scholarly journals Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 254
Author(s):  
Marwa Belhaj Salem ◽  
Mitra Fouladirad ◽  
Estelle Deloux
Keyword(s):  
Mechanical System ◽  
Dynamic Environment ◽  
Machine Learning Algorithms ◽  
Maintenance Policy ◽  
Degradation Behaviour ◽  
Pump System ◽  
Variance Gamma ◽  
Variance Gamma Process ◽  
Operating Environments ◽  
Finite State

Recently, maintaining a complex mechanical system at the appropriate times is considered a significant task for reliability engineers and researchers. Moreover, the development of advanced mechanical systems and the dynamics of the operating environments raises the complexity of a system’s degradation behaviour. In this aspect, an efficient maintenance policy is of great importance, and a better modelling of the operating system’s degradation is essential. In this study, the non-monotonic degradation of a centrifugal pump system operating in the dynamic environment is considered and modelled using variance gamma stochastic process. The covariates are introduced to present the dynamic environmental effects and are modelled using a finite state Markov chain. The degradation of the system in the presence of covariates is modelled and prognostic results are analysed. Two machine learning algorithms k-nearest-neighbour (KNN) and neural network (NN) are applied to identify the various characteristics of degradation and the environmental conditions. A predefined degradation threshold is assigned and used to propose a prognostic result for each classification state. It was observed that this methodology shows promising prognostic results.

scholarly journals Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets

2020 ◽  
pp. 458-467
Author(s):  
Virendra Tiwari ◽  
Balendra Garg ◽  
Uday Prakash Sharma
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Dynamic Environment ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Learning Mechanisms

The machine learning algorithms are capable of managing multi-dimensional data under the dynamic environment. Despite its so many vital features, there are some challenges to overcome. The machine learning algorithms still requires some additional mechanisms or procedures for predicting a large number of new classes with managing privacy. The deficiencies show the reliable use of a machine learning algorithm relies on human experts because raw data may complicate the learning process which may generate inaccurate results. So the interpretation of outcomes with expertise in machine learning mechanisms is a significant challenge in the machine learning algorithm. The machine learning technique suffers from the issue of high dimensionality, adaptability, distributed computing, scalability, the streaming data, and the duplicity. The main issue of the machine learning algorithm is found its vulnerability to manage errors. Furthermore, machine learning techniques are also found to lack variability. This paper studies how can be reduced the computational complexity of machine learning algorithms by finding how to make predictions using an improved algorithm.

Avatars-Based Decision Support System Using Blockchain and Knowledge Sharing for Processes Simulation

2021 ◽  
Vol 17 (1) ◽  
pp. 72-92
Author(s):  
Vardan Mkrttchian
Keyword(s):  
Human Capital ◽  
Decision Support ◽  
Knowledge Sharing ◽  
Process Simulation ◽  
Efficient Algorithm ◽  
Capital Management ◽  
Human Capital Management ◽  
Variance Gamma ◽  
R Language ◽  
Variance Gamma Process

This article is an enhancement of the chapter “About Digital Avatars for Control in Virtual Industries” in the book Big Data and Knowledge Sharing in Virtual Organizations. The article discusses the capabilities of the R language for modeling Levy processes that currently most closely correspond to the nature of the organizational learning movements in sliding mode. The efficient algorithm of the CGMY process simulation as a difference of the tempered stable independent Levy is processed and programmed at R language. The efficient algorithm of variance gamma process simulation using variance gamma random variables is processed and programmed at R language. Overview of CGMY process simulation in practice is use for human capital management in the context of the implementation of digital intelligent decision support systems and knowledge management and for digital intelligent design of avatar-based control with application to human capital management.

scholarly journals Spatial Patterns in Different Stages of Regeneration after Clear-Cutting of a Black Locust Forest in Central China

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1066
Author(s):  
Zhang ◽  
Shen ◽  
Yang ◽  
Ma ◽  
Duan ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Stand Structure ◽  
Robinia Pseudoacacia ◽  
Spatial Relation ◽  
Dispersal Limitation ◽  
Central China ◽  
Variance Gamma ◽  
Clear Cutting ◽  
Variance Gamma Process ◽  
Underlying Mechanisms

