TAY YEE YANG

An industrial talk had been presented by Ir. Juraimi Masmood on 17th April 2018 from 2.15 p.m. to 4.30 p.m. in L50 DK1. He has electrical engineering background and a huge experience as an engineer. He had shared his working experiences in PTP (Pelabuhan Tanjung Pelepas) to all the FKE final year students in this seminar. He has grown his career from a trainer to an engineer and finally become General Manager for Project Integration now in PTP. Some pictures are shown to us to let us understanding the history of development of PTP.

         Ir. Juraimi Masmood worked at KTA Tenaga and carried out his first heavy engineering project which was KLCC Air Conditional Plant Project before working in PTP. The important tasks of handling a heavy engineering project are designing solution, project planning, project cost estimating, product quality, construction process and safety. After that, worked as a resident engineer whom needed to deal with clients, college and contractors. He mentioned that the communication skill is very important because dealing with others is an important task in engineer's life. He had talked about becoming an engineer with clients should has skills to manage development plan and stakeholders' expectations . For an example, the job scope are determining end user requirement, engaging communities and authorities, engaging consultants, engaging contractors/suppliers, development of master plan as well as confirming timely, high quality and safe delivery. 

        One of points I have recorded down is employer's expectation to the employees. Employer's expectations were listed with industry rating as below:

        Before ending the seminar, he has reminded us that ethic is moral as well as engineering ethics is a field of applied ethics and system of moral principle which apply to practice of engineering. The standard used in engineering ethics are IEEE and NSPE ( National Society of Professional Engineering). There still a long journey in our life to become a professional engineer in future. 

Personal Particular

Name              : TAY YEE YANG
Email              : ahken05@gmail.com
Contact           : +60177353084
Race                : Chinese
I/C no             : 941105-01-6303
Nationality     : MALAYSIAN

Educational Background

Universiti Teknologi Malaysia

• Bachelor of Engineering (Electrical–Mechatronic)
  • Final year student with current CGPA, 3.83

SMK Tinggi Kluang                                                       

• Sijil Tinggi Persekolahan Malaysia (STPM): 82

SMK Tengku Aris Bendahara

• Sijil Pelajaran Malaysia (SPM): 3A+,1A,1A-,2B+,2B,1C+

Achievements

• Champion in ASIA-PACIFIC ROBOT CONTEST (ABU ROBOCON) 2016
• Best Design Award in ASIA-PACIFIC ROBOT CONTEST (ABU ROBOCON) 2015
• International Invention and Innovative Competition (InIIC 1/2017) [Silver medal]
• 2017 CREST- Industry Data Analytical Challenge [Silver award]
• UTM CSI Innovation [Gold medal]
• The Great Lab Grand Design Challenge 2017 [Champion]
• IOT Hack UTM 2017 [Champion]
• 2017 Medical Devices Cluster Bootcamp [Gold award]
• UNESCO UTM [Champion in World Hunger Speak-out challenge]
• SAS track of Innovate Malaysia Design Competition 2018 (Central Region) [Winner award]

Relevant Skill and Knowledge

SOFTWARE             SPSS Statistics Software, Advanced Analytical Software, Statistical Analysis System (SAS), VISUAL STUDIO, MATLAB, MULTISIM, PSPICE, QUARTUS, ARDUINO, COCOOX IDE, 3D CAD SOLIDWORK, ANDROID STUDIO, DESIGN 2020, GOOGLE SKETCHUP

PROGRAMING       C, Perl, Python, HTML, C++, R, CSS, SQL

HARDWARE           PIC MICROCHIP FAMILY, ARDUINO, PIXY CAMERA, ATMEGA 32, ANDROID APPS IN PHONE, STM 32 DISCOVERY, WEBCAM WITH COMPUTER VISION, DMP6050, GALILEO BOARD

