• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2192-2200
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• 2015

In this paper, we propose an efficient vehicle image compensation algorithm based on Histogram Equalization. The proposed a vehicle image compensation algorithm was elimination to the vehicle image shake using motion compensation and motion estimation. And, algorithm was calculated the histogram of pixel values from each sub-image by dividing the image as the constant size areas in order to image enhancement. Also, it had enhancement to the image by adjusting the gradient. The proposed algorithm was evaluate the difference between of performance and time, image by applied to the IP, and were confirmed the image enhancement with removing of vehicle camera image shake. In this paper, the proposed vehicle image enhancement algorithm was demonstrated effectiveness when compared to existing vehicle image stabilization, because the elimination of shake for the vehicle images used real-time processing without using a memory. And it was obtained the reduction effect of the computation time by the calculated through block matching, and obtained the better restoration result for naturalness of the image with the lowest noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.471-476
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• 2016

This paper presents our study on speech recognition performance prediction. Our initial study shows that a combination of speech quality measures effectively improves correlation with Word Error Rate (WER) compared to each speech measure alone. In this paper we demonstrate a new combination of various types of speech quality measures shows more significantly improves correlation with WER compared to the speech measure combination of our initial study. In our study, SNR, PESQ, acoustic model score, and MFCC distance are used as the speech quality measures. This paper also presents our speech database verification system for speech recognition employing the speech measures. We develop a WER prediction system using Gaussian mixture model and the speech quality measures as a feature vector. The experimental results show the proposed system is highly effective at predicting WER in a low SNR condition of speech babble and car noise environments.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.77-86
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• 2005

In this paper, we designed a learning algorithm of LVQ that extracts classification errors and learns ones and improves classification performance. The proposed LVQ learning algorithm is the learning Networks which is use SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of LVQ. To extract pattern vectors which cause classification errors, we proposed the error-cause condition, which uses that condition and constructed the pattern vector space which consists of the input pattern vectors that cause the classification errors and learned these pattern vectors , and improved performance of the pattern classification. To prove the performance of the proposed learning algorithm, the simulation is performed by using training vectors and test vectors that are Fisher' Iris data and EMG data, and classification performance of the proposed learning method is compared with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional classification.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.119-124
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• 1998

Block Matching Motion Estimation(BMME) Algorithm is demands a very large amount of computing power and have been proposed many fast algorithms. These algorithms are many problem that larger size of VLSI scale due to non-localized search block data and problem of non-reuse of input data for each processing step. In this paper, we designed systolic arry of high processing capacity, constraints input output pin size and reuse of input data for small VLSI size. The proposed systolic array is optimized memory access time because of iterative reuse of input data on search block and become independent of problem size due to increase of algorithm's parallelism and total processing elements connection is localized spatial and temporal. The designed systolic array is reduced O(N6) time complexity to O(N3) on moving vector and has O(N) input/output pin size.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.219-228
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• 2007

End-to-end path setup and maintenance are very important for mobile ad-hoc wireless communications, because of the mobility and the limited battery capacity of the nodes in the networks. the AODV routing protocol is the one of mary proposed protocols. However, there are route failure problem with the Proposed protocols between intermediate nodes due to such mobility and exhausted battery characteristics, and this is because only the shortest hop count is considered for the route setup. If route failure happens. Problem such as the waste of bandwidth and the increment of the energy consumption occur because of the discarding data packets in the intermediate nodes and the path re-setup process required by the source node. In addition, it obviously causes the network lifetime to be shortened. This paper proposes a routing protocol based on the AODV routing protocol that it makes use of the remaining energy, signal strength and SNR of the MAC layer resources to setup a path.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.1-8
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• 2017

Accurate forecasting enables to effectively prepare for future phenomenon. Especially, meteorological phenomenon is closely related with human life, and it can prevent from damage such as human life and property through forecasting of weather and disaster that can occur. To respond quickly and effectively to oil spill accidents, it is important to accurately predict the movement of oil spills and the weather in the surrounding waters. In this paper, we selected four representative machine learning techniques: support vector machine, Gaussian process, multilayer perceptron, and radial basis function network that have shown good performance and predictability in the previous studies related to oil spill detection and prediction in meteorology such as wind, rainfall and ozone. we suggest the applicability of oil spill prediction model based on machine learning.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.29-34
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• 2019

If it takes much time to calculate the fitness of the solution in genetic algorithms, it is essential to create a metamodel. Much research has been completed to improve the performance of metamodels. In this study, we tried to get a better performance of metamotel using discrete Walsh transform in discrete domain. We transforms the basis of the solution and creates a metamodel using the transformed solution. We experimented with NK-landscape, a representative function of the pseudo-boolean function, and provided empirical evidence on the performance of the proposed model. When we performed the genetic algorithm using the proposed model, we confirmed that the genetic algorithm found a better solution. In particular, our metamodel showed better performance than that using the radial basis function network that modified the similarity function for the discrete domain.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.472-483
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• 2016

This paper proposes a blocking artifacts detection algorithm in frequency domain for MC-FRUC (Motion Compensated Frame Rate Up-Conversion). Conventional MC-FRUC algorithms occur blocking artifacts near interpolated block boundaries since motion compensation is performed from block-based motion vector. For efficiently decreasing blocking artifacts, this paper analyses frequency characteristics of the interpolated frame and reduces blocking artifacts on block boundaries. In experimental results the proposed method shows better subjective quality than some conventional FRUC method and also increases the PSNR(Peak Signal to Noise Ratio) value on average 0.45 dB compared with BDMC(Bi-Directional Motion Compensation).

• The Journal of the Acoustical Society of Korea
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• v.13 no.2E
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• pp.41-50
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• 1994

This paper proposes a DMS(Dynamic Multi-Section) model based on the information of the similar features in word pattern. This model represents each word as a time series of several sections and each section implies duration time information and typical feature vectors. The procedure to make a model in the word pattern is that typical feature vector and duration time information are reflected in the distance, when matching between word pattern and model is repeated. As the result of it, the accumulated distance by matching is to be minimized.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.13-20
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• 2003

Based on the studies on the motion vector distributions estimated on the image sequences, we proposed constrained diamond search (DS) algorithm for fast block-matching motion estimation. By considering the fact that motion vectors are searched within the 2 pixels distance in vertically and horizontally on average, we confirmed that DS algorithm achieves close performance on error ratio and requires less computation compared with new three-step search (NTSS) algorithm. Also, by applying displaced frame difference (DFD) to DS algorithm, we reduced the computational loads needed to estimate the motion vectors within the stable block that do not have motions. And we reduced the possibilities falling into the local minima in the course of estimation of motion vectors by applying DFD to DS algorithm. So, we knew that proposed constrained DS algorithm achieved enhanced results as aspects of error ratio and the number of search points to be necessary compared with conventional DS algorithm, four step search (FSS) algorithm, and block-based gradient-descent search algorithm