2013, Vol.18, No.4

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

Dynamic Software Update(DSU) is a technique, which updates a new version of the software to a running process without stopping. Many DSU systems that update C application programs are introduced. However, these DSU systems differ in implementation method or in main features. In this paper, we propose a new DSU system that can solve some disadvantages of existing DSU systems. DSU system presented in this paper splits existing program to code, global data and local data and then updates each part of the program considering the characteristics of the respective parts. The proposed system in this paper is implemented and tested on Linux. Also, we compared our DSU system with other DSU systems and we could find some strength of our DSU system. First, the code memory usage of our DSU system can be efficient since our system does not need to maintain code of an old version. Second, the global data memory waste is small because our system does not need to allocate the global data again which is not modified in the new version. Finally, we restore local data of old version in stack area of the new version using stack reconstruction technique. This paper is meaningful since we proposed a new DSU method and we implemented a full DSU system using the method.

Panorama picturing is an elongated photographing technique that connects images with rotating and moving multiple images horizontally that are partly overlapped. However, for hand-operated photographs, it is difficult to adjust overlapped parts because of tilted angles. There has been a study comparing adjacent pictures using labeling technique but it was time-consuming and had angle dissonant cases in nature. In this paper, we propose a less time-consuming paranoiac scene reconstruction method. Our method is also based on labeling-and-comparing technique but uses only 1/3 of it. Then, if there exists angle dissonance, it tries to find characteristic points by SURF algorithm and adjusts them with homography. The efficacy of this method is experimentally verified by experiments using various images.

Pseudo sample neural network (PSNN) is a variant of traditional neural network using pseudo samples to mitigate the local-optima-convergence problem when the size of training samples is small. PSNN can take advantage of the smoothed solution space through the use of pseudo samples. PSNN has a focus on the quantity problem in training, whereas, methods stressing the quality of training samples is presented in this paper to improve further the performance of PSNN. It is evident that typical samples and highly correlated features help in training. In this paper,therefore, kernel density estimation is used to select typical samples and correlation factor is introduced to select features, which can improve the performance of PSNN. Debris flow data set is used to demonstrate the usefulness of the proposed methods.