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Computation, Volume 9, Issue 2 (February 2021) – 17 articles

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25 pages, 7083 KiB  
Article
Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks
by Guillermo A. Martínez-Mascorro, José R. Abreu-Pederzini, José C. Ortiz-Bayliss, Angel Garcia-Collantes and Hugo Terashima-Marín
Computation 2021, 9(2), 24; https://doi.org/10.3390/computation9020024 - 23 Feb 2021
Cited by 31 | Viewed by 5827
Abstract
Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks generate vast amounts of data, and the surveillance staff cannot process all the information in real-time. Human sight [...] Read more.
Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks generate vast amounts of data, and the surveillance staff cannot process all the information in real-time. Human sight has critical limitations. Among those limitations, visual focus is one of the most critical when dealing with surveillance. For example, in a surveillance room, a crime can occur in a different screen segment or on a distinct monitor, and the surveillance staff may overlook it. Our proposal focuses on shoplifting crimes by analyzing situations that an average person will consider as typical conditions, but may eventually lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior—the one that may occur before the build-up phase of a crime—by detecting precise segments of a video with a high probability of containing a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily action and shoplifting samples. The results are encouraging as the model correctly classifies suspicious behavior in most of the scenarios where it was tested. For example, when classifying suspicious behavior, the best model generated in this work obtains precision and recall values of 0.8571 and 1 in one of the test scenarios, respectively. Full article
(This article belongs to the Section Computational Engineering)
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<p>Different situations may be recorded by surveillance cameras. Suspicious behavior is not the crime itself. However, particular situations will make us distrust a person if we consider their behavior to be “suspicious”.</p>
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<p>Video segmentation by using the moments obtained from the Pre-Crime Behavior Segment (PCB) method.</p>
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<p>Graphical representation of the process for suspicious behavior sample extraction.</p>
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<p>Architecture of the DL Model used for this investigation. The depth of the kernel for the 3D convolution is adjusted to 10, 30, or 90 frames, according to each particular experiment (see <a href="#sec4-computation-09-00024" class="html-sec">Section 4</a>).</p>
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<p>Overview of the experimental setup followed in this work. For a detailed description of the parameters and the relation of the samples considered for each experiment, please consult <a href="#app1-computation-09-00024" class="html-app">Appendix A</a>.</p>
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<p>Interaction plot of depth (10, 30, and 90 frames) and resolution (32 × 24, 40 × 30, 80 × 60, and 160 × 120 pixels) using the accuracy values obtained from experiment P01.</p>
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<p>Interaction plot of the proportion of the base set used for training (80%, 70%, and 60%) and resolution (32 × 24, 40 × 30, 80 × 60, and 160 × 120 pixels) using the accuracy values obtained from experiment P02.</p>
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<p>Interaction plot of depth (10, 30, and 90 frames) and resolution (32 × 24, 40 × 30, 80 × 60, and 160 × 120 pixels) using the accuracy values obtained from experiment P03.</p>
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<p>Interaction plot of depth (10, 30, and 90 frames) and resolution (32 × 24, 40 × 30, 80 × 60, and 160 × 120 pixels) using the accuracy values obtained from experiment P04 (using 60% of the dataset for training).</p>
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<p>Interaction plot of depth (10, 30, and 90 frames) and resolution (32 × 24, 40 × 30, 80 × 60, and 160 × 120 pixels) using the accuracy values obtained from experiment P04 (using 70% of the dataset for training).</p>
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<p>Confusion matrices for the best model generated for each configuration in the confirmatory experiment.</p>
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30 pages, 6832 KiB  
Article
Modified ALNS Algorithm for a Processing Application of Family Tourist Route Planning: A Case Study of Buriram in Thailand
by Narisara Khamsing, Kantimarn Chindaprasert, Rapeepan Pitakaso, Worapot Sirirak and Chalermchat Theeraviriya
Computation 2021, 9(2), 23; https://doi.org/10.3390/computation9020023 - 22 Feb 2021
Cited by 21 | Viewed by 3804
Abstract
This research presents a solution to the family tourism route problem by considering daily time windows. To find the best solution for travel routing, the modified adaptive large neighborhood search (MALNS) method, using the four destructions and the four reconstructions approach, is applied [...] Read more.
This research presents a solution to the family tourism route problem by considering daily time windows. To find the best solution for travel routing, the modified adaptive large neighborhood search (MALNS) method, using the four destructions and the four reconstructions approach, is applied here. The solution finding performance of the MALNS method is compared with an exact method running on the Lingo program. As shown by various solutions, the MALNS method can balance travel routing designs, including when many tourist attractions are present in each path. Furthermore, the results of the MALNS method are not significantly different from the results of the exact method for small problem sizes. For medium and large problem sizes, the MALNS method shows a higher performance and a smaller processing time for finding solutions. The values for the average total travel cost and average travel satisfaction rating derived by the MALNS method are approximately 0.18% for a medium problem and 0.05% for a large problem, 0.24% for a medium problem, and 0.21% for a large problem, respectively. The values derived from the exact method are slightly different. Moreover, the MALNS method calculation requires less processing time than the exact method, amounting to approximately 99.95% of the time required for the exact method. In this case study, the MALNS algorithm result shows a suitable balance of satisfaction and number of tourism places in relation to the differences between family members of different ages and genders in terms of satisfaction in tour route planning. The proposed solution methodology presents an effective high-quality solution, suggesting that the MALNS method has the potential to be a great competitive algorithm. According to the empirical results shown here, the MALNS method would be useful for creating route plans for tourism organizations that support travel route selection for family tours in Thailand. Full article
(This article belongs to the Section Computational Engineering)
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<p>Family tourism problem framework (<b>a</b>) Problem pattern of family tourism routing. and (<b>b</b>) Problem pattern solving.</p>
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<p>Example of random removal for solution destruction.</p>
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<p>Two routes removal example for K-route removal.</p>
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<p>The worst point removal.</p>
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<p>Example of relative removal.</p>
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<p>Example of two points of greedy insertion.</p>
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<p>Example of Regret-H insertion.</p>
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<p>Example of node swap insertion.</p>
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<p>Example of modified node swap insertion.</p>
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<p>Tentative comparison of the solution of the algorithms (<b>a</b>)Average total travel cost comparison of each problem size. and (<b>b</b>) Average total rating of travel satisfaction of each problem size.</p>
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<p>Routing case study (<b>a</b>) Family tourism routing. and (<b>b</b>) Non-family tourism routing.</p>
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<p>Example of software application model.</p>
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15 pages, 325 KiB  
Article
Improved Stability Criteria on Linear Systems with Distributed Interval Time-Varying Delays and Nonlinear Perturbations
by Jitsin Piyawatthanachot, Narongsak Yotha and Kanit Mukdasai
Computation 2021, 9(2), 22; https://doi.org/10.3390/computation9020022 - 21 Feb 2021
Viewed by 2161
Abstract
The problem of delay-range-dependent stability analysis for linear systems with distributed time-varying delays and nonlinear perturbations is studied without using the model transformation and delay-decomposition approach. The less conservative stability criteria are obtained for the systems by constructing a new augmented Lyapunov–Krasovskii functional [...] Read more.
