Güray, Cenk

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Profile URL
https://hdl.handle.net/20.500.14411/7846
Name Variants
Guray, C
Güray,C.
Guray, Cenk
G.,Cenk
Guray,C.
C.,Güray
Cenk, Guray
G., Cenk
C.,Guray
C., Guray
Cenk, Güray
Güray, Cenk
Job Title
Doktor Öğretim Üyesi
Email Address
Main Affiliation
Department of Metallurgical and Materials Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

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SDG data is not available
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Scholarly Output

14

Articles

9

Views / Downloads

22/40

Supervised MSc Theses

0

Supervised PhD Theses

3

WoS Citation Count

26

Scopus Citation Count

70

Patents

0

Projects

0

WoS Citations per Publication

1.86

Scopus Citations per Publication

5.00

Open Access Source

6

Supervised Theses

3

JournalCount
Türk Kültürü ve Hacı Bektaş Veli Araştırma Dergisi3
Turk Kulturu ve Haci Bektas Veli - Arastirma Dergisi2
Journal Europeen des Systemes Automatises1
Expert Systems with Applications1
Tehnicki vjesnik - Technical Gazette1
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2003 - 20083
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End date: 2009Start date: 2003

Scholarly Output Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Ore-Age: an Intelligent Assisting and Tutoring System for Mining Method Selection
    (2003) Guray,C.; Celebi,N.; Atalay,V.; Gunhan,A.
    In the past studies about the mining method selection process, which is among the most critical aspects in the mining engineering discipline, there are attempts to build up a systematic approach to make this selection. But, these approaches work based on static databases and fail in inserting the intuitive feelings and engineering judgments of experienced engineers to the selection process. In this study, a hybrid system based on 13 different expert systems and one interface agent is developed, to make mining method selection for the given ore-bodies. The learning procedure to insert the expertise of the experienced engineers to the selection process, works based on a neuro-fuzzy model, combining the TSK model of the fuzzy theory and a two layered neural network with the utilization of the back-propagation algorithm. Again, to supply the maximum assistance to the users, the agent executes the system's tutoring procedure in case an inexperienced user enters the system, to complete his/her missing knowledge about mining method selection. The system that is being developed in this study can be introduced as the first example of dynamic, intelligent assisting and tutoring systems in the mining profession.
  • Thumbnail Image
    Article
    Ore-Age: an Intelligent Tutoring System Model for Mining Method Selection
    (The International Journal of Mineral Resources Engineering, 2008) Güray, Cenk
    Mining method selection is a critical decision for an economic, safe productive mining work. Each orebody is unique with its own properties and engineering judgment has a great effect on the decisions. In this study an intelligent assisting and tutoring system for preliminary underground mining method selection is developed. This systems is called Ore-Age, whose goal is making the preliminary mining method selection as effeciently as posssible too while giving them a remarkable education on mining method selection. Due to its semi-autonomous character, Ore-Age determşnes to direct its execution strategy based on the expertise levels od the users. Ore-Age acts as an assisting tool for the experienced engineers during the selection process and continuously looks for the help of its neuro-fuzzy learning algorithm. The reason of Ore-Age to take this learning procedure is to imitate and behave like these experts during his future selections. Besides this ability, Ore-Age tries to act as a tutoring system as efficiently as possible when he faces inexperienced engineers. The regular strategy of teaching process depends on an iterative algorithm checking the decisive concepts one by one to find the point of misconception leading to wrong selection. Furthermore as an alternative strategy, by his error modeling property, Ore-Age can alter hhis strategy and concentrate the users directly to the possible points of misconception, without using the previous "time demanding" algorithm. This system aiming the model thecognitive behaviour of the student is an indication of the reactive characteristic of the system that can alter the strategies based on his own logical decison to achieve a more efficient tutoring procedure. The system that is being developed in this study can be introduced as the first example of dynamic, intelligent assisting and tutoring systems in the mining profession.
  • Thumbnail Image
    Article
    Citation - WoS: 11
    Citation - Scopus: 22
    Ore-Age: a Hybrid System for Assisting and Teaching Mining Method Selection
    (Pergamon-elsevier Science Ltd, 2003) Guray, C; Celebi, N; Atalay, V; Pasamehmetoglu, AG
    Mining method selection is among the most critical and problematic points in mining engineering profession. Choosing a suitable method for a given ore-body is very important for the economics, safety and the productivity of the mining work. In the past studies there are attempts to build up a systematic approach to help the engineers to make this selection. But, these approaches work based on static databases and fail in inserting the intuitive feelings and engineering judgments of experienced engineers to the selection process. In this study, a system based on 13 different expert systems and one interface agent is developed, to make mining method selection for the given ore-bodies. The agent Ore-Age, to follow his goal of supplying the maximum assistance to engineers in selecting the most suitable method for a specific ore-body, tries to learn the experiences of the experts he has faced. After this learning process the knowledge base is evolved to include these experiences, making the system more efficient and intuitive in mining method selection work. To realize the above goal, the system's tutoring procedure is executed by the agent, in case an inexperienced user enters the system, to complete his/her missing knowledge about mining method selection. (C) 2002 Elsevier Science Ltd. All rights reserved.