2012, Vol.17, No.11

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

In this paper, the extension of the addressing mode in the 16-bit Thumb instruction set architecture is proposed to improve the performance of 16-bit Thumb code. The key idea of the proposed approach is the introduction of newaddressingmodes formore frequent instructions by using the saved bits fromthe reduction of the register fields in less frequently used instructions. The proposed approach adopts efficient addressing modes fromthe 32-bit ARMarchitecture, which is the superset of the 16-bit Thumb architecture. To speed up access to a data list, scaled register offset addressingmode and post-indexed addressingmode are introduced for load and store instructions. Experiments showthat the proposed approach improves performance by an average of 8.5% when compared to the conventional approach.

Debris flow deposition model is a model to predict affected areas by debris flow and randomwalk model (RWM) was used to build themodel. Although themodel was proved to be effective in the prediction of affected areas, the model has several free parameters decided experimentally. There are several well-knownmethods to estimate parameters, however, they cannot be applied directly to the debris flowproblemdue to the small size of training data. In this paper, a modified neural network, called pseudo sample neural network (PSNN), was proposed to overcome the sample size problem. In the training phase, PSNNuses pseudo samples, which are generated using the existing samples. The pseudo samples smooth the solution space and reduce the probability of falling into a local optimum. As a result, PSNNcan estimate parameter more robustly than traditional neural networks do. All of these can be proved through the experiments using artificial and real data sets.

Reversible lanes in urban intersections is used to efficiently control vehicles, reduce traffic congestion and increase the capacity of a roadway. But by far traffic control systems in urban intersections are simple and manually operated by police officers. In this study, we present a hybrid algorithmthat intelligently resolve the moving direction of reversible lanes to efficiently manage the flow of traffic at intersection. The proposed algorithmconsists of three stages:(i) fuzzy inference method to get the efficiency of moving direction, (ii) a provisional decision whether to change the reversible lane to different direction, (iii) a final evaluation criterion for changing the directions of the reversible lanes. The fuzzy inference results of efficiency are shown by using matlab application.