1. Weibull decision support systems in maintenanceKhalid Aboura, Johnson Agbinya, Ali Eskandarian, 2014, izvirni znanstveni članek Opis: Background: The Weibull distribution is one of the most important lifetime distributions in applied statistics. Weibull analysis is the leading method in the world for fitting and analyzing lifetime data. We discuss one of the earliest decision support system for the assessment of a distribution for the parameters of the Weibull reliability model using expert information. We then present a different approach to assess the parameters distribution.
Objectives: The studies mentioned in this paper aimed to construct a distribution of the parameters of the Weibull reliability model and apply it in the domain of Maintenance Optimization.
Method: The parameters of the Weibull reliability model are considered random variables and a distribution for the parameters is assessed using informed judgment in the form of reliability estimates from vendor information, engineering knowledge or experience in the field.
Results: The results are the development of modern maintenance optimization models that can be embodied in decision support systems.
Conclusion: While the information management part is important in the building of maintenance optimization decision systems, the accuracy of the mathematical and statistical algorithms determines the level of success of the maintenance solution. Ključne besede: Weibull distribution, reliability, inference, maintenance, expert opinion Objavljeno v DKUM: 23.01.2018; Ogledov: 916; Prenosov: 318 Celotno besedilo (527,86 KB) Gradivo ima več datotek! Več... |
2. An overview of image analysis algorithms for license plate recognitionKhalid Aboura, Rami Al-Hmouz, 2017, izvirni znanstveni članek Opis: Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate.
Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates.
Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%.
Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts. Ključne besede: image analysis, probabilistic modeling, signal processing, license plate recognition Objavljeno v DKUM: 28.11.2017; Ogledov: 984; Prenosov: 333 Celotno besedilo (1,01 MB) Gradivo ima več datotek! Več... |
3. A statistical model for shutdowns due to air quality control for a copper production decision support systemKhalid Aboura, 2015, izvirni znanstveni članek Opis: Background: In the mid-1990s, a decision support system for copper production was developed for one of the largest mining companies in Australia. The research was conducted by scientists from the largest Australian research center and involved the use of simulation to analyze options to increase production of a copper production facility.
Objectives: We describe a statistical model for shutdowns due to air quality control and some of the data analysis conducted during the simulation project. We point to the fact that the simulation was a sophisticated exercise that consisted of many modules and the statistical model for shutdowns was essential for valid simulation runs.
Method: The statistical model made use of a full year of data on daily downtimes and used a combination of techniques to generate replications of the data.
Results: The study was conducted with a high level of cooperation between the scientists and the mining company. This contributed to the development of accurate estimates for input into a support system with an EXCEL based interface.
Conclusion: The environmental conditions affected greatly the operations of the production facility. A good statistical model was essential for the successful simulation and the high budget expansion decision that ensued. Ključne besede: decision support system, simulation, statistical modelling Objavljeno v DKUM: 28.11.2017; Ogledov: 811; Prenosov: 314 Celotno besedilo (339,98 KB) Gradivo ima več datotek! Več... |
4. The need for simulation in complex industrial systemsKhalid Aboura, Miroljub Kljajić, Ali Eskandarian, 2012, izvirni znanstveni članek Opis: We discuss the concept of simulation and its application in the resolution of problems in complex industrial systems. Most problems of serious scale, be it an inventory problem, a production and distribution problem, a management of resources or process improvement, all real world problems require a mix of generic, data algorithmic and Ad-hoc solutions making the best of available information. We describe two projects in which analytical solutions were applied or contemplated. The first case study uses linear programming in the optimal allocation of advertising resources by a major internet service provider. The second study, in a series of projects, analyses options for the expansion of the production and distribution network of mining products, as part of a sensitive strategic business review. Using the examples, we make the case for the need of simulation in complex industrial problems where analytical solutions may be attempted but where the size and complexity of the problem forces a Monte Carlo approach. Ključne besede: simulation, linear programming, production process Objavljeno v DKUM: 10.07.2015; Ogledov: 911; Prenosov: 367 Celotno besedilo (448,62 KB) Gradivo ima več datotek! Več... |