• ETRI Journal
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• v.28 no.6
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• pp.721-731
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• 2006

A fast intra skip detection algorithm based on the ratedistortion (RD) cost for an inter frame (P-slices) is proposed for H.264/AVC video encoding. In the H.264/AVC coding standard, a robust rate-distortion optimization technique is used to select the best coding mode and reference frame for each macroblock (MB). There are three types of intra predictions according to profiles. These are $16{\times}16$ and $4{\times}4$ intra predictions for luminance and an $8{\times}8$ intra prediction for chroma. For the high profile, an $8{\times}8$ intra prediction has been added for luminance. The $4{\times}4$ prediction mode has 9 prediction directions with 4 directions for $16{\times}16$ and $8{\times}8$ luma, and $8{\times}8$ chrominance. In addition to the inter mode search procedure, an intra mode search causes a significant increase in the complexity and computational load for an inter frame. To reduce the computational load of the intra mode search at the inter frame, the RD costs of the neighborhood MBs for the current MB are used and we propose an adaptive thresholding scheme for the intra skip extraction. We verified the performance of the proposed scheme through comparative analysis of experimental results using joint model reference software. The overall encoding time was reduced up to 32% for the IPPP sequence type and 35% for the IBBPBBP sequence type.

• Computers and Concrete
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• v.21 no.4
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• pp.463-470
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• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.48-56
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• 2009

We propose a fast intra mode skip decision algorithm for H.264/AVC video encoding. Although newly added MB encoding algorithms based on various prediction methods increase compression ratio, they require a significant increase in the computational complexity because we calculate rate-distortion(RD) cost for all possible MB coding modes and then choose the best one. In this paper, we propose a fast mode decision algorithm based on an adaptive motion vector map(AMVM) method for H.264/AVC video encoding to reduce the processing time for the inter frame. We verify that the proposed algorithm generates generally good performances in PSNR, bit rates, and processing time.

• Journal of Communications and Networks
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• v.11 no.3
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• pp.279-286
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• 2009

This paper proposes an adaptive mode switching algorithm between two strategies in multiple antenna cellular networks:A single-user mode and a multi-user mode for the broadcast channel. If full channel state information is available at the base station, it is known that a multi user transmission strategy would outperform all single-user transmission strategies. In the absence of full side information, it is unclear what the capacity achieving method is, and thus there are few criteria to decide which of the myriad possible methods performs best given a system configuration. We compare a single user transmission and a multi user transmission with linear receivers in this paper where the transmitter and the receivers have multiple antennas, and find that neither strategy dom inates the other. There is instead a transition point between the two strategies. Then, the mode switching point is determined both ana lytically and numerically for a multiple antenna cellular downlink with correlation between transmit antennas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2796-2813
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• 2014

This paper presents a new algorithm called the adaptive logarithmic increase and adaptive decrease algorithm (A-LIAD), which mainly addresses the Round-Trip Time (RTT) fairness problem in satellite networks with a very high propagation delay as an alternative to the current TCP congestion control algorithm. We defined a new increasing function in the fashion of a logarithm depending on the increasing factor ${\alpha}$, which is different from the other logarithmic increase algorithm adopting a fixed value of ${\alpha}$ = 2 leading to a binary increase. In A-LIAD, the ${\alpha}$ value is derived in the RTT function through the analysis. With the modification of the increasing function applied for the congestion avoidance phase, a hybrid scheme is also presented for the slow start phase. From this hybrid scheme, we can avoid an overshooting problem during a slow start phase even without a SACK option. To verify the feasibility of the algorithm for deployment in a high-speed and long-distance network, several aspects are evaluated through an NS-2 simulation. We performed simulations for intra- and interfairness as well as utilization in different conditions of varying RTT, bandwidth, and PER. From these simulations, we showed that although A-LIAD is not the best in all aspects, it provides a competitive performance in almost all aspects, especially in the start-up and packet loss impact, and thus can be an alternative TCP congestion control algorithm for high BDP networks including a satellite network.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.4
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• pp.394-400
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• 2018

A heterogenous network is the one of key technologies for 5G, where one cell is divided into small cells in order to extend coverage and support high data rates. Divided cells aggravates the intercell interference problem as the cell edge increases. In order to avoid the intercell interference, it is the best to allocate the different spectrum for each cells. However, it also decreases the spectral efficiency. Therefore, the trade-off between the spectral efficiency gain and the signal quality loss by the interference should be considered for an efficient spectrum sharing in the heterogenous network. The adaptive modulation is the method to change the transmitted bit according to the channel quality, which is adopted as the standard in the most practical communication systems. It should be considered to applied the performance analysis into the practical systems. In this paper, the overall cell data rates is analyzed for the heterogenous network under the adaptive modulation. The Monte Carlo simulation results verify the correctness of the analysis.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.12
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• pp.71-82
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• 2003

In this paper, we present a new method to calculate reinforcement value in QoS routing algorithm targeted for real-time applications based on Ant algorithm to efficiently and effectively reinforce ant-like mobile agents to find the best route toward destination in a network regarding necessary QoS metrics. Simulation results show that the proposed method realizes QoS routing more efficiently and more adaptively than those of the existing method thereby providing better solutions for the best route selection for real-time application that has high priority on delay jitter and bandwidth.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Advances in Computational Design
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• v.1 no.4
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.