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Articles by S.C. Ng
Total Records ( 3 ) for S.C. Ng
  S.C. Ng , M.N. Sulaiman and M.H. Selamat
  This study discusses the implementation of machine learning approach in negotiation agents that can learn their opponent’s preferences and constraints during one-to-many negotiations. A novel mechanism in learning negotiation is introduced. The genetic-based model of multi-attribute one-to-many negotiation, namely GA Improved-ITA is proposed. The GA Improved-ITA agents first utilize Genetic-Based Machine Learning (GBML) to identify their opponent’s preferable negotiation issues. It is then followed by branch and bound search to search for the best value for each of the issues. The performance of GA Improved-ITA is promising when it is compared with the results of one-to-many negotiations obtained by Bayesian learning model and heuristic search algorithm.
  S.C. Ng , C.K. Wong , T.S. Lee and F.Y. Lee
  The aim of this study was to design and develop an agent-based distance learning management system, namely the Cybernetics 1 (C1) to support accurate decision making, resource allocation and operational control in an effective education management information system. C1 is a web-based open information system to manage the complete course of undergraduate students’ study flow as well as enhancing the policy-making process of education institutions in line with the developing marketplace. This system is illustrated through a multi-stakeholders scenario that captures the operation of the undergraduate students’ study flow problem. The approach of integrating agent technologies to web services enables C1 to become a more flexible, collaborative and efficient distance learning management system. The system is expected to reduce the work period with an average 75% based on the preliminary research study.
  S.C. Ng , N. Ismail , Aidy Ali , Barkawi Sahari and J.M. Yusof
  Ultrasonic NDE has been a well known approach to investigate material’s microstructures, mechanical properties and structure integrity in industry. The internal structure of a material and position of anomalies can be recognised by the reactions of different materials to ultrasound. However, the interpretation of ultrasound signals is difficult in composite material inspection task due to the fact that the ultrasonic pulse is reflected not only by the defect occurred within the material but the microstructures and multiple lay ups of the material. This phenomenon causes the backscattering noise to hinder the real defect’s signal during the inspection. Backscattering noise exists in multiple frequencies. The objective of this study was to develop a new noise reduction method to enhance the defect detectability in coarse-grained structure material such as composites materials. This method increases Signal-to-noise Ratio (SNR) by means of decomposing the original signal into multiresolution representations. To prevent the loss of information, the signal is processed in both temporal and frequency domain. The proposed method has been tested on simulated signal and Glass Fiber Reinforced Plastics (GFRP) laminates. Both simulation and experimental results showed that this method can significantly reduce grain noise while preserving the resolution of the original signal of the defect.
 
 
 
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