The problem of delay-range-dependent stability analysis for linear systems with distributed time-varying delays and nonlinear perturbations is studied without using the model transformation and delay-decomposition approach. The less conservative stability criteria are obtained for the systems by constructing a new augmented Lyapunov–Krasovskii functional and various inequalities, which are presented in terms of linear matrix inequalities (LMIs). Four numerical examples are demonstrated for the results given to illustrate the effectiveness and improvement over other methods. Full article
22 pages, 2094 KiB  
Article
Modified Fast Inverse Square Root and Square Root Approximation Algorithms: The Method of Switching Magic Constants
by Leonid V. Moroz, Volodymyr V. Samotyy and Oleh Y. Horyachyy
Computation 2021, 9(2), 21; https://doi.org/10.3390/computation9020021 - 17 Feb 2021
Cited by 10 | Viewed by 5696
Abstract
Many low-cost platforms that support floating-point arithmetic, such as microcontrollers and field-programmable gate arrays, do not include fast hardware or software methods for calculating the square root and/or reciprocal square root. Typically, such functions are implemented using direct lookup tables or polynomial approximations, [...] Read more.
Many low-cost platforms that support floating-point arithmetic, such as microcontrollers and field-programmable gate arrays, do not include fast hardware or software methods for calculating the square root and/or reciprocal square root. Typically, such functions are implemented using direct lookup tables or polynomial approximations, with a subsequent application of the Newton–Raphson method. Other, more complex solutions include high-radix digit-recurrence and bipartite or multipartite table-based methods. In contrast, this article proposes a simple modification of the fast inverse square root method that has high accuracy and relatively low latency. Algorithms are given in C/C++ for single- and double-precision numbers in the IEEE 754 format for both square root and reciprocal square root functions. These are based on the switching of magic constants in the initial approximation, depending on the input interval of the normalized floating-point numbers, in order to minimize the maximum relative error on each subinterval after the first iteration—giving 13 correct bits of the result. Our experimental results show that the proposed algorithms provide a fairly good trade-off between accuracy and latency after two iterations for numbers of type float, and after three iterations for numbers of type double when using fused multiply–add instructions—giving almost complete accuracy. Full article
(This article belongs to the Section Computational Engineering)
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<p>Initial approximation <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> </mrow> </semantics> </math> for the reciprocal square root function on the interval <math display="inline"> <semantics> <mrow> <mi>x</mi> <mo>∈</mo> <mo stretchy="false">[</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mn>4</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math>, obtained using the fast inverse square root (FISR) method and the modified FISR method: (<b>a</b>) FISR method with the magic constant of Lomont; (<b>b</b>) method of switching magic constants (intermediate result).</p>
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<p>Alignment of the relative errors of two adjacent piecewise linear initial approximations: (<b>a</b>) approximations <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mrow> <mn>01</mn> </mrow> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mrow> <mn>02</mn> </mrow> </msub> </mrow> </semantics> </math> for the interval <math display="inline"> <semantics> <mrow> <mi>x</mi> <mo>∈</mo> <mo stretchy="false">[</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math>; (<b>b</b>) approximations <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mrow> <mn>02</mn> </mrow> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mrow> <mn>03</mn> </mrow> </msub> </mrow> </semantics> </math> for the interval <math display="inline"> <semantics> <mrow> <mi>x</mi> <mo>∈</mo> <mo stretchy="false">[</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <mn>4</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math>. Here, we ignore the relative errors on other intervals.</p>
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<p>Theoretical relative errors of the <span class="html-italic">RcpSqrt2</span> (Walczyk et al.) and <span class="html-italic">RcpSqrt31f</span> (the proposed dynamic constants (DC) initial approximation) algorithms in the interval <math display="inline"> <semantics> <mrow> <mi>x</mi> <mo>∈</mo> <mo stretchy="false">[</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mn>4</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math> after the first iteration.</p>
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<p>Comparison of the accuracy of the FISR-based algorithms for double-precision numbers—Lomont, Walczyk et al., and DC methods—and their latency on the Raspberry Pi 3 and ESP-WROOM-32 platforms: (<b>a</b>) without fused multiply–add (<math display="inline"> <semantics> <mrow> <mi>fma</mi> </mrow> </semantics> </math>) operation; (<b>b</b>) with hardware <math display="inline"> <semantics> <mrow> <mi>fma</mi> </mrow> </semantics> </math> instructions on the Raspberry Pi and software <math display="inline"> <semantics> <mrow> <mi>fma</mi> </mrow> </semantics> </math> functions on ESP-32.</p>
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15 pages, 502 KiB  
Article
Deep Learning for Fake News Detection in a Pairwise Textual Input Schema
by Despoina Mouratidis, Maria Nefeli Nikiforos and Katia Lida Kermanidis
Computation 2021, 9(2), 20; https://doi.org/10.3390/computation9020020 - 17 Feb 2021
Cited by 28 | Viewed by 6475
Abstract
In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake [...] Read more.
In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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<p>Model architecture.</p>
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<p>Accuracy performance according to training speed and batch size.</p>
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<p>Model’s accuracy with and without user_id feature.</p>
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<p>F1 score comparison for exp1 and exp2.</p>
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11 pages, 3546 KiB  
Article
A Power Dissipation Monitoring Circuit for Intrusion Detection and Botnet Prevention on IoT Devices
by Dimitrios Myridakis, Paul Myridakis and Athanasios Kakarountas
Computation 2021, 9(2), 19; https://doi.org/10.3390/computation9020019 - 6 Feb 2021
Cited by 4 | Viewed by 2768
Abstract
Recently, there has been a sharp increase in the production of smart devices and related networks, and consequently the Internet of Things. One concern for these devices, which is constantly becoming more critical, is their protection against attacks due to their heterogeneity and [...] Read more.
Recently, there has been a sharp increase in the production of smart devices and related networks, and consequently the Internet of Things. One concern for these devices, which is constantly becoming more critical, is their protection against attacks due to their heterogeneity and the absence of international standards to achieve this goal. Thus, these devices are becoming vulnerable, with many of them not even showing any signs of malfunction or suspicious behavior. The aim of the present work is to introduce a circuit that is connected in series with the power supply of a smart device, specifically an IP camera, which allows analysis of its behavior. The detection circuit operates in real time (real-time detection), sampling the supply current of the device, processing the sampled values and finally indicating any detection of abnormal activities, based on a comparison to normal operation conditions. By utilizing techniques borrowed by simple power analysis side channel attack, it was possible to detect deviations from the expected operation of the IP camera, as they occurred due to intentional attacks, quarantining the monitored device from the rest of the network. The circuit is analyzed and a low-cost implementation (under 5US$) is illustrated. It achieved 100% success in the test results, showing excellent performance in intrusion detection. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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<p>The layout of the circuit.</p>
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<p>Digital circuit schematic.</p>
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<p>Schematic of the first level of circuit board.</p>
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<p>Schematic of the second level of circuit board.</p>
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<p>Schematic of the two-level board with the FTDI.</p>
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<p>The first level of the intrusion detection and prevention circuit.</p>
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<p>The second level of the intrusion detection and prevention circuit.</p>
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<p>Current measurements without a software filter.</p>
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<p>Current measurements with the first software filter.</p>
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22 pages, 4438 KiB  
Article
Least-Squares Finite Element Method for a Meso-Scale Model of the Spread of COVID-19
by Fleurianne Bertrand and Emilie Pirch
Computation 2021, 9(2), 18; https://doi.org/10.3390/computation9020018 - 5 Feb 2021
Cited by 9 | Viewed by 3311
Abstract
This paper investigates numerical properties of a flux-based finite element method for the discretization of a SEIQRD (susceptible-exposed-infected-quarantined-recovered-deceased) model for the spread of COVID-19. The model is largely based on the SEIRD (susceptible-exposed-infected-recovered-deceased) models developed in recent works, with additional extension by a [...] Read more.
