scholarly journals Tsunami propagation kernel and its applications

2021 ◽  
Author(s):  
Takenori Shimozono
Keyword(s):  
Kernel Method ◽  
Numerical Models ◽  
Computational Cost ◽  
Specific Area ◽  
Tsunami Propagation ◽  
Kernel Convolution ◽  
Damping Parameter ◽  
Tsunami Risk ◽  
Convolution Kernels ◽  
Low Computational Cost

Abstract. Tsunamis rarely occur in a specific area, and their occurrence is highly uncertain. Generated from their sources in deep water, they occasionally undergo tremendous amplification over decreasing water depth to devastate low-lying coastal areas. Despite the advancement of computational power and simulation algorithms, there is a need for novel and rigorous approaches to efficiently predict coastal amplification of tsunamis during different disaster management phases, such as tsunami risk assessment and real-time forecast. This study presents convolution kernels that can instantly predict onshore waveforms of water surface elevation and flow velocity from observed/simulated wavedata apart from the shore. Kernel convolution involves isolating an incident-wave component from the offshore wavedata and transforming it into the onshore waveform. Moreover, unlike previous derived ones, the present kernels are based on shallow-water equations with a damping term and can account for tsunami attenuation on its path to the shore with a damping parameter. Kernel convolution can be implemented at a low computational cost compared to conventional numerical models that discretise the spatial domain. The prediction capability of the kernel method was demonstrated through application to real-world tsunami cases.

scholarly journals Tsunami propagation kernel and its applications

2021 ◽  
Vol 21 (7) ◽  
pp. 2093-2108
Author(s):  
Takenori Shimozono
Keyword(s):  
Shallow Water ◽  
Kernel Method ◽  
Numerical Models ◽  
Computational Cost ◽  
Specific Area ◽  
Tsunami Propagation ◽  
Wave Data ◽  
Kernel Convolution ◽  
Tsunami Risk ◽  
Offshore Wave

Abstract. Tsunamis rarely occur in a specific area, and their occurrence is highly uncertain. Suddenly generated from their sources in deep water, they occasionally undergo tremendous amplification in shallow water to devastate low-lying coastal areas. Despite the advancement of computational power and simulation algorithms, there is a need for novel and rigorous approaches to efficiently predict coastal amplification of tsunamis during different disaster management phases, such as tsunami risk assessment and real-time forecast. This study presents convolution kernels that can instantly predict onshore waveforms of water surface elevation and flow velocity from observed/simulated wave data away from the shore. Kernel convolution involves isolating an incident-wave component from the offshore wave data and transforming it into the onshore waveform. Moreover, unlike previously derived ones, the present kernels are based on shallow-water equations with a damping term and can account for tsunami attenuation on its path to the shore with a damping parameter. Kernel convolution can be implemented at a low computational cost compared to conventional numerical models that discretise the spatial domain. The prediction capability of the kernel method was demonstrated through application to real-world tsunami cases.

Study of Miniaturization of a Silicon Vapor Chamber for Compact 3D Microelectronics, via a Hybrid Analytical and Finite Element Method

2019 ◽  
Vol 6 (5) ◽  
pp. 01-18
Author(s):  
Ma Yue ◽  
Shirazy Mahmoud ◽  
Coudrain Perceval ◽  
Colonna Jean-Phulippe ◽  
Souifi Abdelkader ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Numerical Models ◽  
Computational Cost ◽  
Vapor Chamber ◽  
Cooling Systems ◽  
Heat Spreaders ◽  
Operating Limits ◽  
Silicon Vapor ◽  
Core Height ◽  
Low Computational Cost

The interest in silicon vapor chambers (SVCs) has increased in the recent years as they have been identified as efficient cooling systems for microelectronics. They present the advantage of higher thermal conductivity and smaller form factor compared to conventional heat spreaders. This work aims to investigate the potential miniaturization of these devices, preliminary to integration on the backside of mobile device chips, located as close as possible to hotspots. While detailed numerical models of vapor chamber operation are developed, an easy modeling with low computational cost is needed for an effective parametric study.  Based on the study of the operating limits, this paper shows the thinning potential of a water filled micropillar for a device operating below 10 W and identify the corresponding vapour core height, and wick thickness.

scholarly journals Study of Miniaturization of a Silicon Vapor Chamber for Compact 3D Microelectronics, via a Hybrid Analytical and Finite Element Method

