• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1379-1392
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• 2018

The complexity of the differential-linear cryptanalysis is strongly influenced by the probability of the differential-linear characteristic computed under the assumption of round independence, linear approximation independence, and uniformity for the trail that does not satisfy differential trail. Therefore, computing the exact probability of the differential-linear characteristic is a very important issue related to the validity of the attack. In this paper, we propose a new concept called DLCT(Differential-Linear Connectivity Table) for the differential-linear cryptanalysis. Additionally, we propose an improved probability computation technique of differential-linear characteristic by applying DLCT. By doing so, we were able to weaken linear approximation independence assumption. We reanalyzed the previous results by applying DLCT to DES and SERPENT. The probability of 7-round differential-linear characteristic of DES is $1/2+2^{-5.81}$, the probability of 9-round differential-linear characteristic of SERPENT is computed again to $1/2+2^{-57.9}$, and data complexity required for the attack is reduced by $2^{0.2}$ and $2^{2.2}$ times, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.916-920
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• 2007

ADCP(Acoustic Doppler Current Profiler)는 유수의 흐름을 방해하지 않으면서 물 속으로 일정 주파수의 초음파를 전송하고, 부유하는 입자들에 의해 산란되어 돌아오는 반향을 수집, 도플러효과를 이용하여 유속을 측정하는 장비이다. ADCP는 하천을 횡단하면서 순간적인 유속을 측정하므로 시간평균한 평균유속과의 차이가 발생하지만 1초에 1회 이상의 빠른 속도로 연직유속분포를 수집하면서 이를 공간적으로 평균함으로써 순간유속이 갖는 변동성을 완화시키는 특징을 갖는다. 본 연구에서는 ADCP를 활용하여 사행하천에서 수평방향 2차원 유속분포를 측정하고자 하였다. 만곡부가 교호적으로 나타나는 사행하천의 흐름구조는 매우 복잡하다. 특히 주 흐름의 수직인 단면에 나선형의 2차류가 관찰되는데, 이는 원심력과 횡방향의 수면경사 및 난류의 상호작용으로 발생된다. 주 흐름의 유속과 다른 분포를 나타낸다. 본 연구에서는 이찬주 등(2005)이 제시한 공간평균기법과 이동경로 수정을 통하여 측선별 2차원 유속분포를 측정하여 기 개발된 RAMS(서울대학교, 2007)를 적용하고 이를 검증하기 위한 자료를 확보하고자 하였다.

• Journal of the HCI Society of Korea
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• v.2 no.1
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• pp.49-56
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• 2007

A nonintrusive projection-based AR approach using complementary pattern has been recently proposed and applied to virtual studio. However, the approach faces the tradeoff between the pattern imperceptibility and compensation accuracy. To alleviate the tradeoff, we propose a content adaptive pattern concealment approach. The projector input images (AR images) are divided into rectangular regions and spatial variation and color distribution are computed in the regions. Based on the spatial variation and color distribution, we embed locally different strength of pattern images into different color channels. It is demonstrated that the proposed approach has two opposite advantages by comparing it with the previous (non-adaptive) approach through a variety of experiments and subjective evaluation. Our content adaptive approach can obtain the same performance using weaker pattern than the previous approach and thus significantly improve the imperceptibility of the pattern. On the contrary, our content adaptive approach can make strong pattern less perceptible and thus produce better compensation results.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.117-124
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• 2011

In this paper, we present more effective throughput enhancement technique to improve the data rate and reliability by using the multiple antenna technique. The conventional spatial diversity scheme is limited in according with the interference from each antenna channel status, and the orthogonality of spreading codes and subcarriers are destroyed due to the frequency selectivity. Proposed system is considered MC-CDMA system with 4 transmit antennas and 1 receive antenna. Proposed system based on SVD with the MS-RLS MMSE subcarrier combining method in order to achieve better performance with low computational complexity. Via computer simulation, we confirm that the proposed system is able to improve the BER performance by suppressing the interference of other antenna signals.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.991-1000
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• 2021

Among non-invasive blood glucose detection technologies, the optical technique is a method that uses light reflection, absorption, and scattering characteristics when passing through a biological medium. It reduces pain or discomfort in measurement and has no risk of infection. So it is becoming a major flow of blood glucose detection research. Among them, near-infrared spectroscopy has a disadvantage in that the complexity increases when analyzing signals detected due to interferences between proteins and acids that share a similar absorption function with blood glucose molecules. In this study, a non-invasive sensor system with multiple near-infrared bands was designed and manufactured to alleviate the deterioration of blood glucose detection function that may occur due to skin absorption of near-infrared rays. A blood survey was conducted to verify the system, and the degree of blood glucose response in the blood was collected as spectral data, and the results of this study were quantitatively verified in terms of correlation between the data and blood glucose.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.346-346
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• 2015