This paper investigates numerical properties of a flux-based finite element method for the discretization of a SEIQRD (susceptible-exposed-infected-quarantined-recovered-deceased) model for the spread of COVID-19. The model is largely based on the SEIRD (susceptible-exposed-infected-recovered-deceased) models developed in recent works, with additional extension by a quarantined compartment of the living population and the resulting first-order system of coupled PDEs is solved by a Least-Squares meso-scale method. We incorporate several data on political measures for the containment of the spread gathered during the course of the year 2020 and develop an indicator that influences the predictions calculated by the method. The numerical experiments conducted show a promising accuracy of predictions of the space-time behavior of the virus compared to the real disease spreading data. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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<p>Flow chart depicting the regulating functions <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>k</mi> </msub> <mo>,</mo> <mi>σ</mi> <mo>,</mo> <mi>η</mi> <mo>,</mo> <msub> <mi>β</mi> <mi>i</mi> </msub> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mo>(</mo> <mi>k</mi> <mo>=</mo> <mi>E</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <mi>Q</mi> <mo>,</mo> <mi>D</mi> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mi>E</mi> <mo>,</mo> <mi>I</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mi>δ</mi> </semantics></math> for the respective compartments of the population <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mi>j</mi> </msub> </semantics></math><math display="inline"><semantics> <mrow> <mo>(</mo> <mi>j</mi> <mo>=</mo> <mi>S</mi> <mo>,</mo> <mi>E</mi> <mo>,</mo> <mi>I</mi> <mo>,</mo> <mi>R</mi> <mo>,</mo> <mi>Q</mi> <mo>,</mo> <mi>D</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Local degrees of freedom by using <math display="inline"><semantics> <msup> <mi>P</mi> <mn>1</mn> </msup> </semantics></math>- and <math display="inline"><semantics> <mrow> <mi>R</mi> <msup> <mi>T</mi> <mn>0</mn> </msup> </mrow> </semantics></math> bases in the discretization of the first-order system to be solved with the Least-Squares Method.</p>
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<p><math display="inline"><semantics> <mrow> <mi>R</mi> <msup> <mi>T</mi> <mn>0</mn> </msup> </mrow> </semantics></math>-basis functions on a triangle patch <math display="inline"><semantics> <msub> <mi>ω</mi> <mi>T</mi> </msub> </semantics></math>.</p>
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<p>Flight data collected by the Federal Statistical Office of Germany in Reference [<a href="#B25-computation-09-00018" class="html-bibr">25</a>]. A value of 100% is assigned if the number of outgoing and incoming flights in Germany for the respective region is the same as in the year 2019.</p>
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<p>Indicator fitted to the collected data on contact restrictions and flights for comparison.</p>
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<p>Values of the parameters <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>,</mo> <msub> <mi>γ</mi> <mrow> <mi>E</mi> <mo>,</mo> <mi>R</mi> </mrow> </msub> <mo>,</mo> <mi>η</mi> </mrow> </semantics></math> with different time period fitting for the respective federal states.</p>
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<p>Values of <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>Q</mi> </msub> <mrow> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>β</mi> <mrow> <mi>E</mi> <mo>,</mo> <mi>I</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>δ</mi> <mrow> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with different time period fitting for the respective federal states.</p>
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<p>Regional spread of the virus at different time stages after initial outbreak on day 1 (D1). (<b>a</b>) D55, (<b>b</b>) D100, (<b>c</b>) D150, (<b>d</b>) D200, (<b>e</b>) D235, (<b>f</b>) D257.</p>
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<p>Predicted number of infections in Germany versus real data from RKI.</p>
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<p>Predicted number of infections in Germany, the interpolation made for each day of the week compared, and the observed data from the RKI.</p>
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<p>Error curves for the respective day of interpolation and their average marked in green; the calculated prediction is marked in violet.</p>
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<p>Sensitivity of the model towards the perturbations of the indicator.</p>
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20 pages, 1281 KiB  
Article
Weighted Consensus Segmentations
by Halima Saker, Rainer Machné, Jörg Fallmann, Douglas B. Murray, Ahmad M. Shahin and Peter F. Stadler
Computation 2021, 9(2), 17; https://doi.org/10.3390/computation9020017 - 5 Feb 2021
Cited by 1 | Viewed by 2775
Abstract
The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an [...] Read more.
The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It is again a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (1) to gain a better mathematical understanding of the Segmentation Aggregation problem and its solutions and (2) to demonstrate that consensus segmentations have useful applications. Extending previously known results we show that for a large class of distance functions only breakpoints present in at least one input segmentation appear in the consensus segmentation. Furthermore, we derive a bound on the size of consensus segments. As show-case applications, we investigate a yeast transcriptome and show that consensus segments provide a robust means of identifying transcriptomic units. This approach is particularly suited for dense transcriptomes with polycistronic transcripts, operons, or a lack of separation between transcripts. As a second application, we demonstrate that consensus segmentations can be used to robustly identify growth regimes from sets of replicate growth curves. Full article
(This article belongs to the Special Issue Bioinformatics Tools for ncRNAs)
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<p>Segmentation of RNA expression patterns. Top: RNA expression in 24 samples taken every 4 min from <span class="html-italic">Saccharomyces cerevisae</span> strain IFO 0233 (shown as color scale, with the total of all experiments shown above). The sequencing data are strand-specific, only the plus strand of about 5000 nt on chromosome V are shown. Middle: nine different segmentations computed with <tt>segmenTier</tt> using different parameter settings; see Reference [<a href="#B11-computation-09-00017" class="html-bibr">11</a>] for details on data and segmentations. Below: annotated coding and non-coding yeast genes. The rightmost block of short segments is a candidate for an unannotated ncRNA located anti-sense to the much longer protein-coding gene Utp7p on the minus strand. The data are the same as those in <a href="#computation-09-00017-f003" class="html-fig">Figure 3</a> of Reference [<a href="#B11-computation-09-00017" class="html-bibr">11</a>].</p>
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<p>Definition of auxiliary variables. The input segments contributing to <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mo>&lt;</mo> </msub> <mrow> <mo>(</mo> <msup> <mi>i</mi> <mrow> <mo>″</mo> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math> are all those to the left of the green line (i.e., the ones shown in light and dark gray. <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mo>≤</mo> </msub> <mrow> <mo>(</mo> <msup> <mi>i</mi> <mo>′</mo> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math> are to the left of the blue line, i.e, those shown in light gray. The large interval <span class="html-italic">B</span> is included in <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mo>≤</mo> </msub> <mrow> <mo>(</mo> <msup> <mi>i</mi> <mo>′</mo> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math> but not in <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mo>&lt;</mo> </msub> <mrow> <mo>(</mo> <msup> <mi>i</mi> <mrow> <mo>″</mo> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math>. The correction terms <math display="inline"><semantics> <mrow> <msubsup> <mi>δ</mi> <mo>&gt;</mo> <mo>∩</mo> </msubsup> <mrow> <mo>(</mo> <msup> <mi>i</mi> <mo>′</mo> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>δ</mi> <mo>&lt;</mo> <mo>∩</mo> </msubsup> <mrow> <mo>(</mo> <msup> <mi>i</mi> <mrow> <mo>″</mo> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math> comprise the cyan and magenta parts, respectively. The correction term <math display="inline"><semantics> <mrow> <msup> <mi>δ</mi> <mo>*</mo> </msup> <mrow> <mo>(</mo> <msup> <mi>i</mi> <mo>′</mo> </msup> <mo>,</mo> <msup> <mi>i</mi> <mrow> <mo>″</mo> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math>, finally adds takes care of the interval <span class="html-italic">B</span>.</p>
