• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.766-769
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• 2005

본 연구에서는 카오스 랜덤 캐리어 PWM에 의한 3상 유도 전동기의 스위칭 소음 저감에 대하여 다루고 있다. 랜덤 수 발생기로서는 종전부터 사용해 온 선형일치발생기(LCG)대신에 Logistic map의 분기트리를 사용하였다. 카오스 랜덤 수 발생은 80C196 마이크로 콘트롤러가 담당하고 있으며, 80C196으로부터 발생된 카오스 랜덤 수와 MAX038에 의하여 삼각파 랜덤 캐리어가 발생되고 있다. 1.5kw급 3상 유도전동기 구동 시스템에 카오스 랜덤 PWM와 종전의 방법을 적용하여 전동기 전압 및 전류 그리고 스위칭 소음의 스펙트럼을 고찰하였다.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.406-415
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• 2021

The worldwide spread of the Internet and the digital information revolution have resulted in a rapid increase in the use and transmission of multimedia information due to the rapid development of communication technologies. It is important to protect images in order to prevent problems such as piracy and illegal distribution. To solve this problem, I propose a new digital color image encryption algorithm in this paper. I design a new pseudo-random number generator based on 1D five-neighborhood maximum length cellular automata (FN-MLCA) to change the pixel values of the plain image into unpredictable values. And then I use a 3D chaotic cat map to effectively shuffle the positions of the image pixel. In this paper, I propose a method to construct a new MLCA by modeling 1D FN-MLCA. This result is an extension of 1D 3-neighborhood CA and shows that more 1D MLCAs can be synthesized. The safety of the proposed algorithm is verified through various statistical analyses.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.11-22
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• 2017

In recent years, coastal dune erosion has accelerated as various structures have been developed around the coastal dunes. A land cover map should be developed to identify the characteristics of sand dunes and to monitor the condition of sand dunes. The Korean Ministry of Environment's land cover maps suffer from problems, such as limited classes, target areas, and durations. Thus, this study conducted experiments using RGB and multispectral images based on UAV (Unmanned Aerial Vehicle) over an approximately one-year cycle to create a land cover map of coastal dunes. RF (Random Forest) classifier was used for the analysis in accordance with the experimental region's characteristics. The pixel- and object-based classification results obtained by using RGB and multispectral cameras were evaluated, respectively. The study results showed that object-based classification using multispectral images had the highest accuracy. Our results suggest that constant monitoring of coastal dunes can be performed effectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.1-6
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• 2005

In the Markov network which models disparity map with the Markov Random Fields(MRF), the belief propagation algorithm is operated by message passing between nodes corresponding to each pixel. The initial message value is converged by iterations of the algorithm and the algorithm requires many iterations to get converged messages. In this paper, we simplify the algorithm by regarding the objects in the disparity map as combinations of lines with same message valued nodes to reduce iterations of the algorithm.

• The Journal of Korean Association of Computer Education
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• v.18 no.5
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• pp.63-70
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• 2015

In the environment that the complex conditions need to be satisfied, skyline query have been applied to various field. To processing a skyline query in centralized scheme, several techniques have been suggested and recently map/reduce platform based approaches has been proposed which divides data space into multiple partitions for the vast volume of multidimensional data. However, the performances of these approaches are fluctuated due to the uneven data loading between servers and redundant tasks. Motivated by these issues, this paper suggests a novel technique called MR-DEAP which solves the uneven data loading using the random sampling. The experimental result gains the proposed MR-DEAP outperforms MR-Angular and MR-BNL scheme.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.1 no.1
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• pp.74-82
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• 2020

