Methodology of Stepwise Multi-Scale Stress Inversion for Predicting Fault Tectonics and Fracturing: Case Study for Pre-Jurassic Complex of Tomsk Region

Author(s):  
D. Konoshonkin ◽  
I. Churochkin ◽  
N. Konoshonkina ◽  
V. Belozerov ◽  
S. Zhigulskiy
Author(s):  
A.A. Klimova ◽  
◽  
A.S. Mishunina ◽  
S.V. Azarova ◽  
D.E. Fominykh ◽  
...  
Keyword(s):  

Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.


Author(s):  
Masakazu Hashimoto ◽  
Kenji Kawaike ◽  
Tomonori Deguchi ◽  
Shammi Haque ◽  
Arpan Paul ◽  
...  

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 102
Author(s):  
Maya Briani ◽  
Emiliano Cristiani ◽  
Paolo Ranut

In this paper, we propose two models describing the dynamics of heavy and light vehicles on a road network, taking into account the interactions between the two classes. The models are tailored for two-lane highways where heavy vehicles cannot overtake. This means that heavy vehicles cannot saturate the whole road space, while light vehicles can. In these conditions, the creeping phenomenon can appear, i.e., one class of vehicles can proceed even if the other class has reached the maximal density. The first model we propose couples two first-order macroscopic LWR models, while the second model couples a second-order microscopic follow-the-leader model with a first-order macroscopic LWR model. Numerical results show that both models are able to catch some second-order (inertial) phenomena such as stop and go waves. Models are calibrated by means of real data measured by fixed sensors placed along the A4 Italian highway Trieste–Venice and its branches, provided by Autovie Venete S.p.A.


2018 ◽  
Vol 630 ◽  
pp. 444-452 ◽  
Author(s):  
Junqiang Yao ◽  
Yong Zhao ◽  
Yaning Chen ◽  
Xiaojing Yu ◽  
Ruibo Zhang
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2019 ◽  
Vol 06 (03n04) ◽  
pp. 2050009
Author(s):  
Jayne Lino ◽  
Guillaume Rohat ◽  
Paul Kirshen ◽  
Hy Dao

Climate change will impact cities’ infrastructure and urban dwellers, who often show differentiated capacity to cope with climate-related hazards. The Shared Socioeconomic Pathways (SSPs) are part of an emerging research field which uses global socioeconomic and climate scenarios, developed by the climate change research community, to explore how different socioeconomic pathways will influence future society’s ability to cope with climate change. While the SSPs have been extensively used at the global scale, their use at the local and urban scale has remained rare, as they first need to be contextualized and extended for the particular place of interest. In this study, we present and apply a method to develop multi-scale extended SSPs at the city and neighborhood scale. Using Boston, Massachusetts, as a case study, we combined scenario matching, experts’ elicitation, and participatory processes to contextualize and make the global SSPs relevant at the urban scale. We subsequently employed the extended SSPs to explore future neighborhood-level vulnerability to extreme heat under multiple plausible socioeconomic trajectories, highlighting the usefulness of extended SSPs in informing future vulnerability assessments. The large differences in outcomes hint at the enormous potential of risk reduction that social and urban planning policies could trigger in the next decades.


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