MARC details
| 000 -LEADER |
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| fixed length control field |
03772ntm a2200361 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000104397 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113438.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180723s2018 my a f a m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0008052(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201906181321 |
| Level of effort used to assign nonsubject heading access points |
amy2 |
| Level of effort used to assign subject headings |
201808131534 |
| Level of effort used to assign classification |
fateeha |
| -- |
201807231427 |
| -- |
fateeha |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKEE .S84 2018 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Suhaimi Mohd Daud, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Classification of engine lubricant oil degradation using electronic nose / |
| Statement of responsibility, etc. |
Suhaimi Mohd Daud |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 364 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD ROM |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
| Source |
rdamedia |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Source |
rdacarrier |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
computer disc |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Electrical and Electronics Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2018 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Lubricating oil is one of the products of petroleum refinery and it is one of the largest areas of research in the automotive sector. It is very useful in maintaining optimum engine performance in order to protect the engine from the effects of heat, pressure, corrosion, oxidation, contaminants, and cooling of an engine caused by two moving parts that touch each other. The aim this study is to classify the lubricant oil sample based on its odor using electronic nose as the main instrument and classify the lubricant oil odor-profile using case-based Reasoning and K-Nearest Neighbor (K-NN) classifier. There are several methods used to study and analyze the lubricant oil such as ICP-MS, Spectrophotometer and e-nose. E-nose is one of the approaches in analyzing lubricating oils by using odor changes as parameters. Through the smell, lubricating oils can be analyzed based on the changes in odor caused by changes in the content and effects of various factors for each lubricating oil sample. In this study, e-nose has been used to measure and collect odor raw data for the lubricant oil sample comprising 11 oil samples which are 1 sample from virgin oil and 10 samples from oil that have been used at 1000KM up to 10000KM of the engine. The raw data collected will be through a signal-processing phase with the aim of extracting the odor-profile for each lubricating oil sample. This odor-profile is a very useful pattern for the process of classification using Case-based Reasoning (CBR) and K-Nearest Neighbor (K-NN). Furthermore, this odor-profile is the combination of several unique features that produce a very significant odor pattern for each sample. Hence, the meaningful odor patterns that represent the odor-profile of each oil sample is the factor that makes the process of classification become very good and significant. Next, these meaningful odor-profiles of each sample were classified using the CBR and K-NN classification technique in order to differentiate between a sample and another based on the odor that produced by the each sample. Both of these techniques are very popular and widely used in the field of classification. The results of this study show that the classification odor profile for each sample lubricant has successfully achieved 100% classification using CBR classifier. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Electrical and Electronics Engineering |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Disertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |