scholarly journals Multi-Objective Weather Routing of Sailboats Considering Wave Resistance

2018 ◽  
Vol 25 (1) ◽  
pp. 4-12 ◽  
Author(s):  
Marcin Życzkowski ◽  
Przemysław Krata ◽  
Rafał Szłapczyński
Keyword(s):  
Input Data ◽  
Wave Resistance ◽  
Wind Vector ◽  
Sea Waves ◽  
Resistance Increase ◽  
Deterministic Algorithms ◽  
Proposed Model ◽  
Basic Input ◽  
Vessel Speed ◽  
Speed Characteristic

Abstract The article presents a method to determine the route of a sailing vessel with the aid of deterministic algorithms. The method assumes that the area in which the route is to be determined is limited and the basic input data comprise the wind vector and the speed characteristic of the vessel. Compared to previous works of the authors, the present article additionally takes into account the effect of sea waves with the resultant resistance increase on the vessel speed. This approach brings the proposed model closer to real behaviour of a sailing vessel. The result returned by the method is the sailing route, optimised based on the multi-criteria objective function. Along with the time criterion, this function also takes into account comfort of voyage and the number of performed turns. The developed method has been implemented as simulation application SaillingAssistance and experimentally verified.

scholarly journals Status of Molecular Opacities of Interest in the Modeling of a Proto-Solar Nebula

1974 ◽  
Vol 65 ◽  
pp. 13-20
Author(s):  
W. F. Huebner ◽  
L. W. Fullerton
Keyword(s):  
Optical Properties ◽  
Equation Of State ◽  
Volatile Compounds ◽  
Gaseous Phase ◽  
Input Data ◽  
Solar Nebula ◽  
Ionic Species ◽  
Constitutive Properties ◽  
Gas Medium ◽  
Basic Input

The report centers on general procedures applicable to the calculation of constitutive properties (equation of state and opacity) of media that serve as models for the solar nebula during planet formation and for the atmospheres of some planets. Specifically considered are the equilibrium compositions of a mixture of atoms, molecules, and their ionic species in the gaseous phase, condensation into grains with refractory cores and mantles of volatile compounds, and the ‘optical’ properties of the grain-gas medium. A summary of available and still needed basic (input) data and some currently available results are presented.

scholarly journals Uncertainty and Sensitivity Analysis of Void Reactivity Feedback for 3D BWR Assembly Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Andrius Slavickas ◽  
Raimondas Pabarčius ◽  
Aurimas Tonkūnas ◽  
Eugenijus Ušpuras
Keyword(s):  
Sensitivity Analysis ◽  
Cross Section ◽  
Input Data ◽  
Statistical Approach ◽  
Assembly Model ◽  
Uncertainty And Sensitivity Analysis ◽  
Geometrical Dimensions ◽  
Basic Input ◽  
Propagation Of Uncertainties ◽  
Selection Of

Uncertainty and sensitivity analysis of void reactivity feedback for 3D BWR fuel assembly model is presented in this paper. Uncertainties in basic input data, such as the selection of different cross section library, manufacturing uncertainties in material compositions, and geometrical dimensions, as well as operating data are considered. An extensive modelling of different input data realizations associated with their uncertainties was performed during sensitivity analysis. The propagation of uncertainties was analyzed using the statistical approach. The results revealed that important information on the code predictions can be obtained by analyzing and comparing the codes estimations and their associated uncertainties.

Fire Protection of High-Performance Concrete Using Protective Lining

2011 ◽  
Vol 82 ◽  
pp. 758-763
Author(s):  
Eike Wolfram Klingsch ◽  
Andrea Frangi ◽  
Mario Fontana
Keyword(s):  
High Performance ◽  
Input Data ◽  
High Performance Concrete ◽  
Fire Behavior ◽  
Explosive Spalling ◽  
Protective Lining ◽  
In Fire ◽  
Concrete Elements ◽  
Basic Input ◽  
Fire Design

The paper presents results of experimental and numerical analyses on the fire behavior of concrete elements protected by sprayed protective linings. Particular attention is given to high- (HPC) and ultrahigh performance concrete (UHPC), as HPC and UHPC tend to exhibit explosive spalling in fire due to low porosity. The results provide basic input data for the development of simplified rules for the fire design of concrete structures protected by sprayed protective linings.