홍수로 인한 인명 및 재산의 피해는 자연재해 중 가장 많은 부분을 차지한다. 홍수의 심각성과 빈도가 증가함에 따라, 홍수 재해와 관련된 경제적 손실을 감소할 필요성에 대해 국제적인 우려가 늘어나고 있다. 홍수로 인해 야기되는 재해는 적절한 예방 대책을 통해 저감시킬 수 있는데 그 중 홍수위험지역을 예측하는 것은 홍수를 완화시킬 중요한 해결책이 될 수 있다. 따라서 미국과 유럽 등에서는 홍수위험지역을 예측하여 실시간으로 국민들에게 정보를 제공해주어 위험성을 미리 인식시키고 대비할 수 있도록 국가적인 지원을 하고 있다. 아직 국내에서는 홍수통제소 등의 국가기관에서 주요 국가하천에서의 홍수위만을 실시간으로 제공하고 있어 홍수위험지도의 제작 및 제공이 필요한 실정이다. 이러한 실시간 홍수위험지도를 제작하기 위한 연구는 지속되어 왔으나 범람구역의 설정 및 복잡한 수리해석 등의 어려움을 동반하여 적용을 위한 노력이 필요한 상황이다. 본 연구에서는 1차원 모형인 FLDWAV 모형을 이용하여 실시간 홍수위험지도 제작을 위한 기초 자료 제공으로 홍수위험지역을 선정해보았다. 국내 홍수예 경보시스템에서 사용하는 FLDWAV 모형을 이용하여 실시간으로 홍수위를 산정하여 홍수위험지역을 선정할 수 있었으며, 그 결과도 홍수흔적도와 상당히 일치하는 것으로 나타났다. 본 연구를 통해 빠르고 정확한 홍수위험지역 선정이 가능할 것으로 판단되며, 정확한 수리계산이 필요한 지역이나 홍수보험의 가입이 필요한 지역의 선정 등에 기여할 수 있을 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Management & Information Systems Review
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• v.35 no.2
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• pp.83-97
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• 2016

The purpose of this study is to evaluate our system and consider how the tax rules on corporate reorganization and asset adjustment account can be improved. The scope of this study includes the Korean tax rules on corporate reorganizations, as well as key tax benefits provided by the Special Tax Treatment Control Act. In case of Korea, The relevant regulations and system of taxation respecting the merger has been made the rapid progress in several respects in this situation, there are capable of improvement. This paper suggests improve some problems on tax avoidance abusing incomplete tax law. First, the asset adjustment account is the difficulty that it needs to follow-up during this period of time, as well as the complexity of the accounting and tax adjustments on the practice. If it is permitted to succeed asset-liability as market value, the complexity of asset adjustment account in corporate tax accounting also disappear. Second, in case that controlling shareholders possess more than 20% of merged entity, they could not get the tax deferral until after the time of two years has elapsed. It needs to further alleviate the merged entity ratio than the present level. Finally, after the merger it will be to strengthen the surveillance provisions of five years from the current two years. In addition, continuity of shareholder's requirements and business requirements, it is also necessary to strengthen the requirements of the follow-up provided by a separate regulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.341-343
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• 2019

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

• Journal of Technology Innovation
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• v.30 no.2
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• pp.1-27
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• 2022

In recent years, the autonomous vehicle industry has changed drastically. So the needs and interests in predicting future technologies and market prospects of the autonomous vehicle field have been very increased. However, considering the characteristics of the automotive industry, which has various factors, complex correlation of them and big influence on each other, the study of systematic future forecasting methodologies are urgent and necessary which are applicable to autonomous vehicle industry. In this research, the two methods such as "Field Anomaly Relaxation" and "Multiple Perspective Concept" were analyzed and chosen, which are suitable to automotive industry. By the combination of two methods this research developed and examined the three future scenarios related to core technologies and industry trends. And these scenarios feasibility was verified by experts and evaluation checklist. This research has a contribution that this future scenario forecasting approach can be applied to the industries which have various volatility like the autonomous vehicle industry.