Prediction of classroom reverberation time using neural network

In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds

Tahap kemahiran generik pelajar Malaysia dalam proses pengajaran dan pembelajaran: kajian kes pelajar Institut Kemahiran Mara, Johor Baharu

Kajian ini bertujuan untuk mengenal pasti tahap kemahiran generik pelajar di Institut Kemahiran Mara, Johor Baharu dalam proses pengajaran dan pembelajaran. Kajian ini berdasarkan lima elemen kemahiran generik iaitu kemahiran komunikasi, kemahiran kepimpinan, kemahiran kerja berkumpulan, kemahiran menyelesaikan masalah dan kemahiran etika dan profesional. Responden kajian terdiri dari 300 orang pelajar dalam pelbagai jurusan kejuruteraan di IKM, Johor Baharu. Kajian ini dilakukan secara kuantitatif dengan menggunakan borang soal selidik sebagai instrumen kajian. Data kajian dianalisis secara deskriptif dengan menggunakan perisian Statistical Packages for the Social Science (SPSS) untuk mendapatkan nilai min, sisihan piawai dan peratusan. Analisis inferens menggunakan ujian-t adalah untuk melihat perbezaan kemahiran generik terhadap jantina pelajar. Dapatan kajian menunjukkan bahawa tahap kemahiran generik yang dikaji adalah tinggi di mana nilai min keseluruhan bagi kemahiran kerja berkumpulan ialah 4.08, kemahiran komunikasi ialah 4.06, kemahiran kepimpinan ialah 4.05, kemahiran menyelesaikan masalah ialah 4.05 dan kemahiran etika dan profesional ialah 4.03. Manakala ujian-t menunjukkan tidak terdapat perbezaan min yang signifikan bagi kemahiran generik terhadap jantina pelajar. Kajian ini penting kepada pihak Institut Kemahiran Mara dalam mengenal pasti tahap amalan kemahiran generik perlu ditingkatkan lagi dengan mempelbagaikan cara pengajaran dan pembelajaran yang berorientasikan pelajar

Comparison between GLCM and modified Zernike moments for material surfaces identification from photo images

Types of materials are one of an important data for research in acoustic engineering. This paper compares methods for extracting texture data of material surfaces for classification. Gray Level Co-occurrence Matrix (GLCM) and modified Zernike moments that is applied for image extraction are tested and compared with back propagation neural network used for classification. These methods are also applied to the Brodatz texture database as a general comparison. The GLCM method shows a good performance and regression, R>0.9 for the Brodatz database while the collected surfaces datasets using GLCM and modified Zernike moments as well as the Brodatz datasets using modified Zernike moments method had only managed an acceptable performance and regression of R>0.8

PSO-BP algorithm implementation for material surface image identification

Implementation of neural network for acoustic computation is not ne w. In this paper, a new improved method in predicting material surface from photographic image was implemented using a hybrid of particle swarm optimization and back-propagation neural network (PSO-BP) algorithm. Before the system clas sified the data using PSO-BP algorithm, the photographic images of room surfaces need to be extracted using Gray Level Co-occurrence Matrix (GLCM) and Modified Zernike Moments. The result indicated that the PSO-BP algorithm have a hig her accuracy compared to the BP algorithm, managed to record highest accuracy of 88% as opposed to 81.3% for the latter

Development of the system to predict the absorption coefficient of the classroom acoustic using modified Zernike moments and radial basic function (RBF)

The development of a system to predict absorption coefficient using modified Zernike moments and Radial Basic Function (RBF) for classroom is a system based on the system developed using Matlab simulation. This project is designed to predict the absorption coefficient of the surface material and reverberation time in the classroom’s for wall concrete, floors and ceilings. In this project, the modified Zernike moments of image processing is applied to extract image features. RBF is used to identify the absorption coefficient of the material. For the reverberation time, Sabine equation is used to calculate the reverberation time for model dimension and reverberation time of the classroom at UTHM. In addition, Matlab Graphical User Interface (GUI) was developed in order to determine the effectiveness of this system. The proposed system has been trained with 200 samples of surface materials from 10 classrooms in UTHM. The system is tested with 30 samples of surface material images, in which the surface has never been tested using the system created before. The results of predicting the absorption coefficient can predict almost 90% accurately. To sum up, the developed system can predict the absorption coefficient and the reverberation time of classroom at UTHM