S-Curve Model

2016 ◽  
pp. 370-385
Keyword(s):  
Curve Model ◽  
S Curve

S-curve Model of Relationship Between Energy Consumption and Economic Development

2014 ◽  
Vol 24 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Anjian Wang ◽  
Gaoshang Wang ◽  
Qishen Chen ◽  
Wenjia Yu ◽  
Kun Yan ◽  
...  
Keyword(s):  
Economic Development ◽  
Energy Consumption ◽  
Curve Model ◽  
S Curve

Uncertain Technology Evolution and Decision Making in Design

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Jonathan L. Arendt ◽  
Daniel A. McAdams ◽  
Richard J. Malak
Keyword(s):  
Research And Development ◽  
Pareto Frontier ◽  
Evolutionary Process ◽  
Evolutionary Model ◽  
Development Effort ◽  
Technology Evolution ◽  
Curve Model ◽  
S Curve ◽  
Over Time

The potential for engineering technology to evolve over time can be a critical consideration in design decisions that involve long-term commitments. Investments in manufacturing equipment, contractual relationships, and other factors can make it difficult for engineering firms to backtrack once they have chosen one technology over others. Although engineering technologies tend to improve in performance over time, competing technologies can evolve at different rates and details about how a technology might evolve are generally uncertain. In this article we present a general framework for modeling and making decisions about evolving technologies under uncertainty. In this research, the evolution of technology performance is modeled as an S-curve; the performance evolves slowly at first, quickly during heavy research and development effort, and slowly again as the performance approaches its limits. We extend the existing single-attribute S-curve model to the case of technologies with multiple performance attributes. By combining an S-curve evolutionary model for each attribute with a Pareto frontier representation of the optimal implementations of a technology at a particular point in time, we can project how the Pareto frontier will move over time as a technology evolves. Designer uncertainty about the precise shape of the S-curve model is considered through a Monte Carlo simulation of the evolutionary process. To demonstrate how designers can apply the framework, we consider the scenario of a green power generation company deciding between competing wind turbine technologies. Wind turbines, like many other technologies, are currently evolving as research and development efforts improve performance. The engineering example demonstrates how the multi-attribute technology evolution modeling technique provides designers with greater insight into critical uncertainties present in long-term decision problems.

An Updated Trend Analysis Representing the Outbreak of Novel Coronavirus (2019-nCoV) in 16 Cities of Hubei Province, China Using Logistic S-Curve Model

2020 ◽  
Author(s):  
Muhammad Fawad ◽  
Sumaira Mubarik ◽  
Saima Shakil Malik ◽  
Yangyang Hao ◽  
Chuanhua Yu ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Hubei Province ◽  
Curve Model ◽  
S Curve ◽  
Novel Coronavirus

Product Performance Evolution Prediction by Lotka-Volterra Equations

2017 ◽  
Author(s):  
Guanglu Zhang ◽  
Daniel A. McAdams ◽  
Milad Mohammadi Darani ◽  
Venkatesh Shankar
Keyword(s):  
Time History ◽  
Evaluation Process ◽  
Development Planning ◽  
Performance Data ◽  
Product Performance ◽  
Volterra Equations ◽  
Performance Impact ◽  
Frame Design ◽  
Curve Model ◽  
S Curve

During the development planning of a new product, designers rely on the prediction of the product performance to make business investments and frame design strategy. The S-curve model is commonly used for this purpose, but it has several drawbacks. A significant volume of product performance data doesn’t fit well with an S-curve. An S-curve model is also not capable of showing what factors shape the future performance of a product and how designers can change it. In this paper, Lotka-Volterra equations, which subsume both the logistic S-curve model and Moore’s Law, are used to describe the interaction between a product (system technology) and the components and elements (component technologies) that are combined to form the product. The equations are simplified by a relationship table and a maturation evaluation process as a two-step simplification. The historical performance data of the system and its components are fitted by the simplified Lotka-Volterra equations. The methods developed here allow designers to predict the performances of a product and its components quantitatively by the simplified Lotka-Volterra equations. The methods also shed light on the extent of performance impact from a specific module on a product, which is valuable for identifying the key features of a product and thus making outsourcing decisions. Smart phones are used as an example to demonstrate the two-step simplification. The data fitting method is validated by the time history performance data of airliners and turbofan aero-engines.

Used Car Price Estimation: Moving from Linear Regression towards a New S-Curve Model

2021 ◽  
Vol 22 (3) ◽  
pp. 1174-1187
Author(s):  
Fadzilah Salim ◽  
Nur Azman Abu
Keyword(s):  
Linear Regression ◽  
Linear Model ◽  
Real Life ◽  
Real Data ◽  
Prediction Errors ◽  
Curve Model ◽  
Non Linear ◽  
Used Car ◽  
S Curve ◽  
Used Cars

A simple linear regression is commonly used as a practical predictive model on a used car price. It is a useful model which carry smaller prediction errors around its central mean. Practically, real data will hardly produce a linear relationship. A non-linear model has been observed to better forecast any price appreciation and manage prediction errors in real-life phenomena. In this paper, an S-curve model shall be proposed as an alternative non-linear model in estimating the price of used cars. A dynamic S-shaped Membership Function (SMF) is used as a basis to build an S-curve pricing model in this research study. Real used car price data has been collected from a popular website. Comparisons against linear regression and cubic regression are made. An S-curve model has produced smaller error than linear regression while its residual is closer to a cubic regression. Overall, an S-curve model is anticipated to provide a better and more practical estimate on used car prices in Malaysia.

S-Curve Model-Based Adaptive Algorithm for Nonuniformity Correction in Infrared Focal Plane Arrays

2009 ◽  
Vol 29 (4) ◽  
pp. 927-931 ◽  
Author(s):  
赖睿 Lai Rui ◽  
杨银堂 Yang Yintang ◽  
王炳健 Wang Bingjian ◽  
周慧鑫 Zhou Huixin ◽  
刘上乾 Liu Shangqian
Keyword(s):  
Adaptive Algorithm ◽  
Focal Plane ◽  
Focal Plane Arrays ◽  
Nonuniformity Correction ◽  
Infrared Focal Plane Arrays ◽  
Model Based ◽  
Curve Model ◽  
S Curve

scholarly journals Tax evasion in the EU countries following a predictive analysis and a forecast model for Slovakia

2021 ◽  
Vol 12 (3) ◽  
pp. 701-728
Author(s):  
Ján Dobrovič ◽  
Rastislav Rajnoha ◽  
Petr Šuleř
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Tax Evasion ◽  
International System ◽  
Value Added ◽  
Percentage Error ◽  
Eu Countries ◽  
Curve Model ◽  
S Curve ◽  
The Eu

Research background: Tax evasion is an urgent challenge for governments, as reaching sufficient level of tax revenues enable adequate sustainable economic development. The motivation for the research was thus the identification of the situation in the EU countries.  Purpose of the article: The main research objective was to identify the extent of tax evasion in the EU countries, with a subsequent specific focus on the econometric predictive models and a forecast of their future development in the case of Slovakia as the poorest performing country of the V4 in this area.  Methods: The research was primarily based on testing selected statistical indicators in the field of tax evasions expressed on the basis of the VAT gap. The data for the research was obtained from the EUROSTAT database and the international system VIES for the period between 2000 and 2017. In addition to panel graphs, the research hypotheses were tested primarily using a cluster analysis, t-test, time series analysis, and an analysis of the time series trend with 4 basic models: linear trend, quadratic trend, growth curve model, and S-curve model. On the basis of the Mean Absolute Percentage Error (MAPE), the S-Curve model was selected as the determining model of predicting tax evasion.  Findings & value added: Based on the results of the cluster analysis, the EU countries were divided into five reference groups by the VAT gap value, using the VAT gap percentage share on the overall GDP value. The research also provides a unique methodological framework and a unique econometric model for predicting the future VAT gap in Slovakia as the poorest performing country of the V4 in this area, which is applicable to other V4 and EU countries. The research results also enable policy-makers in the EU countries and specifically also in Slovakia and other V4 countries to compare themselves explicitly with the reference countries of the EU in terms of tax evasion and subsequently adopt adequate measures to improve the effectiveness and performance in this field.

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