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A parallel net of (1-NN, K-NN) classifiers for optical inspection of surface defects in ferrites

100%

Jóźwik A. , Chmielewski L. , Skłodowski M. , Cudny W.

Machine Graphics and Vision

1998

tom Vol. 7, No. 1/2

99-112

The challenging task of optical inspection of surface defects in ferrite cores has been successfully approached with a set of methods. In this paper the attention is paid to the k Nearest Neighbours classifier developed for the system. A parallel net of two-decision classifiers is presented. The combination of the 1-NN and k-NN rules reduces the training time. A great part of computations is restricted to the class overlap area. The classification quality is significantly improved if a separate feature selection for each of the component classifiers is done. A dramatic improvement of classification speed obtained by reference patterns sets reduction for component classifiers is vital, as in the considered task the classifier is used for recognition of pixels. The proposed modifications of the classifier are of general usefulness for pattern recognition. The presented quality inspection system can be applied to various defect detection tasks.

An example of computer aided decision-making system for recognition of respiration pathology

67%

Jóźwik A. , Sokołowska B. , Budzińska K.

2001

tom Vol. 2, Part. 1

MI41--MI48

A main objective of the work was presentation of a new statistic approach to an analysis of respiration data. The breathing with intact and denervated diaphragm was compared. The respiration process was desciribed by three parameters: breathing frequency, tidal volume, and minute ventilation. Experimental data concerned a group of twelve anaesthetised cats. These data were analysed by a modification of the well-known k nearest neighbour rule (k-NN). It has been adopted from the statistical pattern recognition theory. The three ventilatory parameters were used to recognise whether we deal with the normal or the pathological case. Certain percentage of misclassifications must be taken into account. This misclassification rate is a measure how strong is the dependence between the ventilation parameters and preservation of the diaphragm innervation. The proposed method promises good differentiation of the two compared ways of respiration. It offers nearly five times smaller misclassification rate as compared with the standard k-NN rule.

Modyfikacja algorytmu Changa z wykorzystaniem metody znajdowania punktów najbliższych

59%

Sierszeń A.

2007

tom T. 11, z. 3

193-202

Modyfikacja algorytmu Changa polegająca na zastąpieniu oryginalnego sposobu wyznaczania odległości do najbliżej położonego punktu z tej samej klasy zmodyfikowanymi metodami znajdowania punktów najbliższych przynosi dużą akceleracje obliczeń. Przeprowadzone eksperymenty dowodzą, że zaproponowana metoda nie zmniejsza w sposób istotny jakości klasyfikacji.

The modification of Chang's algorithm consisting in replacement of the original method of determining the distance to the nearest point from the same class with modified methods of finding the mutually nearest points causes a great acceleration of the computational phase. Results of experiments show that the presented method does not significantly decrease the quality of classification.