Applications are invited for a fully funded 3-year PhD studentship (tuition fee waiver + monthly stipend) in the School of Computer Science, Faculty of Science, University of Nottingham Malaysia Campus, under the supervision of Assistant Professor Dr Chen ZhiYuan.
Intelligent Pipe Crack Detection System by Using the Non-destructive Testing based Mindstorm Robot with Multiple NXT Sensors
• Ministry of Science, Technology and Innovation (MOSTI), Project 01-02-12-SF0360, Govt. of Malaysia
• Faculty of Science, University of Nottingham Malaysia Campus
The detection of cracks in solid infrastructure is a setback of immense significance. Particularly, the detection of cracks in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal and utility operators. The key challenge is that while joints and laterals have an expected form, the unpredictability and abnormality of cracks make them hard to model. The most widespread ways at current is time intense and labour demanding as it entails lagging removal and check using the traditional non-destructive assessment practices such as visual, ultrasonic or radiography; and lagging replacement. This can also necessitate that the tools are shutdown adding up to extra financial burden to plant repairs and function. To examine and continue maintaining these pipes cost so much in terms of cash, manual labour and time, therefore automating the process is needed. This project aims to research design and develop an Intelligent Pipe Crack Detection System by Using the Non-destructive Testing based Mindstorm Robot with Multiple NXT Sensors. Another important outcome from this project is to determine the suitability of non-destructive testing (NDT) methods with multiple NXT sensors of the Mindstorm Robot. The NDT technique and applicability criteria will provide the potential of a widespread use in different industries with a longer service life and lower leakage rates. The development of defect recognition and automatic crack detection expert system with support vector machine (SVM) based classifier and the recommendation of appropriate solutions for pipe repair with the case based reasoning (CBR) technology will result in less shut-down time and reduce risk of serious accidents and pollution.
Normally graduate of this University or any other approved higher education institution holding:
i). A Master's degree accepted by the Academic Board;
ii). Other relevant or equivalent qualification with Master's degree accepted by the Senate or competent authority in Malaysia.
English Lanquaqe Requirements:
IELTS:6.O (no elements below 5.5)
TOEFL (IBT): 79 (no element below 19)
PTE (Academic): 55 (minimum 51)
1119 (GCE 0): Grade C
GCSE/IGCSE: Grade C
CAE: 169 (no element below 162) or C
Applicants with an MSc/MEng degree in Computer Science, Mathematics, Electrical and Electronic Engineering, or any other related area are welcome to apply. Previous experience in Machine Learning, Pattern Recognition, Robotics Technologies, Expert System, Sensor Signal Processing Non-destructive Testing and Pipe Crack Detection will be an advantage but not a must. The successful candidate is expected to take up the scholarship in September 2016 or no later than October 2016.
For further enquiry on the project and the application procedure, please send your CV together with a cover letter to explain your career aim and why you want to apply, to:
Assistant Professor Dr Chen ZhiYuan
School of Computer Science,
Faculty of Science, The University of Nottingham Malaysia Campus
Selangor Darul Ehsan
Tel: +6 (03) 8924 8141
Posted on 11th August 2016