• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.287-294
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• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

• Journal of the Korea Society for Simulation
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• v.16 no.3
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• pp.57-63
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• 2007

In real time rendering of fetus the empty space leaping while traversing a ray is most frequently used accelerating technique. The main idea is to skip empty voxel samples which do not contribute the result image and it speeds up the rendering time by avoiding sampling data while traversing a ray in the empty region, saving a substantial number of interpolations. Calculating the distance from the nearest object boundary for every yokel can reduce the sampling operation. Among widely-well-known distance maps, those estimates the true distance, such as euclidean distance, takes a long time to compute because of the complicated floating-point operations, and others which uses approximated distance functions, such as city-block and chessboard, provides faster computation time but sampling error may can occur. In this paper, therefore, we analyze the characteristics of several distance maps and compare the number of samples and rendering time. And we aim to suggest the most appropriate distance map for rendering of fetus in ultrasound image.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.895-900
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• 2023

We study low-complexity graph sampling which selects a subset of nodes from graph nodes so as to reconstruct the original signal from the sampled one. To achieve complexity reduction, we propose a graph sampling algorithm with thresholding which selects a node with a cost lower than a given threshold at each step without fully searching all of the remaining nodes to find one with the minimum cost. Since it is important to find the threshold as close to a minimum cost as possible to avoid degradation of the reconstruction performance, we present a mathematical expression to compute the threshold at each step. We investigate the performance of the different sampling methods for various graphs, showing that the proposed algorithm runs 1.3 times faster than the previous method while maintaining the reconstruction performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.680-687
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• 2018

Borehole radar is a radar used for underground resources and geological exploration purposes. It needs a high-speed sampler to transmit electromagnetic waves with a pulse width of several ns and to receive reflected waves of several tens to several hundreds of MHz reflected from the object to be surveyed. ETS (Equivalent-Time Sampling), which can achieve sampling performance of several GHz with a sampling frequency of several tens of MHz, is suitable for use as a sampler of a borehole radar receiver. In this paper, we propose a method to control the sampling clock delay, which is the most important factor in ETS sampler design, using four clocks with phase difference of $90^{\circ}$ for one clock source. The proposed method can reduce the time required to acquire the data within the set interval by 1/25 than the conventional method using the delay generator. When the implemented sampler is applied to the receiver of existing borehole radar, it is possible to accumulate 58 additional times due to the shortened sampling time. In addition, by using one delay control logic compared with the conventional method using several sampling clock delay control logic in order to satisfy the target sampling range, it is possible to omit the correction process which was necessary in the past. As a result, the structure of the system can be simplified and a uniform sampler can be realized.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.20-25
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• 2019

Most multimedia signals contain image data, so the problem of efficient processing and transmitting the image data is an important task of the information society. This paper proposes a compression algorithm that reduces the color bits according to importance using YUV color space among the various methods of compressing image data. 4: 2: 2 subsampling is the standard in the field of video. Using the color information and the characteristics of the human retina, YUV color data was reduced by 4: 2: 2 subsampling. The YUV images and RGB images can be interconverted using the transformation matrix. The image data was converted into color space by YUV, and the relatively low U and V bits were subjected to a downscaling operation. The data was then compressed through 4: 2: 2 subsampling. The performance of the proposed algorithm was compared and analyzed by a comparison with existing methods. As a result of the analysis, it was possible to compress the image without reducing the information of the low importance color element and without significant deterioration in the quality compared to the original.

• Journal of Internet Computing and Services
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• v.17 no.2
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• pp.49-57
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• 2016

Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving prediction accuracy. Bagging is one of the most popular ensemble learning techniques. Bagging has been known to be successful in increasing the accuracy of prediction of the individual classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then combines the predictions of these classifiers to get the final classification result. Bootstrap samples are simple random samples selected from the original training data, so not all bootstrap samples are equally informative, due to the randomness. In this study, we proposed a new method for improving the performance of the standard bagging ensemble by optimizing bootstrap samples. A genetic algorithm is used to optimize bootstrap samples of the ensemble for improving prediction accuracy of the ensemble model. The proposed model is applied to a bankruptcy prediction problem using a real dataset from Korean companies. The experimental results showed the effectiveness of the proposed model.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.11a
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• pp.145-146
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• 2002

대기중의 유해 에어로솔의 장거리 이동에 대한 과학적인 자료를 마련하기 위해 2002년 3월 29일부터 4월 11일까지 제주도 서쪽 끝에 위치한 고산사이트에서 Filter Pack을 사용한 PM$_{2.5}$ 에어로솔의 샘플링을 수행하였다. 이로부터 제주도 고산지역 대기 중 PM$_{2.5}$ 중 중금속 성분을 측정하여, 오염물질의 배출원 추정 및 장거리 이동가능성을 검토하고자 하였다. (중략)(중략)

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.236-239
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• 2014

3차원 영상을 제작하기 위해서는 여러 시점의 색상 영상과 함께 깊이 정보를 필요로 한다. 하지만 깊이 정보를 얻을 때 사용하는 ToF 카메라는 해상도가 낮으며 적외선 신호의 주파수 문제 때문에 최대 3대까지 사용할 수 있다. 따라서 깊이 정보를 색상 영상과 함께 사용하기 위해서 깊이 정보의 업샘플링이 필수적이다. 업샘플링은 깊이 정보를 색상 카메라 위치로 3차원 워핑하고 결합형 양방향 필터(joint bilateral filter, JBF)를 사용하여 빈 영역을 채우는 방법으로 진행된다. 업샘플링은 오랜 시간이 소요되지만 그래픽스 프로세싱 유닛(graphics processing units, GPU)를 이용하여 빠르게 수행될 수 있다. 본 논문에서는 다중 GPU의 병렬 수행을 통하여 빠르게 다시점 깊이맵을 생성할 수 있는 방법을 제안한다. 다중 GPU 병렬 수행은 범용 목적 GPU(general purpose computing on GPU, GPGPU) 중의 하나인 CUDA를 이용하였으며, 본 논문에서 제안된 방법을 이용하여 3개의 GPU 사용한 실험 결과 초당 35 프레임의 다시점 깊이맵을 생성했다.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.04a
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• pp.227-228
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• 2002

대기오염물질 중 입자상물질(particulate matter, PM)의 크기분포 측정은 주로 다단임팩터(cascade impactor)에 의해 이루어지고 있다. 임팩터 각 단에 포집된 입자의 무게를 측정하여 입자상물질의 크기 분포로 환산하는 다단임팩터는 입자의 샘플링에 많은 시간이 소모되며 필터의 전처리 및 무게측정과정이 번거롭고, 시간에 따른 농도분포의 추이를 파악할 수 없는 단점이 있다. (중략)

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.465-469
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• 2017

In this paper we propose a human body tracking method for application of smartphones. The conventional human body tracking method is divided into a sensor-based method and a vision-based method. The sensor-based methods have a weakness in that tracking accuracy is low due to cumulative error of position information. The vision-based method has no cumulative error, but it requires reduction of the computational complexity for application of smartphone. In this paper we use the improved HOG algorithm as a human body tracking method for application of smartphone. The improved HOG algorithm is implemented through downsampling and frame sampling. Gaussian pyramid is applied for downsampling, and uniform sampling is applied for frame sampling. We measured the proposed algorithm on two devices, four resolutions, and four frame sampling intervals. We derive the best detection rate among downsampling and frame sampling parameters that can be applied in realtime.