• Nuclear Medicine and Molecular Imaging
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• 제41권2호
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• pp.71-77
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• 2007

Advance of neuroimaging technique has greatly influenced recent brain research field. Among various neuroimaging modalities, positron emission tomography has played a key role in molecular neuroimaging though functional MRI has taken over its role in the cognitive neuroscience. As the analysis technique for PET data is more sophisticated, the complexity of the method is more increasing. Despite the wide usage of the neuroimaging techniques, the assumption and limitation of procedures have not often been dealt with for the clinician and researchers, which might be critical for reliability and interpretation of the results. In the current paper, steps of voxel-based statistical analysis of PET including preprocessing, intensity normalization, spatial normalization, and partial volume correction will be revisited in terms of the principles and limitations. Additionally, new image analysis techniques such as surface-based PET analysis, correlational analysis and multimodal imaging by combining PET and DTI, PET and TMS or EEG will also be discussed.

• 한국통신학회논문지
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• 제41권1호
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• pp.37-41
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• 2016

Localization is one of the key issues of demandable applications, especially smart services. Beside the traditional GPS based localization technique, the localization issue by visible light communications is promising market because of possibility of combining visible light communications with positioning technique for a high accurate, especially indoor localization service. This paper provides the overview and new image sensor scheme for localization issue based on visible light communication. The survey is introduced from core techniques to enhancement issues of localization. We hope these will be the essential references for the impact selection method in implementation and standardization issues.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.131-135
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• 2021

Numbers of studies related to optimization of design of organic light emitting diodes(OLED) through machine learning are increasing. We propose the generative method of the image to assess the performance of the device combining with machine learning technique. Principle parameter regarding dark spot growth mechanism of the OLED can be the key factor to determine the long-time performance. Captured images from actual device and randomly generated images at specific time and initial pinhole state are fed into the deep neural network system. The simulation reinforced by the machine learning technique can predict the device parameters accurately and faster. Similarly, the inverse design using multiple layer perceptron(MLP) system can infer the initial degradation factors at manufacturing with given device parameter to feedback the design of manufacturing process.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.69-78
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• 2022

In this paper, we propose the new technique adopting power control to MIMO(multi-input multi-output)-OFDMA(orthogonal frequency division multiplexing Access) system with multi-beamformer. The proposed power controlling algorithm for MIMO-OFDMA allocates the transmitting power of each subcarrier based on the CSI(channel state information) and the interference signal. CSI is feedback from base station to mobile station to decide the transmitting power of each subcarrier. Through the proposed technique, we can control iteratively the transmitting power and update the weight of beamformer simultaneously. Therefore, the SNIR of each subcarrier become to converge the target SNIR and the beam is formed toward the desired direction. And the performance of MIMO-OFDMA system with the proposed approach is very improved. The improvement in bit error rate is investigated through computer simulation of a MIMO-OFDMA system with the proposed approach.

• Journal of applied mathematics & informatics
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• 제41권5호
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• pp.973-988
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• 2023

In real-life scenarios, we may have to deal with real numbers or numbers in intervals or a combination of both to solve multi-criteria decision-making (MCDM) problems. Also, we may come across a situation where we must combine this interval and actual number membership values into a single real number. The most significant factor in combining these membership values into a single value is by using aggregation operators or scoring algorithms. To overcome such a situation, we suggest the cubic hypersoft set (CHSS) concept as a workaround. Ultimately, this makes it simple for the decision-maker to obtain information without misconceptions. The primary aim of this study is to establish some operational laws for the cubic hypersoft set, present the fundamental properties of aggregation operators and propose an algorithm by using the technique of order of preference by similarity to the ideal solution (TOPSIS) technique based on correlation coefficients to analyze the stress-coping skills of workers.

• Nuclear Engineering and Technology
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• 제56권5호
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• pp.1672-1678
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• 2024

We introduce a creative approach combining machine learning with optimization techniques to enhance the optimization of the loading pattern (LP). Finding the optimal LP is a critical decision that impacts both the reload safety and the economic feasibility of the nuclear fuel cycle. While simulated annealing (SA) is a widely accepted technique to solve the LP optimization problem, it suffers from the drawback of high computational cost since LP optimization requires three-dimensional depletion calculations. In this note, we introduce a technique to tackle this issue by leveraging neural networks to filter out inappropriate patterns, thereby reducing the number of SA evaluations. We demonstrate the efficacy of our novel approach by constructing a machine learning-based optimization model for the LP data of the Korea Standard Nuclear Power Plant (OPR-1000).