Estimating underlying mechanisms and dynamics from observed tree patterns can provide guidance for plantation management. Robinia pseudoacacia can reproduce via clonally produced ramets, leading to a complex distribution of stems. Three second generation plots and three third generation plots (each plot 50 m × 50 m) were established across a wide age range after clear-cutting in a Robinia pseudoacacia plantation in central China. We measured spatial coordinates, diameter at breast height (DBH) or diameter at basal stem, and heights of all recruits, as well as the coordinates and base diameter of all stumps, in six plots. The spatial pattern in different plots and the spatial relation between stumps and regenerations after clear-cutting were analyzed. To estimate the underlying processes of the observed patterns, we fitted Matérn and Variance-Gamma cluster processes to the observed dataset. The results revealed that the percentage of ramets from stumps decreasing with age in the two types of stands (from 40.4% to 30.1%, from 57.6% to 35.7%), and trees exhibited an aggregated distribution in all plots, but the degree of aggregation exhibited a decreasing trend with age, and aggregation occurred at different scale. Furthermore, a large proportion of ramets had their nearest neighbor at a short distance (<1 m) based on analysis of the nearest neighbour function. The bivariate analysis revealed that the spatial relation between stumps and ramets changed with age, and a repulsion trend was found between them in all the six plots. The Variance-Gamma process with covariate of Cartesian coordinates fitted the observed patterns better than others. The observed pattern was likely driven by root dispersal limitation, seed dispersal limitation, human disturbance, and intraspecific competition. Spatial patterns are important characteristics in forest stand structure, and understanding the pattern change and its underlying mechanisms could allow for better timing of artificial disturbances to optimize stand structure and promote stand growth.

scholarly journals Valuation of European and American Options under Variance Gamma Process

2014 ◽  
Vol 02 (11) ◽  
pp. 1000-1008 ◽  
Author(s):  
Ferry Jaya Permana ◽  
Dharma Lesmono ◽  
Erwinna Chendra
Keyword(s):  
American Options ◽  
Gamma Process ◽  
Variance Gamma ◽  
Variance Gamma Process

Asymptotic failure rate of a Markov deteriorating system with preventive maintenance

2003 ◽  
Vol 40 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Sophie Mercier ◽  
Michel Roussignol
Keyword(s):  
Failure Rate ◽  
Preventive Maintenance ◽  
Sufficient Conditions ◽  
Maintenance Policy ◽  
System A ◽  
Up States ◽  
Finite State ◽  
Degradation Degree ◽  
Preventive Maintenance Policy ◽  
Finite State Space

We consider a system with a finite state space subject to continuous-time Markovian deterioration while running that leads to failure. Failures are instantaneously detected. This system is submitted to sequential checking and preventive maintenance: up states are divided into ‘good’ and ‘degraded’ ones and the system is sequentially checked through perfect and instantaneous inspections until it is found in a degraded up state and stopped to allow maintenance (or until it fails). Time between inspections is random and is chosen at each inspection according to the current degradation degree of the system. Markov renewal equations fulfilled by the reliability of the maintained system are given and an exponential equivalent is derived for the reliability. We prove the existence of an asymptotic failure rate for the maintained system, which we are able to compute. Sufficient conditions are given for the preventive maintenance policy to improve the reliability and the asymptotic failure rate of the system. A numerical example illustrates our study.

VARIANCE GAMMA PROCESS WITH MONTE CARLO SIMULATION AND CLOSED FORM APPROACH FOR EUROPEAN CALL OPTION PRICE DETERMINATION

2021 ◽  
Vol 14 (2) ◽  
pp. 183-193
Author(s):  
Abdul Hoyyi ◽  
Abdurakhman Abdurakhman ◽  
Dedi Rosadi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Closed Form ◽  
Stock Price ◽  
Option Price ◽  
Secondary Data ◽  
Exponential Model ◽  
Variance Gamma ◽  
Variance Gamma Process ◽  
Normally Distributed

The Option is widely applied in the financial sector.  The Black-Scholes-Merton model is often used in calculating option prices on a stock price movement. The model uses geometric Brownian motion which assumes that the data is normally distributed. However, in reality, stock price movements can cause sharp spikes in data, resulting in nonnormal data distribution. So we need a stock price model that is not normally distributed. One of the fastest growing stock price models today is the  process exponential model. The  process has the ability to model data that has excess kurtosis and a longer tail (heavy tail) compared to the normal distribution. One of the members of the  process is the Variance Gamma (VG) process. The VG process has three parameters which each of them, to control volatility, kurtosis and skewness. In this research, the secondary data samples of options and stocks of two companies were used, namely zoom video communications, Inc. (ZM) and Nokia Corporation (NOK).  The price of call options is determined by using closed form equations and Monte Carlo simulation. The Simulation was carried out for various  values until convergent result was obtained.

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