Continuous Capture scale down model comparison of a continuous and a discrete approach

2021 ◽  
Author(s):  
Jonathan Christian Seidel ◽  
Mathias Göbel ◽  
Rui Claudio Dos Reis Rodrigues ◽  
Nora Maria Kaup ◽  
Johanna Rapp ◽  
...  
Keyword(s):  
Model Comparison ◽  
Discrete Approach ◽  
Scale Down

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

Withdrawal: Model comparison of DBD-PA-induced body force in quiescent air and separated flow over NACA0015

2020 ◽  
Author(s):  
Di Chen ◽  
Kengo Asada ◽  
Satoshi Sekimoto ◽  
Hiroyuki Nishida ◽  
Kozo Fujii
Keyword(s):  
Model Comparison ◽  
Body Force ◽  
Separated Flow

SIMULATION AND EARLY WARNING OF NATURAL AND TECHNOGENIC INFLUENCES IN COASTAL AREAS IN THE SEA OF AZOV

2017 ◽  
Author(s):  
Tatiana Shulga ◽  
Tatiana Shulga ◽  
Leonid Cherkesov ◽  
Leonid Cherkesov
Keyword(s):  
Model Comparison ◽  
Three Dimensional ◽  
Storm Surges ◽  
Ecological Condition ◽  
Sea Of Azov ◽  
The Sea Of Azov ◽  
Waves And Currents ◽  
Coordinate Model ◽  
Passive Admixture ◽  
Comparison Of The Results

In this work, the waves and currents generated by prognostic wind in the Sea of Azov are investigated using a three-dimensional nonlinear sigma-coordinate model. The mathematical model was also used for studying the transformation of passive admixture in the Sea of Azov, caused by the spatiotemporal variations in the fields of wind and atmospheric pressure, obtained from the prediction SKIRON model. Comparison of the results of numerical calculations and the data of field observations, obtained during the action of the wind on a number of hydrological stations was carried out. The evolutions of storm surges, velocities of currents and the characteristics of the pollution region at different levels of intensity of prognostic wind and stationary currents were found. The results of a comprehensive study allow reliably estimate modern ecological condition of offshore zones, develop predictive models of catastrophic water events and make science-based solutions to minimize the possible damage.

Carbon tetrachloride biodegradation in a fixed-biofilm reactor and its kinetic study

1998 ◽  
Vol 38 (8-9) ◽  
pp. 155-162 ◽  
Author(s):  
G. Jin ◽  
A. J. Englande
Keyword(s):  
Carbon Tetrachloride ◽  
Continuous Flow ◽  
Model Comparison ◽  
Biofilm Reactor ◽  
Pseudomonas Cepacia ◽  
Initial Concentration ◽  
Biphasic Model ◽  
Providencia Stuartii ◽  
Kinetics Of ◽  
Fixed Biofilm

Kinetics of Carbon Tetrachloride biodegradation are evaluated in a continuous-flow fixed-biofilm reactor with controlled initial redox potential. The column was seeded with a mixed culture of indigenous microorganisms Pseudomonas cepacia and Providencia stuartii. The fixed biofilm reactor exhibited 98%–99.9% biodegradation of CT introduced into the reactor at an initial concentration of about 200 μg/l for retention times of 1 to 4 days respectively. Four models were employed to evaluate the kinetics of CT biodegradation. These included: Eckenfelder (1989), Arvin (1991), Bouwer and McCarty (1985) and a biphasic model. Comparison of calculated results with observed results between these models agreed very closely to each other (0.968 < R2 < 0.999). Predicted performance was best described by the model of Bouwer and McCarty (1985). However, the biphasic and Eckenfelder models provided excellent correlations and were much simpler to apply. The biphasic model yielded very good correlations of the data for all detention times evaluated; whereas, the Eckenfelder model effected comparable results only at the longer retention times studied.

Impact of Big Data over Telecom Industry

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Muhammad Waqar Khan ◽  
Muhammad Asghar Khan ◽  
Muhammad Alam ◽  
Wajahat Ali
Keyword(s):  
Big Data ◽  
Data Science ◽  
Cell Phones ◽  
Smart Phones ◽  
World Population ◽  
Huge Amount ◽  
Scale Down ◽  
Telecom Industry ◽  
Telecom Sector ◽  
Theoretical Computing

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>

scholarly journals Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

2019 ◽  
Vol 9 (22) ◽  
pp. 4871 ◽  
Author(s):  
Quan Liu ◽  
Chen Feng ◽  
Zida Song ◽  
Joseph Louis ◽  
Jian Zhou
Keyword(s):  
Deep Learning ◽  
Model Comparison ◽  
Surveillance Systems ◽  
Comparison Study ◽  
Learning Models ◽  
The Core ◽  
Dump Trucks ◽  
Deep Learning Model ◽  
Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

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