Example of Modelling and Simulation in Engineering format
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Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format Example of Modelling and Simulation in Engineering format
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open access Open Access

Modelling and Simulation in Engineering — Template for authors

Publisher: Hindawi
Categories Rank Trend in last 3 yrs
Engineering (all) #109 of 297 down down by 4 ranks
Modeling and Simulation #152 of 290 up up by 9 ranks
Computer Science Applications #364 of 693 down down by 3 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 129 Published Papers | 301 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 01/06/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

2.3

44% from 2019

CiteRatio for Modelling and Simulation in Engineering from 2016 - 2020
Year Value
2020 2.3
2019 1.6
2018 1.1
2017 1.4
2016 1.2
graph view Graph view
table view Table view

0.264

40% from 2019

SJR for Modelling and Simulation in Engineering from 2016 - 2020
Year Value
2020 0.264
2019 0.188
2018 0.181
2017 0.151
2016 0.195
graph view Graph view
table view Table view

1.056

125% from 2019

SNIP for Modelling and Simulation in Engineering from 2016 - 2020
Year Value
2020 1.056
2019 0.469
2018 0.67
2017 0.402
2016 0.72
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 44% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 40% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 125% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Modelling and Simulation in Engineering

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Hindawi

Modelling and Simulation in Engineering

Modelling and Simulation in Engineering aims at providing a forum for the discussion of formalisms, methodologies and simulation tools that are intended to support the new, broader interpretation of Engineering. Competitive pressures of Global Economy have had a profound effec...... Read More

Engineering

i
Last updated on
01 Jun 2020
i
ISSN
1687-5591
i
Impact Factor
Low - 0.437
i
Acceptance Rate
11%
i
Frequency
Not provided
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
unsrt
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker. “Specular andreev reflection in graphene”. Phys. Rev. Lett., vol. 97, no. 6, 067007, 2006.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1155/2014/794574
Otsu based optimal multilevel image thresholding using firefly algorithm
N. Sri Madhava Raja1, Venkatesan Rajinikanth1, K. Latha2

Abstract:

Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu's between-class variance function is maximized to obtain optimal threshold level for... Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu's between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Levy flight (LF) guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time. read more read less

Topics:

Firefly algorithm (56%)56% related to the paper, Thresholding (54%)54% related to the paper
View PDF
130 Citations
open accessOpen access Journal Article DOI: 10.1155/2012/931943
SDNN/RMSSD as a surrogate for LF/HF: a revised investigation
Hui-Min Wang1, Sheng-Chieh Huang1

Abstract:

Thousands of papers involved in heart rate variability (HRV). However, little was known about one important measure of HRV, the root mean square of successive heartbeat interval differences (RMSSDs). Another fundamental measure SDNN indicates standard deviation of normal to normal R-R intervals, where R is the peak of a QRS c... Thousands of papers involved in heart rate variability (HRV). However, little was known about one important measure of HRV, the root mean square of successive heartbeat interval differences (RMSSDs). Another fundamental measure SDNN indicates standard deviation of normal to normal R-R intervals, where R is the peak of a QRS complex (heartbeat). Compared with SDNN, RMSSD is a short-termvariation of heart rate. Through a time-frequency transformation, the ratio of low- and high-frequency power LF/HF represents the sympatho-vagal balance of the autonomic nervous system (ANS). Some research claimed that SDNN/RMSSD was a good surrogate for LF/HF. However, only two special cases supported this hypothesis in the literature survey. The first happened in resting supine state and the other was a group of prefrontal cortex patients. Both of their Pearson correlation coefficients reached 0.90, a reasonable criterion. In our study, a 6-week experiment was performed with 32 healthy young Asian males. The Pearson correlation coefficients had a normal distribution with average values smaller than 0.6 for 3 and 5-minute epochs, respectively. Our findings suggest this surrogate aspect could remain as a hypothesis. read more read less

Topics:

Literature survey (52%)52% related to the paper
View PDF
104 Citations
open accessOpen access Journal Article DOI: 10.1155/2009/540895
MIMO system reduction using modified pole clustering and genetic algorithm
C. B. Vishwakarma1, Rajendra Prasad2

Abstract:

A new mixed method for reducing the order of the large-scale linear dynamic multi-input-multi-output (MIMO) systems has been presented. In this method, the common denominator polynomial of the reduced-order transfer function matrix is synthesized by using modified pole clustering while the coefficients of the numerator elemen... A new mixed method for reducing the order of the large-scale linear dynamic multi-input-multi-output (MIMO) systems has been presented. In this method, the common denominator polynomial of the reduced-order transfer function matrix is synthesized by using modified pole clustering while the coefficients of the numerator elements are computed by minimizing the integral square error between the time responses of the original and reduced system element using Genetic Algorithm. The modified pole clustering generates more dominant cluster centres than cluster centres obtained by pole clustering technique already available in literature. The proposed algorithm is computer-oriented and comparable in quality. This method guarantees stability of the reduced model if the original high-order system is stable. The algorithm of the proposed method is illustrated with the help of an example and the results are compared with the other well-known reduction techniques. read more read less

Topics:

CURE data clustering algorithm (61%)61% related to the paper, Cluster analysis (61%)61% related to the paper, Correlation clustering (61%)61% related to the paper, Polynomial (52%)52% related to the paper, Reduction (complexity) (52%)52% related to the paper
View PDF
75 Citations
open accessOpen access Journal Article DOI: 10.1155/2008/756436
Breast Tumor simulation and parameters estimation using evolutionary algorithms
Manu Mital1, Ramana M. Pidaparti1

Abstract:

An estimation methodology is presented to determine the breast tumor parameters using the surface temperature profile that may be obtained by infrared thermography. The estimation methodology involves evolutionary algorithms using artificial neural network (ANN) and genetic algorithm (GA). The ANN is used to map the relations... An estimation methodology is presented to determine the breast tumor parameters using the surface temperature profile that may be obtained by infrared thermography. The estimation methodology involves evolutionary algorithms using artificial neural network (ANN) and genetic algorithm (GA). The ANN is used to map the relationship of tumor parameters (depth, size, and heat generation) to the temperature profile over the idealized breast model. The relationship obtained from ANN is compared to that obtained by finite element software. Results from ANN training/testing were in good agreement with those obtained from finite element model. After ANN validation, GA is used to estimate tumor parameters by minimizing a fitness function involving comparing the temperature profiles from simulated or clinical data to those obtained by ANN. Results show that it is possible to determine the depth, diameter, and heat generation rate from the surface temperature data (with 5% random noise) with good accuracy for the 2D model. With 10% noise, the accuracy of estimation deteriorates for deep-seated tumors with low heat generation. In order to further develop this methodology for use in a clinical scenario, several aspects such as 3D breast geometry and the effects of nonuniform cooling should be considered in future investigations. read more read less

Topics:

Heat generation (58%)58% related to the paper, Fitness function (50%)50% related to the paper
View PDF
69 Citations
open accessOpen access Journal Article DOI: 10.1155/2017/2857926
Optimal Allocation of DG and DSTATCOM in Radial Distribution System Using Cuckoo Search Optimization Algorithm
T. Yuvaraj1, Kumar Ravi1, K.R. Devabalaji2

Abstract:

This paper proposes a new approach to determine the optimal location and sizing of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) simultaneously in the distribution network. The objective function is formulated to minimize the total power losses of the system subjected to equality and inequality co... This paper proposes a new approach to determine the optimal location and sizing of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) simultaneously in the distribution network. The objective function is formulated to minimize the total power losses of the system subjected to equality and inequality constraints. Loss sensitivity factor (LSF) and Voltage Stability Index (VSI) are used to predetermine the optimal location of DG and DSTATCOM, respectively. Recently developed nature-inspired cuckoo search algorithm (CSA) has been used to determine the optimal size of both DG and DSTATCOM. In the present work, five different cases have been considered during DG and DSTATCOM placement to access the performance of the proposed technique. To check the feasibility, the proposed method is tested on IEEE 12-bus, 34-bus, and 69-bus radial distribution system and the results were compared with other existing techniques. read more read less

Topics:

Cuckoo search (53%)53% related to the paper
View PDF
68 Citations
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13. What is Sherpa RoMEO Archiving Policy for Modelling and Simulation in Engineering?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Modelling and Simulation in Engineering. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Modelling and Simulation in Engineering?

The 5 most common citation types in order of usage for Modelling and Simulation in Engineering are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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