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<p>Alternative segmentations of the yeast transcriptome data shown in <a href="#computation-09-00017-f001" class="html-fig">Figure 1</a> (here, the coverage time-series is shown as gray-values and the logarithms of the total coverage). Below, we show eight alternative segmentations computed with segmenTier [<a href="#B11-computation-09-00017" class="html-bibr">11</a>] with different parameter settings. The consensus segments, computed for potential <math display="inline"><semantics> <mrow> <mi mathvariant="fraktur">e</mi> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>z</mi> <mn>2</mn> </msup> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>, match very well with the expectations from visual inspection of the data and from the annotation of yeast the genome (bottom). SRG1 is a non-coding RNA that represses the adjacent SER3 gene by transcriptional interference [<a href="#B35-computation-09-00017" class="html-bibr">35</a>].</p>
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<p>Quantitative evaluation of the consensus of genome-wide transcriptome segmentations of RNA-seq data from <span class="html-italic">S. cerevisae</span> from ref. [<a href="#B11-computation-09-00017" class="html-bibr">11</a>]. (<b>a</b>) Cumulative distribution function of the length ratios <span class="html-italic">r</span> between overlapping segments and previously annotated ORF transcripts [<a href="#B36-computation-09-00017" class="html-bibr">36</a>]. A ratio of <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> indicates a good match. The consensus (black solid line) of five representative segmentations (colored dashed lines) by <tt>segmenTier</tt> with widely different parameter settings (as indicated in <a href="#computation-09-00017-f002" class="html-fig">Figure 2</a>d of Reference [<a href="#B11-computation-09-00017" class="html-bibr">11</a>]) is at least on par with the best individual segmentation. (<b>b</b>) Overlap of the consensus with the five different input segmentations. The individual segmentations share between about 30% and 70% of their segments with the consensus (vertical jump at <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>). The consensus was computed with <math display="inline"><semantics> <mrow> <mi mathvariant="fraktur">e</mi> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>z</mi> <mn>2</mn> </msup> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
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<p>Distribution of RNA expression across the consensus segmentation of <span class="html-italic">S. cerevisae</span> IFO 0233. We distinguish segments that overlap a coding sequence (CDS) or another annotation item (Annotation) present in the current genome annotation taken from SGD (Saccharomyces Genome Database), and unannotated segments (All). An overlap of at least 30% with an annotation item was required. Densities are normalized to 1 for each class. Cumulative distributions are superimposed.</p>
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<p>Four <span class="html-italic">Escherichia coli</span> cultures were grown at identical conditions (four replicates in a larger experiment) in M9 medium with 0.2% glucose at 37 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C in a BMG Clariostar platereader and the optical density at 600 nm, <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <mo>(</mo> <msub> <mi>OD</mi> <mn>600</mn> </msub> <mspace width="4.pt"/> <mi>nm</mi> </mrow> </semantics></math> was measured every 10 minutes. The growth curves of each of the four replicates were segmented into intervals with constant slope by the <tt>dpseg</tt> algorithm with the default jump penalty parameter <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> [<a href="#B41-computation-09-00017" class="html-bibr">41</a>].</p>
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<p>Consensus segmentation (shown by blue vertical lines) for a collection of 10 random segmentations with equal weights for six different potential functions <math display="inline"><semantics> <mrow> <mi mathvariant="fraktur">e</mi> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </semantics></math>. Note that only breakpoints of the input segmentations (marked by × appear in the consensus segmentation.</p>
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15 pages, 961 KiB  
Article
An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models
by George Tsakalidis, Kostas Georgoulakos, Dimitris Paganias and Kostas Vergidis
Computation 2021, 9(2), 16; https://doi.org/10.3390/computation9020016 - 4 Feb 2021
Cited by 3 | Viewed by 2758
Abstract
Business process optimization (BPO) has become an increasingly attractive subject in the wider area of business process intelligence and is considered as the problem of composing feasible business process designs with optimal attribute values, such as execution time and cost. Despite the fact [...] Read more.
Business process optimization (BPO) has become an increasingly attractive subject in the wider area of business process intelligence and is considered as the problem of composing feasible business process designs with optimal attribute values, such as execution time and cost. Despite the fact that many approaches have produced promising results regarding the enhancement of attribute performance, little has been done to reduce the computational complexity due to the size of the problem. The proposed approach introduces an elaborate preprocessing phase as a component to an established optimization framework (bpoF) that applies evolutionary multi-objective optimization algorithms (EMOAs) to generate a series of diverse optimized business process designs based on specific process requirements. The preprocessing phase follows a systematic rule-based algorithmic procedure for reducing the library size of candidate tasks. The experimental results on synthetic data demonstrate a considerable reduction of the library size and a positive influence on the performance of EMOAs, which is expressed with the generation of an increasing number of nondominated solutions. An important feature of the proposed phase is that the preprocessing effects are explicitly measured before the EMOAs application; thus, the effects on the library reduction size are directly correlated with the improved performance of the EMOAs in terms of average time of execution and nondominated solution generation. The work presented in this paper intends to pave the way for addressing the abiding optimization challenges related to the computational complexity of the search space of the optimization problem by working on the problem specification at an earlier stage. Full article
(This article belongs to the Section Computational Engineering)
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<p>The inputs and outputs of the bpo<sup>F</sup> [<a href="#B5-computation-09-00016" class="html-bibr">5</a>].</p>
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<p>The inputs, outputs, and steps of the preprocessing phase (p3) algorithms.</p>
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<p>Number of nondominated solutions for different versions of the bpo<sup>F</sup>.</p>
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<p>Time (in seconds) for the generation of nondominated solutions.</p>
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17 pages, 2362 KiB  
Article
ESTA: Educating Adolescents in Sustainable Travel Urban Behavior through Mobile Applications Using Motivational Features
by Maria Eftychia Angelaki, Theodoros Karvounidis and Christos Douligeris
Computation 2021, 9(2), 15; https://doi.org/10.3390/computation9020015 - 2 Feb 2021
Viewed by 3336
Abstract
This paper proposes the use of motivational features in mobile applications to support adolescents’ education in sustainable travel urban behavior, so that they become more mindful of their environmental impact. To this effect, existing persuasive strategies are adopted, implemented, and integrated into six [...] Read more.
