Pemodelan lengkung splin-B berasaskan algoritma inspirasi tabii

Pemodelan lengkung adalah aspek utama dalam bidang pemodelan geometri. Pemodelan lengkung konvensional memberi ralat yang besar kerana bilangan titik data lebih banyak daripada bilangan bucu poligon kawalan bagi model lengkung yang dijana. Kajian ini memfokus kepada memodel lengkung splin-B. Kaedah pengoptimuman vektor knot berasaskan algoritma inspirasi tabii diguna bagi menjana lengkung splin-B dengan ralat minimum. Tiga algoritma inspirasi tabii yang dikaji ialah algoritma pengoptimuman zarah berkelompok, gelintaran graviti dan gelintaran harmoni. Hasil kajian mendapati algoritma pengoptimuman zarah berkelompok berupaya memodel lengkung splin-B dengan baik berbanding algoritma gelintaran graviti dan gelintaran harmoni. Hasil kajian juga mendapati semakin bertambah bucu poligon kawalan maka semakin kecil nilai ralat. Oleh itu lengkung splin-B dapat dijana dengan tepat dengan nilat ralat yang kecil

B-SPLINE CURVE MODELLING BASED ON NATURE INSPIRED ALGORITHMS

Curve modelling is an essential aspect in the field of geometric modelling. Conventional curve modelling generates large errors due to the number of data points which is more than the number of control polygon vertices. This study focuses on modelling the B-spline curve. Knot vector optimization method based on nature inspired algorithms is used to model B-spline curve with minimum error. The three nature inspired algorithms are particle swarm optimization, gravity search algorithm and harmony search algorithm. The result shows that the particle swarms optimization model the curve more accurate than the gravity search algorithm and harmony search algorithm. The study also demonstrates that by increasing the number of control polygon vertices, the error will be reduced. Hence the B-spline curve can be modelled accurately by minimum error

Discover Knowledge from Distribution Maps Using Bayesian Networks

This paper applies a Bayesian network to model multi criteria distribution maps and to discover knowledge contained in spatial data. The procedure consists of three steps: pre processing map data, training the Bayesian Network model using distribution maps of Australia and testing the generalization and diagnosis of the model using individual states' maps. The Bayesian network that we used in this study is known as naïve Bayesian network. Results show that this environmental Bayesian network model can generalize the classification rules from training data for good prediction and diagnosis of a distribution map