• 응용통계연구
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• 제27권3호
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• pp.357-371
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• 2014

최근 들어 데이터 마이닝의 분류문제에 있어 목표변수의 불균형 문제가 많은 관심을 받고 있다. 이러한 문제를 해결하기 위해, 이전 연구들은 원 자료에 대하여 데이터 전처리 과정을 실시했는데, 전처리 과정에는 목표변수의 다수계급을 소수계급의 비율에 맞게 조정하는 과소표집법, 소수계급을 복원추출하여 다수계급의 비율에 맞게 조정하는 과대표집법, 소수계급에 K-최근접 이웃 방법 등을 활용하여 과대표집법을 적용 후 다수계급에는 과소표집법을 적용한 하이브리드 기법 등이 있다. 또한 앙상블 기법도 이러한 불균형 데이터의 분류 성능을 높일 수 있다고 알려져 있어, 본 논문에서는 데이터의 전처리 과정과 앙상블 기법을 함께 고려한 여러 모형들을 사용하여, 불균형 자료에 대한 이들모형의 분류성능을 비교평가한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5998-6016
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• 2017

In thin-film transistor liquid-crystal displays (TFT-LCDs), which are most commonly used in mobile devices, the backlight accounts for about 70% of the power consumption. Therefore, most low-power-related studies focus on realizing power savings through backlight dimming. Image compensation is performed to mitigate the visual distortion caused by the backlight dimming. Therefore, popular techniques include pixel compensation for brightness recovery and contrast enhancement, such as histogram equalization. However, existing pixel compensation techniques often have limitations with respect to blur owing to the pixel saturation phenomenon, or because contrast enhancement cannot adequately satisfy the human visual system (HVS). To overcome these, in this study, we propose a novel dimming technique to achieve both power saving and HVS-awareness by combining the pixel compensation and histogram specifications, which convert the original cumulative density function (CDF) by designing and using the desired CDF of an image. Because the process of obtaining the desired CDF is customized to consider image characteristics, histogram specification is found to achieve better HVS-awareness than histogram equalization. For the experiments, we employ the LIVE image database, and we use the structural similarity (SSIM) index to measure the degree of visual satisfaction. The experimental results show that the proposed technique achieves up to 15.9% increase in the SSIM index compared with existing dimming techniques that use pixel compensation and histogram equalization in the case of the same low-power ratio. Further, the results indicate that it achieves improved HVS-awareness and increased power saving concurrently compared with previous techniques.

• 한국인터넷방송통신학회논문지
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• 제9권3호
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• pp.179-185
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• 2009

인지 라디오(CR : Cognitive Radio)는 주파수 자원을 효율적으로 사용하기 위해 제안된 기술로 최근 주파수 자원의 부족 현상을 완화하기 위한 방법으로 연구 되고 있다. 인지 라디오는 주파수 정책에 따라 주파수를 할당받은 우선순위 사용자(Primary user)가 해당 주파수를 사용하고 있지 않을 때 부 사용자(Secondary user)가 빈 주파수를 검출하여 해당 주파수를 사용하는 기술이다. 부 사용자가 우선순위 사용자를 검출하는 기술인 스펙트럼 센싱은 수행하는 과정에서 다중경로 페이딩문제로 인해 성능이 낮아지는데 이를 보완하기 위해 본 논문에서는 다이버시티기법을 사용한다. 다이버시티 기술에는 시간 다이버시티, 주파수 다이버시티, 공간 다이버시티 등이 있는데 본 논문에서는 공간 다이버시티의 한 종류인 안테나 다이버시티를 적용하여 다중경로 페이딩 문제를 해소하며 스펙트럼 센싱의 성능 및 신회성이 높아짐을 모의실험의 결과를 통하여 제시한다.

• 한국음향학회지
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• 제41권2호
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• pp.150-158
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• 2022

본 논문에서는 2021년 9월 동해에서 수행된 초장거리 대역확산 수중음향통신의 해상실험 결과를 제시한다. 8개의 수직 배열 센서를 이용하여 수중음향통신 신호를 수집하였으며, 전송 거리는 160 km로 하였다. 송신 신호로 30 bps의 다중 코드 대역 확산 방식과 100 bps의 처프 대역확산 방식이 사용되었다. 실험 결과 단일 채널에서 채널 부호화 기법이 적용되지 않은 경우에 높은 비트 오류율을 나타내었으나 각 수신 채널에서 프레임 동기화를 수행한 후 신호들에 등이득 조합 다이버시티 기법을 적용하면 비부호화 비트 오류율이 순방향 오류 정정 한계인 0.1 이하로 감소하였다.