This paper proposes the use of motivational features in mobile applications to support adolescents’ education in sustainable travel urban behavior, so that they become more mindful of their environmental impact. To this effect, existing persuasive strategies are adopted, implemented, and integrated into six simulated screens of a prospective mobile application named ESTA, designed for this purpose through a user-centered design process. These screens are then assessed by secondary education pupils, the outcome of which is analyzed and presented in detail. The analysis takes into consideration the possibility for the daily use of ESTA in order for the adolescents to foster an eco-friendly and healthy transit attitude and make more sustainable mobility choices that will follow them throughout their life. The potential effectiveness of ESTA is demonstrated via two use cases: the “Daily Commuting” case is addressed towards adolescents who want to move within their area of residence or neighborhood following their daily routine and activities, while the “Weekend Entertainment” is addressed towards adolescents who want to move using the available public transport modes, encouraging them to adopt greener weekend travel habits. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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<p>Pupils’ intention to use a mobile application in order to obtain a more sustainable travel behavior.</p>
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<p>(<b>a</b>) Welcome Screen integrating social reinforcement and recognition; (<b>b</b>) Home Screen integrating options for personalization, challenges and goal setting, recognition and cause-and-effect simulation.</p>
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<p>(<b>a</b>) My Preferences Screen integrating options for gaming, social reinforcement and normative influence and cooperation; (<b>b</b>) My EcoGoals Screen integrating social network, recognition, influence on the emotion and self-monitoring and reminders.</p>
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<p>(<b>a</b>) Plan a trip Screen integrating social reinforcement, normative influence and cooperation; (<b>b</b>) My Past Trips Screen integrating self-monitoring and reminders weekly reflection, suggestion recognition, goal setting, influence on the emotion and cause-and-effect simulation.</p>
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<p>ESTA’s simulation screens overall satisfaction rating scale concerning the user interface design and the position of on-screen messages.</p>
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10 pages, 3471 KiB  
Article
Dynamic Stability Enhancement of a Hybrid Renewable Energy System in Stand-Alone Applications
by Ezzeddine Touti, Hossem Zayed, Remus Pusca and Raphael Romary
Computation 2021, 9(2), 14; https://doi.org/10.3390/computation9020014 - 1 Feb 2021
Cited by 5 | Viewed by 2845
Abstract
Renewable energy systems have been extensively developed and they are attractive to become widespread in the future because they can deliver energy at a competitive price and generally do not cause environmental pollution. However, stand-alone energy systems may not be practical for satisfying [...] Read more.
Renewable energy systems have been extensively developed and they are attractive to become widespread in the future because they can deliver energy at a competitive price and generally do not cause environmental pollution. However, stand-alone energy systems may not be practical for satisfying the electric load demands, especially in places having unsteady wind speeds with high unpredictability. Hybrid energy systems seem to be a more economically feasible alternative to satisfy the energy demands of several isolated clients worldwide. The combination of these systems makes it possible to guarantee the power stability, efficiency, and reliability. The aim of this paper is to present a comprehensive analysis and to propose a technical solution to integrate a self-excited induction generator in a low power multisource system. Therefore, to avoid the voltage collapsing and the machine demagnetization, the various parameters have to be identified. This procedure allows for the limitation of a safe operating area where the best stability of the machine can be obtained. Hence, the load variation interval is determined. An improvement of the induction generator stability will be analyzed. Simulation results will be validated through experimental tests. Full article
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<p>Multisource System configuration.</p>
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<p>Single phase equivalent circuit and <span class="html-italic">R-L-C</span> load.</p>
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<p>Evolution of Stator angular frequency for <span class="html-italic">R</span> load (continuous line) and <span class="html-italic">R-L</span> load (dashed line).</p>
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<p>Stator angular frequency variation for <span class="html-italic">R</span> load (<span class="html-italic">R</span> = 65 Ω, <span class="html-italic">C</span> = 38 µF) and <span class="html-italic">R-L</span> load (<span class="html-italic">R</span> = 59 Ω, <span class="html-italic">C</span> = 87.5 µF).</p>
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<p>Experimental test bench of SEIG connected to a DC bus.</p>
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<p>Stator voltage variation for <span class="html-italic">R</span> load changes without connection of the DC bus: (<b>a</b>) simulation results, (<b>b</b>) experimental results.</p>
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<p>SEIG voltage evolution for <span class="html-italic">R</span> load variation for SEIG connected to the DC bus: (<b>a</b>) simulation results, (<b>b</b>) experimental results.</p>
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<p>Voltage collapse for 65% of load variation for SEIG connected to the DC bus: (<b>a</b>) simulation results, (<b>b</b>) experimental results.</p>
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24 pages, 2208 KiB  
Article
Kinetic Simulations of Compressible Non-Ideal Fluids: From Supercritical Flows to Phase-Change and Exotic Behavior
by Ehsan Reyhanian, Benedikt Dorschner and Ilya Karlin
Computation 2021, 9(2), 13; https://doi.org/10.3390/computation9020013 - 30 Jan 2021
Cited by 4 | Viewed by 2845
Abstract
We investigate a kinetic model for compressible non-ideal fluids. The model imposes the local thermodynamic pressure through a rescaling of the particle’s velocities, which accounts for both long- and short-range effects and hence full thermodynamic consistency. The model is fully Galilean invariant and [...] Read more.