scholarly journals Thermal inactivation of Enterococcus faecium

2008 ◽  
Vol 25 (No. 5) ◽  
pp. 283-290 ◽  
Author(s):  
V. Špelina ◽  
L. Schlemmerová ◽  
A. Landfeld ◽  
K. Kýhos ◽  
P. Měřička ◽  
...  
Keyword(s):  
Enterococcus Faecium ◽  
Input Data ◽  
Thermal Inactivation ◽  
Visual Basic ◽  
Final Concentration ◽  
Cow Milk ◽  
Basic Language ◽  
Proposed Model ◽  
Ph Range ◽  
Visual Basic Language

Data for thermal inactivation of working suspension of Enterococcus faecium in model solutions were acquired and used to develop a mathematical model for thermal inactivation of the bacterium. The model is valid within the water activity range 0.97 to 0.99; pH range 6.0 to 7.6; temperature range 60°C to 65°C, and was determined for the microorganism concentration ranges of 10<sup>2</sup> per ml to 108 per ml of the model inactivation solution. An Excel procedure was developed in Visual Basic language which enables the calculation of the final concentration of the microorganism from the input data for pH, a<sub>w</sub>, logN<sub>0</sub>, temperature, and holding time of the treatment. The proposed model was verified in experiments using cow and human milks. With cow milk, the correspondence between the experimental and the predicted data is highly satisfactory. With human milk, the model predicts a smaller effect of heating than is that manifested experimentally.

Comparative uncertainty analysis of copper loads in stormwater systems using GLUE and grey-box modeling

2007 ◽  
Vol 56 (6) ◽  
pp. 11-18 ◽  
Author(s):  
E. Lindblom ◽  
H. Madsen ◽  
P.S. Mikkelsen
Keyword(s):  
Parameter Estimation ◽  
Uncertainty Analysis ◽  
Conceptual Model ◽  
Input Data ◽  
Proposed Model ◽  
Model Predictions ◽  
Pros And Cons ◽  
Future Work ◽  
Stormwater Pollution ◽  
Made In

In this paper two attempts to assess the uncertainty involved with model predictions of copper loads from stormwater systems are made. In the first attempt, the GLUE methodology is applied to derive model parameter sets that result in model outputs encompassing a significant number of the measurements. In the second attempt the conceptual model is reformulated to a grey-box model followed by parameter estimation. Given data from an extensive measurement campaign, the two methods suggest that the output of the stormwater pollution model is associated with significant uncertainty. With the proposed model and input data, the GLUE analysis show that the total sampled copper mass can be predicted within a range of ±50% of the median value (385 g), whereas the grey-box analysis showed a prediction uncertainty of less than ±30%. Future work will clarify the pros and cons of the two methods and furthermore explore to what extent the estimation can be improved by modifying the underlying accumulation-washout model.

scholarly journals Capacity Visual Attention Networks

10.29007/lcmk ◽  
2018 ◽  
Author(s):  
Marcus Edel ◽  
Joscha Lausch
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Machine Translation ◽  
Input Data ◽  
Counting Process ◽  
Model Output ◽  
Attention Networks ◽  
Attention Model ◽  
Proposed Model ◽  
Extract Information

Inspired by recent work in machine translation and object detection, we introduce an attention-based model that automatically learns to extract information from an image by adaptively assigning its capacity across different portions of the input data and only processing the selected regions of different sizes at high resolution. This is achieved by combining two modules: an attention sub-network which uses a mechanism to model a human-like counting process and a capacity sub-network. This sub-network efficiently identifies input regions for which the attention model output is most sensitive and to which we should devote more capacity and dynamically adapt the size of the region. We focus our evaluation on the Cluttered MNIST, SVHN, and Cluttered GTSRB image datasets. Our findings indicate that the proposed model is able to drastically reduce the number of computations, compared with traditional convolutional neural networks, while maintaining similar or better performance.

