Abstract

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Vehicle acoustic comfort is one of the ergonomic measurement criteria that are essential for car occupants. furthermore, interior cabin noise of a car may affect the driver’s concentration when driving. this study is to investigate the noise comfort level of car interior on several compact cars. the objective is to measure interior cabin noise for all three cars and then to compare their acoustic comfort level using subfield data mining approach. a deduction will be made to rate the best car among the three in term of acoustic comfort. the interior cabin noise will be obtained for the cases where engine speed is varied while the cars are in stationary and moving condition. the noise will be assessed according to pre-determined subjective and objective criteria. the sound quality parameters will be assessed by regression analysis. in subjective assessment, the recorded noise is evaluated based on jury assessment. then, the data mining approach is implemented to illustrate the noise level. the collected noise data are divided into five clusters through hierarchical clustering method. to assess the accuracy of noise data clusters, the method of k-nearest neighbours is performed and the results show a high accuracy rate (> 95%). finally, the interior noise of the three cars used is compared by using the analysis of variation. the vehicle acoustic comfort index is produced for the three cars tested in this study. in addition, the acoustic quality among the three cars is presented using anova. annoyance index of the three cars is generated using data mining method. from the results, axia has the best acoustic comfort among of the three cars by objective evaluation. by subjective evaluation, axia recorded the lowest level of annoyance.

As a developing country, Malaysia has shown great interest and effort to commit and invest for green technology and green energy especially tidal energy resources. This research project is conducted to carry out an assessment study to analyse and compare several locations in Malaysia while using the optimal analytical approach in finding the best sites to harness tidal stream resource around Malaysia. Previous studies that only considered flow velocity at the deployment sites may not be accurate since geometry of the coastal region and the friction force exerted from the tidal flow with the seabed can also contribute to the suitability of the proposed tidal sites. Tidal Model Driver was used to extract tidal elevation data on specific harmonic constituents and tidal velocity attribute, and then compared to the data obtained from General Bathymetric Chart of the Oceans. The weighted decision matrix was used to analyse the potential of each location by considering essential criteria such as tidal velocity, depth of coastline, geometry of coastline and environmental impacts. Top locations with the highest score were chosen as potential location to invest in a tidal energy farm, which consists of Pulau Pangkor, Port Dickson, and Pulau Tioman among others. The locations that produce the highest yearly energy density are found to be Pulau Pangkor with 1.21 MWh/year followed by Pulau Pinang with 836.5 kWh/year. Notably, the inclusion of other factors besides tidal velocity such as the site characteristic and the amount of potential impact to the environment provided different outcome than published materials in this field.