We investigate a kinetic model for compressible non-ideal fluids. The model imposes the local thermodynamic pressure through a rescaling of the particle’s velocities, which accounts for both long- and short-range effects and hence full thermodynamic consistency. The model is fully Galilean invariant and treats mass, momentum, and energy as local conservation laws. The analysis and derivation of the hydrodynamic limit is followed by the assessment of accuracy and robustness through benchmark simulations ranging from the Joule–Thompson effect to a phase-change and high-speed flows. In particular, we show the direct simulation of the inversion line of a van der Waals gas followed by simulations of phase-change such as the one-dimensional evaporation of a saturated liquid, nucleate, and film boiling and eventually, we investigate the stability of a perturbed strong shock front in two different fluid mediums. In all of the cases, we find excellent agreement with the corresponding theoretical analysis and experimental correlations. We show that our model can operate in the entire phase diagram, including super- as well as sub-critical regimes and inherently captures phase-change phenomena. Full article
(This article belongs to the Special Issue Computational Models for Complex Fluid Interfaces across Scales)
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<p>Flowchart of the semi-Lagrangian advection using the predictor-corrector algorithm.</p>
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<p>Joule-Thomson coefficient against reduced pressure. The value of the Joule–Thomson coefficient along the dimensionless isenthalpic lines <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo>^</mo> </mover> <mo>=</mo> <mi>h</mi> <mo>/</mo> <mi>R</mi> <msub> <mi>T</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics> </math> was measured in a wide range of reduced pressures up to <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>/</mo> <msub> <mi>P</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics> </math>. Line: Theory; Solid: <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo>^</mo> </mover> <mo>=</mo> <mn>11.25</mn> </mrow> </semantics> </math>; Dashed: <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo>^</mo> </mover> <mo>=</mo> <mn>15</mn> </mrow> </semantics> </math>; Dash dot: <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo>^</mo> </mover> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>. Symbols: Present method; Squares: <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo>^</mo> </mover> <mo>=</mo> <mn>11.25</mn> </mrow> </semantics> </math>; Triangles: <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo>^</mo> </mover> <mo>=</mo> <mn>15</mn> </mrow> </semantics> </math>; Inverted triangles: <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo>^</mo> </mover> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>. Inset: Simulated lines of constant enthalpy on the <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>r</mi> </msub> <mo>−</mo> <msub> <mi>P</mi> <mi>r</mi> </msub> </mrow> </semantics> </math> (phase) diagram.</p>
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<p>Location of the interface versus time in lattice units for four different Stefan numbers. Line: Analytical solution. Symbols: Present method.</p>
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<p>(<b>a</b>) Schematic of the nucleate problem. (<b>b</b>) The interface of the vapor bubble during the nucleation, starting from the appearance of the first nucleus until the release of the first bubble. From bottom to top; Fine-dashed: Time = 600, Dash dot: Time = 1200, Dashed: Time = 1800, Long-dashed: Time = 2400, Dash dot-dot: Time = 3000, Solid: Time = 3780. Times are measured in lattice units.</p>
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<p>Bubble release period against different gravity numbers. Symbols: Simulation. The solid line represents a function <math display="inline"> <semantics> <mrow> <mn>0.945</mn> <msup> <mi>g</mi> <mrow> <mo>−</mo> <mn>0.75</mn> </mrow> </msup> </mrow> </semantics> </math>.</p>
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<p>Bubble growth from the vapor film at <math display="inline"> <semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>2482.58</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>J</mi> <mi>a</mi> <mo>=</mo> <mn>0.064</mn> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <mi>P</mi> <mi>r</mi> <mo>=</mo> <mn>0.094</mn> </mrow> </semantics> </math>. The phase boundary is shown at different times. From left to right: <math display="inline"> <semantics> <mrow> <msup> <mi>t</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msup> <mi>t</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>9.8</mn> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msup> <mi>t</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>14.96</mn> </mrow> </semantics> </math>.</p>
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<p>Space-averaged Nusselt number as a function of dimensionless time for <math display="inline"> <semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>2482.58</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>J</mi> <mi>a</mi> <mo>=</mo> <mn>0.064</mn> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <mi>P</mi> <mi>r</mi> <mo>=</mo> <mn>0.094</mn> </mrow> </semantics> </math>. The error bars amount to <math display="inline"> <semantics> <mrow> <mo>±</mo> <mn>25</mn> <mo>%</mo> </mrow> </semantics> </math> acceptable error as shown by Klimenko [<a href="#B59-computation-09-00013" class="html-bibr">59</a>].</p>
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<p>Evolution of an initially perturbed shock (dashed line) in time in an ideal gas medium with <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>5</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>Ma</mi> <mo>=</mo> <mn>3.0</mn> </mrow> </semantics> </math>.</p>
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<p>Case(1): <b>Left</b>: Initial perturbation on the shock front. <b>Right</b>: Evolution of the shock-front at time <math display="inline"> <semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>9.73</mn> </mrow> </semantics> </math>. Both plots and their coloring, show reduced density with respect to the pre-shock value <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>/</mo> <msub> <mi>ρ</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>.</p>
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<p>Comparison between the theoretical solution and the simulations for the ripple amplitude of an initially perturbed shock propagating through (<b>left</b>) an ideal gas with <math display="inline"> <semantics> <mrow> <msub> <mi>M</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>3.0</mn> </mrow> </semantics> </math> (<b>right</b>) a van der Waals (vdW) fluid with <math display="inline"> <semantics> <mrow> <msub> <mi>M</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>3.033</mn> </mrow> </semantics> </math>.</p>
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<p>Case(3): The plot of <math display="inline"> <semantics> <msub> <mi>h</mi> <mi>D</mi> </msub> </semantics> </math> and the Hugoniot curve as a function of the downstream specific volume. It is visible that the Hugoniot curve has more than two intersection points with the Rayleigh line. In addition, as the volume decreases, there are regions where <math display="inline"> <semantics> <mrow> <msub> <mi>h</mi> <mi>D</mi> </msub> <mo>&lt;</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics> </math>.</p>
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<p>Case(3): <b>Left</b>: Initial perturbation on the shock front. <b>Right</b>: Evolution of the shock-front at time <math display="inline"> <semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>16.3</mn> </mrow> </semantics> </math>. The shock wave has split into two travelings waves in the same direction. Both plots and their coloring show reduced density with respect to the critical value <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>/</mo> <msub> <mi>ρ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics> </math>.</p>
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34 pages, 38945 KiB  
Article
The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets
by Evangelos Maltezos, Athanasios Douklias, Aris Dadoukis, Fay Misichroni, Lazaros Karagiannidis, Markos Antonopoulos, Katerina Voulgary, Eleftherios Ouzounoglou and Angelos Amditis
Computation 2021, 9(2), 12; https://doi.org/10.3390/computation9020012 - 29 Jan 2021
Cited by 11 | Viewed by 4660
Abstract
Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) [...] Read more.
Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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<p>The proposed situational awareness system, namely the INUS platform.</p>
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<p>Basic hardware modules of the system.</p>
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<p>Example snapshot of the User Interface of the intelligence officer’s workstation on the OKUTAMA dataset.</p>
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<p>Workflow of the object tracking module.</p>
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<p>Processed frames as the time goes on (frames (<b>a</b>–<b>e</b>)) during a multiple car (three objects) tracking process on the UAV123 dataset.</p>
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<p>Processed frames as the time progresses (frames (<b>a</b>–<b>g</b>)) during a multiple person (two objects) tracking process on the OKUTAMA dataset.</p>
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<p>Processed frames as the time progresses (frames (<b>a</b>–<b>e</b>)) during a multiple car (two objects) tracking process on the VIVID dataset.</p>
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<p>Processed frames as the time progresses (frames (<b>a</b>–<b>e</b>)) during a multiple person (two objects) tracking process on the LITIV2012 dataset.</p>
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<p>Object detection results (persons in blue rectangles and cars in orange rectangles) as the time goes on (frames (<b>a</b>–<b>c</b>)) on the OKUTAMA dataset and applying several deep learning schemes.</p>
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<p>Object detection results (persons in blue rectangles and cars in orange rectangles) as the time goes on (frames (<b>a</b>–<b>c</b>)) on the OKUTAMA dataset and applying several deep learning schemes.</p>
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<p>Object detection results (persons in blue rectangles and cars in orange rectangles) as time progresses (frames (<b>a</b>–<b>c</b>)) on the UCF-ARG dataset and applying several pretrained deep learning schemes.</p>
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<p>Object detection results (persons in blue rectangles and cars in orange rectangles) as time progresses (frames (<b>a</b>–<b>c</b>)) on the UCF-ARG dataset and applying several pretrained deep learning schemes.</p>
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<p>Left: annotation file; right: the corresponding image subset with the bounding boxes of each sample object (green rectangles).</p>
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<p>Distribution of the samples in each class per dataset used for the training and validation sets.</p>
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<p>Training and validation loss percentages versus training epochs for the custom YOLOv3 model.</p>
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<p>Object detection results (persons in cyan and car in orange/yellow rectangles) applying the custom YOLOv3 model in real case videos.</p>
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<p>Left column: object detection results (green rectangles) as time progresses (frames (<b>a</b>–<b>c</b>)) on the LITIV2012 dataset and applying the OBIA technique. Right column: the corresponding binary masks associated with the selected color and area thresholds.</p>
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<p>Object detection results (persons in blue rectangles and cars in orange rectangles) for the terrestrial asset using the thermal camera as time progresses (frames (<b>a</b>–<b>f</b>)).</p>
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<p>Object detection results (persons in blue rectangles and cars in orange rectangles) for the terrestrial asset using the thermal camera as time progresses (frames (<b>a</b>–<b>f</b>)).</p>
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<p>Collinearity condition in the 3D space.</p>
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<p>Examples of the calculated positions of a tracked object superimposed on Google Earth. The blue point indicates the real position of the object and the red point indicates the corresponding calculated position of the object through the localization module.</p>
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<p>Sample chess-board pattern images from the RGB camera that collected in our laboratory.</p>
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<p>Left: calibrated radial distortion curve. Right: calibrated intrinsic RGB camera parameters.</p>
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31 pages, 2649 KiB  
Article
Revisiting the Homogenized Lattice Boltzmann Method with Applications on Particulate Flows
by Robin Trunk, Timo Weckerle, Nicolas Hafen, Gudrun Thäter, Hermann Nirschl and Mathias J. Krause
Computation 2021, 9(2), 11; https://doi.org/10.3390/computation9020011 - 27 Jan 2021
Cited by 16 | Viewed by 4125
Abstract
The simulation of surface resolved particles is a valuable tool to gain more insights in the behaviour of particulate flows in engineering processes. In this work the homogenized lattice Boltzmann method as one approach for such direct numerical simulations is revisited and validated [...] Read more.