Network Intrusion Detection with Auto-encoder and One-class Support Vectors Machine

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Supervised Machine Learning ◽  
False Alarms ◽  
Network Intrusion ◽  
Support Vectors ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Latent Space ◽  
Lower Accuracy

Recent advances in machine learning have shown promising results for detecting network intrusion through supervised machine learning. However, such techniques are ineffective for new types of attacks. In the preferred unsupervised and semi-supervised cases, these newer techniques suffer from lower accuracy and higher rates of false alarms. This work proposes a machine learning model that combines auto-encoder with one-class support vectors machine. In this model, the auto-encoders learn the representation of the input data in a latent space and reduces the dimensionality of the input data. The dimensionality-reduced input is then extracted from the auto-encoder and passed to a one-class support vectors machine to classify the network event as an attack or a normal event. The model is trained on normal network events only. The proposed model is then evaluated and compared with several existing models. It achieves high accuracy when tested on the NSL-KDD and KDD99 datasets, with total accuracies of 96.24% and 99.45%, respectively.

Anomalies detection in the application layer with new combined methods in IoT networks

2021 ◽  
pp. 1-8
Author(s):  
Adeleh Jafar Gholi Beik ◽  
Mohammad Ebrahim Shiri Ahmad Abadib ◽  
Afshin Rezakhani
Keyword(s):  
Internet Of Things ◽  
Input Data ◽  
The Internet ◽  
Application Layer ◽  
Data Set ◽  
Malicious Activity ◽  
Proposed Model ◽  
Improved Model ◽  
Internet Networks ◽  
The Internet Of Things

Today, due to increasing dependence on the internet, the tendency to make smart and the Internet of things (IoT), has risen. Also, detecting attacks, and malicious activity as well as anomalies on the internet networks, and preventing them from different layers is a necessity. In this method, a new hybrid model of IWC clustering and Random Forest methods are introduced to identify normal and abnormal conditions. It also shows unauthorized access and attacks to different layers of the Internet of Things, especially the application layer. The IWC is a clustering and improved model of the k-means method. After being tested, evaluated, and compared with previous methods, the proposed model indicates that identifying anomalies in, its data has been efficient and useful. Unlabeled data from the Intel data set IBRL is used to cluster its input data. The NSL-KDD data set is also used in the proposed method to select the best classification and identify attacks on the network.

scholarly journals Influence of the Selected Parameters on the Effectiveness of IGCC System Integrated With CCS Installation

2014 ◽  
Vol 35 (2) ◽  
pp. 233-248 ◽  
Author(s):  
Anna Skorek-Osikowska ◽  
Łukasz Bartela ◽  
Janusz Kotowicz
Keyword(s):  
Input Data ◽  
Emissions Trading ◽  
Ecological Analysis ◽  
Auxiliary Power ◽  
Emissions Trading Scheme ◽  
Trading Scheme ◽  
Break Even Point ◽  
Basic Input ◽  
Eu Emissions Trading Scheme ◽  
The Eu

Abstract The paper presents the basic input data and modelling results of IGCC system with membrane CO2 capture installation and without capture. The models were built using commercial software (Aspen and GateCycle) and with the use of authors’ own computational codes. The main parameters of the systems were calculated, such as gross and net power, auxiliary power of individual installations and efficiencies. The models were used for the economic and ecological analysis of the systems. The Break Even Point method of analysis was used. The calculations took into account the EU emissions trading scheme. Sensitivity analysis on the influence of selected quantities on break-even price of electricity was performed

scholarly journals Numerical procedure for estimating temperature in solarized soils

2006 ◽  
Vol 41 (3) ◽  
pp. 533-537 ◽  
Author(s):  
Chigueru Tiba ◽  
Raquel Ghini
Keyword(s):  
Solar Radiation ◽  
Temperature Variation ◽  
Air Temperature ◽  
Input Data ◽  
Numerical Procedure ◽  
Monthly Means ◽  
Acceptable Accuracy ◽  
Proposed Model ◽  
Soil Temperatures ◽  
Maximum Air Temperature

The objective of this work was to develop a simplified numerical procedure for the estimation of accumulated monthly hours of solarized soil temperatures. The proposed model requires monthly means of daily solar radiation and maximum air temperature as input data, and a daily pattern of temperature variation assumed to be sine-shaped. The procedure was verified using observations made during the years 1992 and 1993 in Jaguariúna, SP. The proposed procedure can predict monthly temperature hours at 10 cm depth in the solarized soil, with acceptable accuracy, in the region for which it was developed.

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