2019 ◽  
Vol 7 (6) ◽  
pp. 1-16
Author(s):  
Yue MA ◽  
M. R. S. Shirazy ◽  
Q. Struss ◽  
P. Coudrain ◽  
J.P. Colonna ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Numerical Models ◽  
Computational Cost ◽  
Vapor Chamber ◽  
Cooling Systems ◽  
Heat Spreaders ◽  
Operating Limits ◽  
Silicon Vapor ◽  
Core Height ◽  
Low Computational Cost

The interest in silicon vapor chambers (SVCs) has increased in the recent years as they have been identified as efficient cooling systems for microelectronics. They present the advantage of higher thermal conductivity and smaller form factor compared to conventional heat spreaders. This work aims to investigate the potential miniaturization of these devices, preliminary to integration on the backside of mobile device chips, located as close as possible to hotspots. While detailed numerical models of vapor chamber operation are developed, an easy modeling with low computational cost is needed for an effective parametric study.  Based on the study of the operating limits, this paper shows the thinning potential of a water filled micropillar for a device operating below 10 W and identify the corresponding vapour core height, and wick thickness.

scholarly journals Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6808
Author(s):  
Jianqiang Xiao ◽  
Dianbo Ma ◽  
Satoshi Yamane
Keyword(s):  
Neural Networks ◽  
Computational Complexity ◽  
Convolutional Neural Networks ◽  
Stereo Matching ◽  
State Of The Art ◽  
Computational Cost ◽  
The State ◽  
Matching Network ◽  
Convolution Kernels ◽  
Low Computational Cost

Despite recent stereo matching algorithms achieving significant results on public benchmarks, the problem of requiring heavy computation remains unsolved. Most works focus on designing an architecture to reduce the computational complexity, while we take aim at optimizing 3D convolution kernels on the Pyramid Stereo Matching Network (PSMNet) for solving the problem. In this paper, we design a series of comparative experiments exploring the performance of well-known convolution kernels on PSMNet. Our model saves the computational complexity from 256.66G MAdd (Multiply-Add operations) to 69.03G MAdd (198.47G MAdd to 10.84G MAdd for only considering 3D convolutional neural networks) without losing accuracy. On Scene Flow and KITTI 2015 datasets, our model achieves results comparable to the state-of-the-art with a low computational cost.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

scholarly journals An optimal and low computational cost fractional Newton-type method for solving nonlinear equations

2021 ◽  
pp. 107650
Author(s):  
Giro Candelario ◽  
Alicia Cordero ◽  
Juan R. Torregrosa ◽  
María P. Vassileva
Keyword(s):  
Nonlinear Equations ◽  
Computational Cost ◽  
Type Method ◽  
Low Computational Cost

Low-Computational Cost Estimation Algorithm for Adaptive Optics Systems*

2011 ◽  
Vol 44 (1) ◽  
pp. 5573-5578
Author(s):  
M. Abbas Turki ◽  
D. Esqueda Merino ◽  
K. Kasper ◽  
C. Durieu
Keyword(s):  
Adaptive Optics ◽  
Cost Estimation ◽  
Computational Cost ◽  
Estimation Algorithm ◽  
Low Computational Cost

scholarly journals Status Estimation and In-Process Connection of Kanbans Using BLE Beacons and LPWA Network to Implement Intra-Traceability for the Kanban System

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5038
Author(s):  
Kosuke Shima ◽  
Masahiro Yamaguchi ◽  
Takumi Yoshida ◽  
Takanobu Otsuka
Keyword(s):  
Inventory Management ◽  
Low Cost ◽  
Computational Cost ◽  
Low Energy ◽  
Manufacturing Strategy ◽  
Implementation Cost ◽  
Measurement Systems ◽  
Process Route ◽  
Kanban System ◽  
Low Computational Cost

IoT-based measurement systems for manufacturing have been widely implemented. As components that can be implemented at low cost, BLE beacons have been used in several systems developed in previous research. In this work, we focus on the Kanban system, which is a measure used in manufacturing strategy. The Kanban system emphasizes inventory management and is used to produce only required amounts. In the Kanban system, the Kanban cards are rotated through the factory along with the products, and when the products change to a different process route, the Kanban card is removed from the products and the products are assigned to another Kanban. For this reason, a single Kanban cannot trace products from plan to completion. In this work, we propose a system that uses a Bluetooth low energy (BLE) beacon to connect Kanbans in different routes but assigned to the same products. The proposed method estimates the beacon status of whether the Kanban is inside or outside a postbox, which can then be computed by a micro controller at low computational cost. In addition, the system connects the Kanbans using the beacons as paired connection targets. In an experiment, we confirmed that the system connected 70% of the beacons accurately. We also confirmed that the system could connect the Kanbans at a small implementation cost.

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