LANGUAGE COMPETENCY
WRITING: ENGLISH, CHINESE, MALAY
SPEAKING: ENGLISH, MANDARIN, MALAY

Relevant Experience

• SPEAKER IN ANDROID WORKSHOP OPEN FOR UNIVERSITI STUDENTS (2016)
• SPEAKER IN ROBOCON RECRUITMENT GAME WORKSHOP (2015)
• MENTOR IN ROBOCON RECRUITMENT GAME AUTOCAR (2016)
• PROGRAMMER OF IMAGE PROCESSING IN ROBOCON 2015
• DEVELOPER OF COMMUNICATION INTERFACE BETWEEN DIFFERENT EMBEDDED BOARD
• DEVELOPER OF ROBOT PATH PLAN PROGRAM
• SOFTWARE PROGRAMMER FOR LIFT SIMULATOR

Working Experience

• INTEL - Internship for 12 weeks

Volunteer Experience and Leadership

• School Reporter Under SIN CHEW Daily
• President of Taekwondo Club
• President of Basketball Club
• Vice President of Chinese Language Institute
• Platoon Leader of Persatuan Bulan Sabit Merah Malaysia
• Program Latihan Khidmat Negara (PLKN)

Self-Introduction

• Initiative and responsible
• Active in teamworking
• Creative and prone to brainstorming
• Enjoy challenging task
• Passionate about all kind of knowledge

Reference

Academic Advisor

Name: Prof. Zamani Bin Md. Zain
E-mail: zamanizain@utm.my
Contact number: +(607)-5557018
Office: P19a (01-01-07)

The UTM FKE student come late to the class has been an issue that troubling every lecturer as it might slow down the class progress and influence the image of university. The core reason of student come late to class is studied in this assignment in order to take a right action on solving this issue.

Methodology

1. The data were collected from 47 FKE student through survey form and undergo transformation in Excel.
2. Chi-square test analysis was conducted to examine the association to find out the core factor of student come late to class. Pearson Chi-square test was used to examine the association of nominal data.  Mantel-Haenszel chi-square was used to examine the association of ordinal data.
3. Cramer's V Statistic for Pearson Chi-square test and Spearman correlation for Mantel-Haenszel chi-square are used to measure the strength of association.
4. Odd ratio analysis was also used to abstract some information from the survey data.
5. All the analysis was run in SAS advance analytical software.

Result 

After the analysis, time management skill of the student is proven as the most significance factor for student to come late to class. Actions should be taken to enhance the time management skill of the student such as organizing campaign, enforce punishment, or time management aided technology solution can be created. 

The process of maintenance becomes more and more important nowadays when the number of installed industrial robots or machines rise exponentially. The traditional manual maintenance is insufficient to support the advancement of robotic technology and the latest machinery complexity design. Therefore, this project is carried out to develop a self-supervised maintenance system for fault detection and identification by using IOT solution with machine learning model. The overall target of the project is to design a product solution to increase the efficiency of maintenance system.

A Raspberry Pi model 3 is used to construct the Internet of Things (IoT) device to collect the machinery data were and send to the SAS server in every second.

 
Figure 1.0: Technology outlook of the on-cloud analytical system.

Data collected undergone data analysis using Statistical Analysis Software (SAS) and all the procedures are written in SAS language.

 
Figure 1.1: Log file of REST API based on HTTP.

A trained machine learning model was deployed in the system to find out the pattern in different condition to identify the machine fault condition. This system was tested on a simulated model of an industrial robot, Automated Guided Vehicle (AGV) to identify the performance of the device designed and check the functionality of the system.

 
Figure 1.2: Zalpha Series AGV and Simulated Robot Model.

The real-time data and the analysis outcome were visualized in web-based form and APP form on any mobile device. There are some alert notification technique also involved to let the data analysis result to interface with industry personel.

Figure 1.3: Real-time data visualization in web-based form.

 
Figure 1.4: Visualization of machine learning model output in APP form.

This project also was used to participated SAS track Innovate Malaysia Design Challenge and was awarded.

Figure 1.5: Scene during competition.

 
Figure 1.6: The winner of SAS track IMDC.