scholarly journals Licensing under general demand and cost functions

2016 ◽  
Vol 253 (3) ◽  
pp. 673-680 ◽  
Author(s):  
Debapriya Sen ◽  
Giorgos Stamatopoulos
Keyword(s):  
Cost Functions ◽  
General Demand

Cost Functions and Determinants of Unit Cost Effects in Horizontal Airline M&As

2017 ◽  
Author(s):  
Sveinn Vidar Gudmundsson ◽  
Rico Merkert ◽  
Renato Redondi
Keyword(s):  
Unit Cost ◽  
Cost Functions

Conclusion

2018 ◽  
Author(s):  
Margaret Gilbert
Keyword(s):  
Problem Solving ◽  
Practical Significance ◽  
Joint Commitment ◽  
General Demand

Given the practical significance of demand-rights, it is important to understand how they are possible. This is the general demand-right problem. Solving it turns out to be a challenging task, one that takes us beyond the resources of the central contemporary theories of rights.This book has provided a solution to the general demand-right problem: joint commitment is a ground of demand-rights....

Rail Cost Functions

2021 ◽  
pp. 425-430
Author(s):  
Kristofer Odolinski ◽  
Phill Wheat
Keyword(s):  
Cost Functions

scholarly journals Unsupervised Offline Changepoint Detection Ensembles

2021 ◽  
Vol 11 (9) ◽  
pp. 4280
Author(s):  
Iurii Katser ◽  
Viacheslav Kozitsin ◽  
Victor Lobachev ◽  
Ivan Maksimov
Keyword(s):  
Numerical Experiment ◽  
Failure Time ◽  
Cost Functions ◽  
Technical Diagnostics ◽  
Classification Problems ◽  
Tennessee Eastman Process ◽  
Changepoint Detection ◽  
Aggregation Functions ◽  
The Individual ◽  
Ensemble Algorithms

Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal’s changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach’s novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.

scholarly journals Comparative Study of Different Cost Functions Between Neighbors for Optimizing Energy Dissipation in WSN

2019 ◽  
Vol 13 (1) ◽  
pp. 289-300 ◽  
Author(s):  
Subrata Dutta ◽  
Mohammad S. Obaidat ◽  
Keshav Dahal ◽  
Debasis Giri ◽  
Sarmistha Neogy
Keyword(s):  
Energy Dissipation ◽  
Comparative Study ◽  
Cost Functions

scholarly journals Application of the theory of decision making under risk and uncertainty at modelling of costs

2010 ◽  
Vol 56 (No. 5) ◽  
pp. 201-208 ◽  
Author(s):  
M. Beranová ◽  
D. Martinovičová
Keyword(s):  
Decision Making ◽  
Financial Accounting ◽  
Cost Functions ◽  
Risk And Uncertainty ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
Bayesian Theorem ◽  
Long Run ◽  
Short Run ◽  
The Cost

The costs functions are mentioned mostly in the relation to the Break-even Analysis where they are presented in the linear form. But there exist several different types and forms of cost functions. Fist of all, it is necessary to distinguish between the short-run and long-run cost function that are both very important tools of the managerial decision making even if each one is used on a different level of management. Also several methods of estimation of the cost function's parameters are elaborated in the literature. But all these methods are based on the past data taken from the financial accounting while the financial accounting is not able to separate the fixed and variable costs and it is also strongly adjusted to taxation in the many companies. As a tool of the managerial decision making support, the cost functions should provide a vision to the future where many factors of risk and uncertainty influence economic results. Consequently, these random factors should be considered in the construction of cost functions, especially in the long-run. In order to quantify the influences of these risks and uncertainties, the authors submit the application of the Bayesian Theorem.

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