DEM simulation of reverse faulting through sands with the aid of GPU computing

2015 ◽  
Vol 66 ◽  
pp. 253-263 ◽  
Author(s):  
Mohammad Hazeghian ◽  
Abbas Soroush
Keyword(s):  
Gpu Computing ◽  
Dem Simulation ◽  
Reverse Faulting

Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining

2021 ◽  
pp. 002072092098849
Author(s):  
Soumya Ranjan Nayak ◽  
S Sivakumar ◽  
Akash Kumar Bhoi ◽  
Gyoo-Soo Chae ◽  
Pradeep Kumar Mallick
Keyword(s):  
Credit Card ◽  
Mixed Mode ◽  
Processing Time ◽  
Gpu Computing ◽  
Graphical Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Large Set ◽  
Minimal Processing ◽  
Graphical Processing

Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.

scholarly journals Inhibition of temperature runaway phenomenon in the Sabatier process using bed dilution structure: LBM‐DEM simulation

AIChE Journal ◽  
2021 ◽  
Author(s):  
Yixiong Lin ◽  
Chen Yang ◽  
Cheolyong Choi ◽  
Wei Zhang ◽  
Kazui Fukumoto ◽  
...  
Keyword(s):  
Dem Simulation

Resolved CFD–DEM simulation on hydrodynamic bridging in a bend rectangle channel

2021 ◽  
Vol 43 (7) ◽  
Author(s):  
Hong Xiong ◽  
Yuxiang Chen ◽  
Ming Chen ◽  
Hui Cheng ◽  
Chunliang Yu ◽  
...  
Keyword(s):  
Dem Simulation

scholarly journals Driving stress and seismotectonic implications of the 2013 Mw5.8 Awaji Island earthquake, southwestern Japan, based on earthquake focal mechanisms before and after the mainshock

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Kazutoshi Imanishi ◽  
Makiko Ohtani ◽  
Takahiko Uchide
Keyword(s):  
Stress Field ◽  
Stress Tensor ◽  
Nankai Trough ◽  
Source Region ◽  
Local Scale ◽  
Southwest Japan ◽  
Stress Tensor Inversion ◽  
Earthquake Fault ◽  
Reverse Faulting ◽  
Before And After

Abstract A driving stress of the Mw5.8 reverse-faulting Awaji Island earthquake (2013), southwest Japan, was investigated using focal mechanism solutions of earthquakes before and after the mainshock. The seismic records from regional high-sensitivity seismic stations were used. Further, the stress tensor inversion method was applied to infer the stress fields in the source region. The results of the stress tensor inversion and the slip tendency analysis revealed that the stress field within the source region deviates from the surrounding area, in which the stress field locally contains a reverse-faulting component with ENE–WSW compression. This local fluctuation in the stress field is key to producing reverse-faulting earthquakes. The existing knowledge on regional-scale stress (tens to hundreds of km) cannot predict the occurrence of the Awaji Island earthquake, emphasizing the importance of estimating local-scale (< tens of km) stress information. It is possible that the local-scale stress heterogeneity has been formed by local tectonic movement, i.e., the formation of flexures in combination with recurring deep aseismic slips. The coseismic Coulomb stress change, induced by the disastrous 1995 Mw6.9 Kobe earthquake, increased along the fault plane of the Awaji Island earthquake; however, the postseismic stress change was negative. We concluded that the gradual stress build-up, due to the interseismic plate locking along the Nankai trough, overcame the postseismic stress reduction in a few years, pushing the Awaji Island earthquake fault over its failure threshold in 2013. The observation that the earthquake occurred in response to the interseismic plate locking has an important implication in terms of seismotectonics in southwest Japan, facilitating further research on the causal relationship between the inland earthquake activity and the Nankai trough earthquake. Furthermore, this study highlighted that the dataset before the mainshock may not have sufficient information to reflect the stress field in the source region due to the lack of earthquakes in that region. This is because the earthquake fault is generally locked prior to the mainshock. Further research is needed for estimating the stress field in the vicinity of an earthquake fault via seismicity before the mainshock alone.

scholarly journals A holistic seismotectonic model of Delhi region

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brijesh K. Bansal ◽  
Kapil Mohan ◽  
Mithila Verma ◽  
Anup K. Sutar
Keyword(s):  
Strain Energy ◽  
Fault Plane ◽  
Near Field ◽  
Energy Estimates ◽  
The West ◽  
Fault Plane Solutions ◽  
Northern India ◽  
Structural Environment ◽  
Reverse Faulting ◽  
Using Data

AbstractDelhi region in northern India experiences frequent shaking due to both far-field and near-field earthquakes from the Himalayan and local sources, respectively. The recent M3.5 and M3.4 earthquakes of 12th April 2020 and 10th May 2020 respectively in northeast Delhi and M4.4 earthquake of 29th May 2020 near Rohtak (~ 50 km west of Delhi), followed by more than a dozen aftershocks, created panic in this densely populated habitat. The past seismic history and the current activity emphasize the need to revisit the subsurface structural setting and its association with the seismicity of the region. Fault plane solutions are determined using data collected from a dense network in Delhi region. The strain energy released in the last two decades is also estimated to understand the subsurface structural environment. Based on fault plane solutions, together with information obtained from strain energy estimates and the available geophysical and geological studies, it is inferred that the Delhi region is sitting on two contrasting structural environments: reverse faulting in the west and normal faulting in the east, separated by the NE-SW trending Delhi Hardwar Ridge/Mahendragarh-Dehradun Fault (DHR-MDF). The WNW-ESE trending Delhi Sargoda Ridge (DSR), which intersects DHR-MDF in the west, is inferred as a thrust fault. The transfer of stress from the interaction zone of DHR-MDF and DSR to nearby smaller faults could further contribute to the scattered shallow seismicity in Delhi region.

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

GPU-based DEM simulation for scale-up βof bladed mixers

2020 ◽  
Author(s):  
Angga Pratama Herman ◽  
Jieqing Gan ◽  
Aibing Yu
Keyword(s):  
Scale Up ◽  
Dem Simulation
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