• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.186-186
• /
• 2018

본 연구에서는 매개변수 자동 보정기법인 PEST(Model-Independent Parameter Estimation and Uncertainty Analysis)를 CAT 모형에 연계하여 유출특성 분석을 실시하였다. CAT-PEST 연계모형은 CAT 모형의 모의유출량을 이용하며 PEST의 반복계산을 통하여 최적 매개변수를 추정한다. 침투방법은 CAT에서 제공하는 Rainfall Excess 방법, Green and Ampt 방법 및 Horton 방법을 이용하였으며 각 침투방법에 따른 유출 특성을 비교 및 분석하였다. 연구대상유역은 보령 댐 유역으로 유역면적은 $163.7km^2$이며 전체면적의 약 80%가 산지로 구성되어 있고, 유로연장은 22.3km, 유역평균경사는 40.19%이다. 또한 보령댐 유역의 월평년값 평균기온은 -0.8에서 $25.5^{\circ}C$로 계절변동이 매우 큰 것을 알 수 있다. 최근 몇 년간 심각한 가뭄 피해를 입은 보령댐 유역은 2016년에 도수관로를 완공하여 이를 통해 금강으로부터 물을 끌어다 쓰고 있는 실정이며, CAT 모형에서는 금강도수유입량을 외부유입 처리하여 유출량을 산정하였다. 모의기간은 1999년부터 2017년까지이며 전체기간에 대한 보정 후 연도별 보정을 실시하였다. 통계적 평가수단은 $R^2$, RMSE 및 NSE를 사용하여 유역 최종출구점에서의 유출량과 비교하였으며 전체기간에 대한 보정결과 NSE와 $R^2$가 0.75 이상으로 나타나 대체적으로 모의 유출수문곡선이 관측 수문곡선과 유사한 양상을 보였다.

• International Journal of Highway Engineering
• /
• v.2 no.2
• /
• pp.149-161
• /
• 2000

The stiffness of the subbase materials is represented by the resilient modulus, $M_R$, which are very important properties in the mechanistic design of flexible pavement system. However, the cyclic $M_R$ testing method is too complex, expensive, and time consuming to be applicable on a production basis. In this study, the alternative $M_R$ testing technique for subbase materials was developed using a free-free resonant column (FF-RC) test considering deformational characteristics of subbase materials. To estimate the deformational characteristics of subbase materials, effects of strain amplitude and mean effective stress on modulus of subbase materials were investigated. The $M_R$ values determined by alternative testing procedures matched well with those determined by standard $M_R$ test, showing the capability of the proposed methods being used in determining $M_R$ values.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.7
• /
• pp.1575-1581
• /
• 2005

Recently, further study is being done on the affect of crack on concrete structure and many people have made every endeavor not to leave it unsettled but to minimize it by repair works. In this paper we propose the image processing method that do not remain manual but automatically process the length, the direction and e width of cracks on concrete surface. First, we calibrate light's affect from image by using closing operation, one of morphology methods that can extract the feature of oracle and we extract the edge of crack image by sobel mask. After it, crack image is binarized by iteration binarization. And we extract the edge of cracks using noise elimination method that use an average of adjacent pixels by 3${\times}$3 mask and Glassfire Labeling algorithm. on, in this paper we propose an image processing method which can automatically measure the length, the direction and the width of cracks using the extracted edges of cracks. The results of experiment showed that the proposed method works better on the extraction of concrete cracks. Also our method showed the possibility that inspector's decision is unnecessary.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.12A
• /
• pp.1291-1297
• /
• 2004

In this paper, the new scheduling algorithm that supports the maximization of system throughput and the proportional fairness among non-real time traffic users is proposed in OFDMA wireless mobile communication systems. The concept of the proposed algorithm is based on the proportional fairness algorithm, which is deployed in 3GPP2, and sorting method. The proportional fairness algorithm is adapted to allocate the number of subcarrier per user. A sorting method is contributed to the maximization of system throughput in the practical allocation of subcarrier per user. Simulation results show that new algorithm had better performance than the max rate rule in case of fairness, higher throughput than the scheduling algorithm without sorting method. Even though the system throughput of the proposed algorithm is almost same with the iteration scheme using subcarrier swapping method between users, the computational time of the former is reduced up to 3 times than the latter.

