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Articles by Godfrey C. Onwubolu
Total Records ( 3 ) for Godfrey C. Onwubolu
  Godfrey C. Onwubolu
  An article published in this journal by Sahin and Motorcu[1] developed a surface roughness model based on the response surface method, multiplicative-logarithmically linearized approach for determination of the cutting parameters in turning of AISI 1040 carbon steel. Their published results for the surface roughness show that it appears Sahin and Motorcu[1] have obtained wrong constants (C, m, n, p), thereby resulting in incorrect solutions for the surface roughness prediction model. This note works through the solutions to show how Sahin and Motorcu[1] incorrectly handled the published prediction model constants to their solution. The established predictive model shows that the surface roughness increases with the increase of feed rate but decreases with cutting speed and depth of cut.
  Alok Sharma , Kuldip K. Paliwal and Godfrey C. Onwubolu
  This study firstly presents a survey on basic classifiers namely minimum distance classifier (MDC), vector quantization (VQ), principal component analysis (PCA), nearest neighbour (NN) and k-nearest neighbour (kNN). Then vector quantized principal component analysis (VQPCA) which is generally used for representation purposes is considered for performing classification task. Some classifiers achieve high classification accuracy but their data storage requirement and processing time are severely expensive. On the other hand some methods for which storage and processing time are economical do not provide sufficient level of classification accuracy. In both the cases the performance is poor. By considering the limitations involved in the classifiers we have developed linear combined distance (LCD) classifier which is the combination of VQ and VQPCA techniques. The proposed technique is effective and outperforms all the other techniques in terms of getting high classification accuracy at very low data storage requirement and processing time. This would allow an object to be accurately classified as quickly as possible using very low data storage capacity.
  Alok Sharma , Kuldip K. Paliwal and Godfrey C. Onwubolu
  The local Principal Component Analysis (PCA) reduces linearly redundant components that may present in higher dimensional space. It deploys an initial guess technique which can be utilized when the distribution of a given multivariate data is known to the user. The problem in initialization arises when the distribution is not known. This study explores a technique that can be easily integrated in the local PCA design and is efficient even when the given statistical distribution is unknown. The initialization using this proposed splitting technique not only splits and reproduces the mean vector but also the orientation of components in the subspace domain. This would ensure that all clusters are used in the design. The proposed integration with the reconstruction distance local PCA design enables easier data processing and more accurate representation of multivariate data. A comparative approach is undertaken to demonstrate the greater effectiveness of the proposed approach in terms of percentage error.
 
 
 
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