Jiangzhuo Ravi, S. S. Vullikanti, Anil Kumar Marathe, Madhav V. Adiga, Abhijin Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9798400701030

We consider the setting of cascades that result from contagion dynamics on large realistic contact networks. We address the question of whether the structural properties of a (partially) observed cascade can characterize the contagion scenario and identify the interventions that might be in effect. Using epidemic spread as a concrete example, we study how social interventions such as compliance in social distancing, extent (and efficacy) of vaccination, and the transmissibility of disease can be inferred. The techniques developed are more generally applicable to other contagions as well. Our approach involves the use of large realistic social contact networks of certain regions of USA and an agent-based model (ABM) to simulate spread under two interventions, namely vaccination and generic social distancing (GSD). Through a machine learning approach, coupled with parameter significance analysis, our experimental results show that subgraph counts of the graph induced by the cascade can be used effectively to characterize the contagion scenario even during the initial stages of the epidemic, when traditional information such as case counts alone are not adequate for this task. Further, we show that our approach performs well even for partially observed cascades. These results demonstrate that cascade data collected from digital tracing applications under poor digital penetration and privacy constraints can provide valuable information about the contagion scenario.

关键词： Epidemic model network diffusion simulation cascade subgraph enumeration digital contact tracing

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Workshop on Conceptual modeling, Ontologies and Metadata Management for FAIR data, FAIR 2019, 6th Workshop on Conceptual modeling in Requirements Engineering and Business analysis, MREBA 2019, 2nd international Workshop on Empirical Methods in Conceptual modeling, EmpER 2019, 8th international Workshop on modeling and Management of big data, MoBiD19 2019 and 7th international Workshop on Ontologies andConceptual Modelling, OntoCom 2019 held at the 38th international conference on Conceptual modeling, ER 2019

Workshop on Conceptual Modeling, Ontologies and Metadata Man...

引用

Workshop on Conceptual modeling, Ontologies and Metadata Management for FAIR data, FAIR 2019, 6th Workshop on Conceptual modeling in Requirements Engineering and Business analysis, MREBA 2019, 2nd international Workshop on Empirical Methods in Conceptual modeling, EmpER 2019, 8th international Workshop on modeling and Management of big data, MoBiD19 2019 and 7th international Workshop on Ontologies andConceptual Modelling, OntoCom 2019 held at the 38th international conference on Conceptual modeling, ER 2019

ISBN: (纸本)9783030341459

The proceedings contain 24 papers. The special focus in this conference is on Conceptual modeling, Ontologies and Metadata Management for FAIR data. The topics include: Facilitating data exploration in industry 4.0;towards understanding aggressive behavior in residential care facilities using process mining;on complex value relations in hive;data capture and analyses from conversational devices in the homes of the elderly;GORO 2.0: Evolving an ontology for goal-oriented requirements engineering;using ontologies for comparing modeling techniques: Experience report;domain ontology for digital marketplaces;exploring semantics in clinical data interoperability;framework for construction and incremental maintenance of high-quality linked data mashup;Aligning DMBOK and open government with the FAIR data principles;a reference architecture for customizable marketplaces;Keyword search algorithm over large RDF datasets;representing the filter bubble: Towards a model to diversification in news;The ontoOO-method: An ontology-driven conceptual modeling approach for evolving the OO-method;stochastic models to improve E-news recommender systems;Exploring reproducibility and FAIR principles in data science using ecological niche modeling as a case study;realizing traceability from the business model to enterprise architecture;creation of multiple conceptual models from user stories – a natural language processing approach;towards a catalog of goals for strategic coopetition;early identification of potential distributed ledger technology business cases using e3value Models;exploiting conceptual modeling for searching genomic metadata: a quantitative and qualitative empirical study;What are real JSON schemas like?: An empirical analysis of structural properties.

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Comparative analysis of different machine learning methods in water quality assessment based on water color

Comparative analysis of different machine learning methods i...

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2022 international conference on Electronic Information Engineering, big data, and Computer Technology, EIBDCT 2022

作者： Wang, Jiangang Zhai, Zhengang Zhu, Yunya Fang, Xusheng The 36th Research Institute of China Electronics Technology Group Corporation Zhejiang Jiaxing314000 China Jiaxing Key Laboratory of Water Econology Intelligence Zhejiang Jiaxing314000 China

ISBN: (纸本)9781510655133

It is a common method of using machine learning methods to analyze data in water quality assessment. When faced with different data and different research, different machine learning methods perform differently. In this study, in the water quality assessment problem based on water color, the effects of SVM(support vector machine) and DT(decision tree) method were compared. Through modeling, training and testing, the experimental data of the two methods were obtained. By drawing the confusion matrix and calculating the evaluation indicators, it's found that the accuracy of DT method was 0.927, which was higher than the SVM method of 0.78. Especially in the F1(harmonic mean) value, in which the DT model was 0.721, while the SVM was only 0.485, so the decision tree method performed better. Although there are still some shortcomings, this research provided a reference for the selection of machine learning methods in water quality assessment. © 2022 SPIE.

关键词： Support vector machines

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Trend analysis Neural Networks for Interpretable analysis of Longitudinal data

Trend Analysis Neural Networks for Interpretable Analysis of...

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9th IEEE international conference on big data (IEEE bigdata)

作者： Yao, Zhenjie Chen, Yixin Wang, Jinwei Wu, Shouling Tu, Yanhui Zhao, Minghui Zhang, Luxia Chinese Acad Sci Inst Microelect Beijing Peoples R China Shandong Future Network Res Inst Jinan Shandong Peoples R China Washington Univ Dept Comp Sci & Engn St Louis MO 63110 USA Peking Univ First Hosp Dept Med Renal Div Beijing Peoples R China North China Univ Sci & Thchnol Kailuan Gen Hosp Dept Cardiol Tangshan Peoples R China Nanjing Future Network Ind Innovat Co LTD Nanjing Peoples R China Peking Tsinghua Ctr Life Sci Beijing Peoples R China Peking Univ Ctr Data Sci Hlth & Med Beijing Peoples R China Peking Univ Natl Inst Hlth Data Sci Beijing Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (纸本)9781665439022

Cohort study is one of the most commonly used study methods in medical and public health researches, which result in longitudinal data. Conventional statistical models and machine learning methods are not capable of modeling the evolution trend of the variables in longitudinal data. In this paper, we propose a Trend analysis Neural Networks (TANN), which models the evolution trend of the variables by adaptive feature learning. TANN was tested on dataset of Kaiuan research. The task was to predict occurrence of death within 5 years, with 3 repeated medical examinations from 2008 to 2013. The AUC of the TANN is 0.7888, which is a slightly improvement than that of conventional methods, while that of GBDT is 0.7824, that of random forests is 0.7822, and that of logistic regression is 0.7789. The experimental results show that the proposed TANN achieves better prediction performance on death events prediction than conventional models. Furthermore, by analyzing the weights of TANN, we could find out important trends of the indicators. The trend discovery mechanism interprets the model well. TANN is an appropriate trade-off between high performance and interpretability.

关键词： Longitudinal data Trend analysis Interpretability Neural networks

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