• Journal of Korea Game Society
• /
• v.20 no.3
• /
• pp.75-84
• /
• 2020

This paper introduces an interactive indirect illumination algorithm which considers indirect visibility. First, a small number of rays are emitted on hemisphere of the current pixel to obtain the first intersection. If this point is directly illuminated by the light source, its illuminated color is collected. Second, in order to approximate the indirect visibility, a 3D ray marching algorithm, which is based on a hierarchy structure, is used to accelerate the ray-voxel intersection. Third, the indirect images are denoised by an edge-avoiding filtering with a local means replacement method.

• Journal of KIISE:Software and Applications
• /
• v.34 no.2
• /
• pp.160-168
• /
• 2007

For developing a text classifier using supervised learning, a manually labeled corpus of large size is required. However, it takes a lot of time and human effort. Recently a research paradigm was proposed to use a raw corpus and a small amount of seed information instead of manually labeled corpus. In this paper we introduce an unsupervised learning method that makes it possible to achieve better performance than other related works. The characteristics of our approach is that average mutual information is used to learn representative words and their weights and then update of the weights is done using a technique inspired by the works in information retrieval. By iterating this teaming process it was shown that a high performance system can be developed.

• The KIPS Transactions:PartC
• /
• v.10C no.7
• /
• pp.857-866
• /
• 2003

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques in order to analyze an audit data set. However. since they mainly analyze the average behavior of a user's activities, some anomalies can be detected inaccurately. In this paper, a new clustering algorithm for modeling the normal pattern of a user's activities is proposed. Since clustering can identify an arbitrary number of dense ranges in an analysis domain, it can eliminate the inaccuracy caused by statistical analysis. Also, clustering can be used to model common knowledge occurring frequently in a set of transactions. Consequently, the common activities of a user can be found more accurately. The common knowledge is represented by the occurrence frequency of similar data objects by the unit of a transaction as veil as the common repetitive ratio of similar data objects in each transaction. Furthermore, the proposed method also addresses how to maintain identified common knowledge as a concise profile. As a result, the profile can be used to detect any anomalous behavior In an online transaction.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.5
• /
• pp.64-74
• /
• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.2
• /
• pp.170-177
• /
• 2019

In this paper, three theories for improving the stability of quadruped robot are presented. First, the Minimum-Jerk Trajectory is used to optimize the leg trajectory. Second, we compare the newly proposed sine wave and the conventional LSM in this paper based on the Jerk value. Third, we calculate the optimum stride of the sway through repetitive robot simulation using ADAMS-MATLAB cosimulation. Through the above process, the improvement of the robot walking is compared with the existing theory. First, the average gradient of the point where the leg trajectory changes rapidly was reduced from at least 1.2 to 2.9 by using the Minimum-Jerk targetory for the movement of the body and the end of the leg during the first walk, thereby increasing the walking stability. Second, the average Jerk was reduced by 0.019 on the Z-axis, 0.457 on the X-axis, and 0.02, 3D on the Y-axis by 0.479 using the Sin wave type sways presented in this paper, rather than the LSM(Longitude Stability Margin) method. Third, the length of the optimal stride for walking at least the Jerk value was derived from the above analysis, and the 20cm width length was the most stable.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.6 no.1
• /
• pp.35-39
• /
• 2001

Multibeam echosounder data, collected to investigate seabed features and topography, are usually subject to outliers resulting from the ship's irregular movements and insufficient correction for pressure calibration to the positions of beams. We introduce a statistical method which adjusts the outliers using the ARMA (Autoregressive Moving Average) technique. Our method was applied to a set of real data acquired in the East Sea. In our approach, autocorrelation of the data is modeled by an AR (1) model. If an observation is substantially different from that obtained from the estimated AR (1) model, it is declared as an outlier and adjusted using the estimated AR (1) model. This procedure is repeated until no outlier is found. The result of processing shows that outliers that are far greater than signals in amplitude were successfully removed.