• Journal of Advanced Marine Engineering and Technology
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• v.34 no.1
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• pp.62-68
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• 2010

The requirement of high efficiency and low emission for marine diesel engines are being enforced because of air pollution and climate change on the earth. In connection with these, many new technologies are considered. But they are mainly for new building ship. It is necessary to be concerned about the improvement of engine performance for existing ship. In this paper, the simulation program for performance of marine two-stroke diesel engine was developed to predict the deteriorating performance according to elapsed time for existing ship. The result was compared with the result of the program named TOP-CODE which was used by engine maker and checked to be shown good agreement between them.

• The KIPS Transactions:PartA
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• v.8A no.3
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• pp.201-208
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• 2001

Explosive growth of the Web and the non-uniform characteristics of client requests result in the performance degradation of Web servers, and server cache has been recognized as the solution. We analyzed Web server accessing characteristics-repetition, size, and locality of access. Based on the result, we analyzed the cache removal policies and proposed a prefetch strategy to improve the hit ratio of server caches. In addition, through the trace-driven simulation based on the traces from real Web sites, we showed the performance improvement by our proposal.

• Tunnel and Underground Space
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• v.14 no.5
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• pp.353-362
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• 2004

Prediction of fragmentation in bench blasting is one of the most important factors to establish the production plan. It is widely accepted that fragmentation could be accurately predicted using the Kuz-Ram model in bench blasting. Nevertheless, the model has an ambiguous or subjective aspect in evaluating the model parameters such as joint condition, rock strength, density, burden, explosive strength and spacing. This study proposes a new method to evaluate the parameters of Kuz-Ram model, and the predicted mean fragment sizes using the proposed method are examined by comparing the measured sizes in the field. The results show that the predictions using Kuz-Ram model with the proposed method coincide with field measurements, but Kuz-Ram model does not reflect the in-situ rock condition and hence needs to be improved.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.11
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• pp.18-24
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• 2009

This paper proposes two novel prediction-based adaptive selection cooperation schemes combined with a new relay selection strategy. In the proposed schemes, the destination predicts whether the transmission will be successful or not before a single relay is selected to transmit source's decoded data. Depending on the prediction, the destination feeds back a command to the whole network. Numerical results show that the proposed schemes combined with the relay selection strategy successfully reduce its outage probability, improve its throughput, save transmitted power, and prolong the lifetime of the network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1587-1594
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• 1994

The pitch predictor is one of the most important part for the robust tree coder. The hybrid backward pitch adapation which is a combination of a block adaptation and a recursive adaptation is used for the pitch predictor. In order to improve the error performance and track the pitch period change of the input speech, it is proposed to smooth the input of the pitch predictor. The smoother with three taps can have fixed coefficients or variable coefficients depending on the estimated autocorrelation function of the output of the pitch synthesizer. The inclusion of a variable smoother can track the pitch period change within a block and reduce the effect of channel errors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2562-2570
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• 2014

One of the important hints for inferring the function of unknown proteins is the knowledge about protein subcellular localization. Recently, there are considerable researches on the prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular localization. In this paper, label power-set classification is improved for the accurate prediction of multiple subcellular localization. The predicted multi-labels from the label power-set classifier are combined with their prediction probability to give the final result. To find the accurate probability estimates of multi-classes, this paper employs pair-wise comparison and error-correcting output codes frameworks. Prediction experiments on protein subcellular localization show significant performance improvement.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1125-1132
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• 2017

In a situation where the severity and frequency of drought events getting stronger and higher, many studies related to drought forecast have been conducted to improve the drought forecast accuracy. However it is difficult to predict drought events using a single model because of nonlinear and complicated characteristics of temporal behavior of drought events. In this study, in order to overcome the shortcomings of the single model approach, we first build various single models capable to explain the relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and other independent variables such as world climate indices. Then, we developed a combined models using Stochastic Gradient Descent method among Ensemble Learnings.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.647-656
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• 2011

Motion estimation using one-bit transform and two-bit transform reduces the complexity for computation of matching error; however, the peak signal-to-noise ratio (PSNR) is degraded. Modified 1BT (M1BT) and modified 2BT (M2BT) have been proposed to compensate degraded PSNR by adding conditional local search. However, these algorithms require many additional search points in fast moving sequences with a block size of $16{\times}16$. This paper provides more efficient search method by preparing candidate blocks using the number of non-matching points (NNMP) than the conditional local search. With this NNMP-based search, we can easily obtain candidate blocks with small NNMP and efficiently search final motion vector. Experimental results show that the proposed algorithm not only reduces computational complexity, but also improves PSNR on average compared with conventional search algorithm used in M1BT, M2BT and AM2BT.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.437-443
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• 2015

This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.