Predictions of suitable habitat areas can provide important information pertaining to the risk assessment and management of alien plants at early stage of their establishment. Here, we predict the invasion potential of Muhlenbergia capillaris (pink muhly) in South Korea using five bioclimatic variables. We adopt four models (generalized linear model, generalized additive model, random forest (RF), and artificial neural network) for projection based on 630 presence and 600 pseudo-absence data points. The RF model yielded the highest performance. The presence probability of M. capillaris was highest within an annual temperature range of 12 to 24℃ and with precipitation from 800 to 1,300 mm. The occurrence of M. capillaris was positively associated with the precipitation of the driest quarter. The projection map showed that suitable areas for M. capillaris are mainly concentrated in the southern coastal regions of South Korea, where temperatures and precipitation are higher than in other regions, especially in the winter season. We can conclude that M. capillaris is not considered to be invasive based on a habitat suitability map. However, there is a possibility that rising temperatures and increasing precipitation levels in winter can accelerate the expansion of this plant on the Korean Peninsula.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.286-295
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• 2000

We present a novel statistically adaptive method using the Minimum Description Length(MDL) principle for unsupervised segmentation of magnetic resonance(MR) images. In the method, Markov random filed(MRF) modeling of tissue region accounts for random noise. Intensity measurements on the local region defined by a window are modeled by a finite Gaussian mixture, which accounts for image inhomogeneities. The segmentation algorithm is based on an iterative conditional modes(ICM) algorithm, approximately finds maximum ${\alpha}$ posteriori(MAP) estimation, and estimates model parameters on the local region. The size of the window for parameter estimation and segmentation is estimated from the image using the MDL principle. In the experiments, the technique well reflected image characteristic of the local region and showed better results than conventional methods in segmentation of MR images with inhomogeneities, especially.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.139-147
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• 2023

As heavy precipitation rates have increased due to climate change, the risk of landslides has also become greater. Studies in the field of disaster risk assessment predominantly focus on evaluating intrinsic importance represented by the use or role of facilities. This work, however, focused on evaluating risks according to the external conditions of facilities, which were presented via debris flow simulation. A random walk model (RWM) was partially improved and used for the debris flow simulation. The existing RWM algorithm contained the problem of the simulation results being overly concentrated on the maximum slope line. To improve the model, the center cell height was adjusted and the inertia application method was modified. Facility information was collected from a digital topographic map layer. The risk level of each object was evaluated by combining the simulation result and the digital topographic map layer. A risk assessment technique suitable for the polygon and polyline layers was applied, respectively. Finally, by combining the evaluated risk with the attribute table of the layer, a system was prepared that could create a list of objects expected to be damaged, derive various statistics, and express the risk of each facility on a map. In short, we used an easy-to-understand simulation algorithm and proposed a technique to express detailed risk information on a map. This work will aid in the user-friendly development of a debris flow risk assessment system.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.4
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• pp.297-302
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• 2003

Genetic linkage maps serve the plant geneticist in a number of ways, from marker assisted selection in plant improvement to map-based cloning in molecular genetic research. Genetic map based upon DNA polymorphism is a powerful tool for the study of qualitative and quantitative traits in crops. The objective of this study was to develop genetic linkage map of soybean using the population derived from the cross of Korean soybean cultivar 'Kwangkyo, and wild accession 'IT182305'. Total 1,000 Operon random primers for RAPD marker, 49 combinations of primer for AFLP marker, and 100 Satt primers for SSR marker were used to screen parental polymorphism. Total 341 markers (242 RAPD, 83 AFLP, and 16 SSR markers) was segregated in 85 $\textrm{F}_2$ population. Forty two markers that shown significantly distorted segregation ratio (1:2:1 for codominant or 3:1 for domimant marker) were not used in mapping procedure. A linkage map was constructed by applying the computer program MAPMAKER/EXP 3.0 to the 299 marker data with LOD 4.0 and maximum distance 50 cM. 176 markers were found to be genetically linked and formed 25 linkage groups. Linkage map spanned 2,292.7 cM across all 25 linkage groups. The average linkage distance between pair of markers among all linkage groups was 13.0 cM. The number of markers per linkage group ranged from 2 to 55. The longest linkage group 3 spanned 967.4 cM with 55 makers. This map requires further saturation with more markers and agronomically important traits will be joined over it.