The simulation of surface resolved particles is a valuable tool to gain more insights in the behaviour of particulate flows in engineering processes. In this work the homogenized lattice Boltzmann method as one approach for such direct numerical simulations is revisited and validated for different scenarios. Those include a 3D case of a settling sphere for various Reynolds numbers. On the basis of this dynamic case, different algorithms for the calculation of the momentum exchange between fluid and particle are evaluated along with different forcing schemes. The result is an updated version of the method, which is in good agreement with the benchmark values based on simulations and experiments. The method is then applied for the investigation of the tubular pinch effect discovered by Segré and Silberberg and the simulation of hindered settling. For the latter, the computational domain is equipped with periodic boundaries for both fluid and particles. The results are compared to the model by Richardson and Zaki and are found to be in good agreement. As no explicit contact treatment is applied, this leads to the assumption of sufficient momentum transfer between particles via the surrounding fluid. The implementations are based on the open-source C++ lattice Boltzmann library OpenLB. Full article
(This article belongs to the Section Computational Engineering)
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<p>Setup of the simulation according to ten Cate at al. [<a href="#B45-computation-09-00011" class="html-bibr">45</a>].</p>
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<p>Settling velocity in the four defined cases for different combinations of forcing and momentum exchange schemes along with the experimental results by ten Cate et al. [<a href="#B45-computation-09-00011" class="html-bibr">45</a>].</p>
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<p>Extract of the results for the case of <math display="inline"><semantics> <mrow> <mi>Re</mi> <mo>=</mo> <mn>11.6</mn> </mrow> </semantics></math> with exact difference method (EDM) and momentum exchange algorithm (MEA)-W for different grid spacings along with the experimental data by ten Cate et al. [<a href="#B45-computation-09-00011" class="html-bibr">45</a>].</p>
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<p>Drag coefficient of correlations discussed in <a href="#sec2dot1-computation-09-00011" class="html-sec">Section 2.1</a> along with the simulation results plotted against the Reynolds number, computed using the maximum settling velocity measured in the simulation.</p>
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<p>Maximum settling velocity for <math display="inline"><semantics> <mrow> <msub> <mi>μ</mi> <mi mathvariant="normal">f</mi> </msub> <mo>=</mo> <mn>0.005</mn> <mo> </mo> <mrow> <mi mathvariant="normal">k</mi> <mi mathvariant="normal">g</mi> <mo> </mo> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo> </mo> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </mrow> </semantics></math> over the chosen lattice velocity <math display="inline"><semantics> <msup> <mi>u</mi> <mi mathvariant="normal">L</mi> </msup> </semantics></math> (see Equation (<a href="#FD27-computation-09-00011" class="html-disp-formula">27</a>)).</p>
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<p>Path in the <span class="html-italic">x</span>-<span class="html-italic">z</span>-plane for spheres with different <math display="inline"><semantics> <mi>Ga</mi> </semantics></math>.</p>
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<p>Contours of the stream-wise vorticity for a sphere with <math display="inline"><semantics> <mrow> <mi>Ga</mi> <mo>=</mo> <mn>548.97</mn> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math> s.</p>
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<p>Computational domain for the investigation of the tubular pinch effect.</p>
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<p>Results of various starting positions of the circle according to case 2 of Inamuro et al. [<a href="#B36-computation-09-00011" class="html-bibr">36</a>].</p>
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<p>Random distribution of spheres in the computational domain for the hindered settling simulations with a solid volume fraction of <math display="inline"><semantics> <mrow> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math>.</p>
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<p>Velocity field around the sphere at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.23</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> for the case of <math display="inline"><semantics> <mrow> <mi>Re</mi> <mo>=</mo> <mn>5.29</mn> </mrow> </semantics></math> with a solid volume fraction of <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>.</p>
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<p>Averages over the settling velocity of the top <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math> of particles for various <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>Re</mi> <mo>=</mo> <mn>49.64</mn> </mrow> </semantics></math>. The part used for temporal averaging is depicted as dashed line.</p>
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<p>Performance data as million lattice updates per second and core, regarding the number of simulated particles.</p>
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16 pages, 3014 KiB  
Article
The Performance of a Gradient-Based Method to Estimate the Discretization Error in Computational Fluid Dynamics
by Adhika Satyadharma and Harinaldi
Computation 2021, 9(2), 10; https://doi.org/10.3390/computation9020010 - 24 Jan 2021
Cited by 2 | Viewed by 3309
Abstract
Although the grid convergence index is a widely used for the estimation of discretization error in computational fluid dynamics, it still has some problems. These problems are mainly rooted in the usage of the order of a convergence variable within the model which [...] Read more.
Although the grid convergence index is a widely used for the estimation of discretization error in computational fluid dynamics, it still has some problems. These problems are mainly rooted in the usage of the order of a convergence variable within the model which is a fundamental variable that the model is built upon. To improve the model, a new perspective must be taken. By analyzing the behavior of the gradient within simulation data, a gradient-based model was created. The performance of this model is tested on its accuracy, precision, and how it will affect a computational time of a simulation. The testing is conducted on a dataset of 36 simulated variables, simulated using the method of manufactured solutions, with an average of 26.5 meshes/case. The result shows the new gradient based method is more accurate and more precise then the grid convergence index(GCI). This allows for the usage of a coarser mesh for its analysis, thus it has the potential to reduce the overall computational by at least by 25% and also makes the discretization error analysis more available for general usage. Full article
(This article belongs to the Section Computational Engineering)
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<p>An illustration of how uncertainty is calculated: (<b>a</b>) within computational fluid dynamics (CFD); (<b>b</b>) within an experiment.</p>
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<p>A visual representation of how the limiting value is calculated with the suggested model: (<b>a</b>) on the simulation data; (<b>b</b>) on the gradient.</p>
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<p>Failure rate of each method, based on the value of <span class="html-italic">p</span>.</p>
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<p>The MMS simulation result that fits condition B: (<b>a</b>) Case 12; (<b>b</b>) Case 24; (<b>c</b>) Case 30.</p>
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<p>The MMS simulation result that fits condition A: (<b>a</b>) Case 3; (<b>b</b>) Case 6; (<b>c</b>) Case 32.</p>
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<p>The result of a random data test: (<b>a</b>) U<sub>Target</sub> = 10%; (<b>b</b>) U<sub>Target</sub> = 5%; (<b>c</b>) U<sub>Target</sub> = 1%.</p>
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<p>The MMS simulation result that has switching conditions: (<b>a</b>) Case 20; (<b>b</b>) Case 35.</p>
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<p>The cell distribution in the simulation of a flow passes through a cylinder.</p>
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<p>The simulation result of a flow passes through a cylinder: (<b>a</b>) <span class="html-italic">C<sub>D</sub></span>; (<b>b</b>) time.</p>
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15 pages, 1184 KiB  
Article
High-Performance Computation in Residue Number System Using Floating-Point Arithmetic
by Konstantin Isupov
Computation 2021, 9(2), 9; https://doi.org/10.3390/computation9020009 - 21 Jan 2021
Cited by 8 | Viewed by 5691
Abstract
Residue number system (RNS) is known for its parallel arithmetic and has been used in recent decades in various important applications, from digital signal processing and deep neural networks to cryptography and high-precision computation. However, comparison, sign identification, overflow detection, and division are [...] Read more.
Residue number system (RNS) is known for its parallel arithmetic and has been used in recent decades in various important applications, from digital signal processing and deep neural networks to cryptography and high-precision computation. However, comparison, sign identification, overflow detection, and division are still hard to implement in RNS. For such operations, most of the methods proposed in the literature only support small dynamic ranges (up to several tens of bits), so they are only suitable for low-precision applications. We recently proposed a method that supports arbitrary moduli sets with cryptographically sized dynamic ranges, up to several thousands of bits. The practical interest of our method compared to existing methods is that it relies only on very fast standard floating-point operations, so it is suitable for multiple-precision applications and can be efficiently implemented on many general-purpose platforms that support IEEE 754 arithmetic. In this paper, we make further improvements to this method and demonstrate that it can successfully be applied to implement efficient data-parallel primitives operating in the RNS domain, namely finding the maximum element of an array of RNS numbers on graphics processing units. Our experimental results on an NVIDIA RTX 2080 GPU show that for random residues and a 128-moduli set with 2048-bit dynamic range, the proposed implementation reduces the running time by a factor of 39 and the memory consumption by a factor of 13 compared to an implementation based on mixed-radix conversion. Full article
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<p>Accurate computation of the floating-point interval evaluation for an RNS representation using finite precision arithmetic. For a complete description and proof of the correctness of the algorithm, see [<a href="#B21-computation-09-00009" class="html-bibr">21</a>].</p>
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<p>Number of iterations required to compute the interval evaluation of an RNS number with accuracy <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>7</mn> </mrow> </msup> </mrow> </semantics></math> in 8- and 32-moduli sets using double precision floating-point arithmetic.</p>
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<p>Number of iterations required to compute the interval evaluation of an RNS number with accuracy <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>7</mn> </mrow> </msup> </mrow> </semantics></math> in 64- and 256-moduli sets using double precision floating-point arithmetic.</p>
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<p>Calculating the maximum element of an array of RNS numbers using floating-point interval evaluations. Each interval evaluation is denoted as <math display="inline"><semantics> <mrow> <mo>[</mo> <mi>f</mi> <mo>,</mo> <mi>f</mi> <mo>]</mo> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>i</mi> <mi>d</mi> <mi>x</mi> </mrow> </semantics></math> is the index of the corresponding RNS number in the input array.</p>
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<p>Performance gains of the proposed approach over the other two approaches.</p>
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14 pages, 11943 KiB  
Article
Dam Breach Simulation with the Material Point Method
by Chendi Cao and Mitchell Neilsen
Computation 2021, 9(2), 8; https://doi.org/10.3390/computation9020008 - 20 Jan 2021
Cited by 8 | Viewed by 3536
Abstract
Dam embankment breaches caused by overtopping or internal erosion can impact both life and property downstream. It is important to accurately predict the amount of erosion, peak discharge, and the resulting downstream flow. This paper presents a new model based on the material [...] Read more.
Dam embankment breaches caused by overtopping or internal erosion can impact both life and property downstream. It is important to accurately predict the amount of erosion, peak discharge, and the resulting downstream flow. This paper presents a new model based on the material point method to simulate soil and water interaction and predict failure rate parameters. The model assumes that the dam consists of a homogeneous embankment constructed with cohesive soil, and water inflow is defined by a hydrograph using other readily available reach routing software. The model uses continuum mixture theory to describe each phase where each species individually obeys the conservation of mass and momentum. A two-grid material point method is used to discretize the governing equations. The Drucker–Prager plastic flow model, combined with a Hencky strain-based hyperelasticity model, is used to compute soil stress. Water is modeled as a weakly compressible fluid. Analysis of the model demonstrates the efficacy of our approach for existing examples of overtopping dam breach, dam failures, and collisions. Simulation results from our model are compared with a physical-based breach model, WinDAM C. The new model can capture water and soil interaction at a finer granularity than WinDAM C. The new model gradually removes the granular material during the breach process. The impact of material properties on the dam breach process is also analyzed. Full article
(This article belongs to the Section Computational Engineering)
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<p>Overtopping dam breach simulation.</p>
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<p>(<b>a</b>) Deformation of a material. (<b>b</b>) Multi-phase deformation combining water and soil.</p>
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<p>The Material Point Method (MPM) algorithm with two grid multi-species. 1. (<b>a</b>)-&gt;(<b>b</b>) Transfer to grids: the mass and momentum of each species are transferred to its corresponding grid. 2. (<b>b</b>)-&gt;(<b>c</b>) Update the grids’ momentum: the coupled water and soil grid velocities are updated. 3. (<b>c</b>)-&gt;(<b>d</b>) Update the particles: all particle states, including the momentum, velocity, and cohesion based on saturation, are updated.</p>
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<p>Overtopping dam breach simulation with different cohesion at timestamps (<b>a</b>) <math display="inline"><semantics> <msub> <mi>t</mi> <mn>0</mn> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>+</mo> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math>.</p>
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<p>The overtopping dam breach simulation with an empty (<b>a</b>) and full (<b>b</b>) reservoir at different timestamps ((1)–(4)).</p>
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<p>WinDAM C 05-HR-OBA (<b>a</b>) and the proposed MPM simulation (<b>b</b>) method comparison.</p>
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