• Smart Structures and Systems
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• v.8 no.1
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• pp.107-117
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• 2011

Biological sensory systems continuously monitor and analyze changes in real-world environments that are relevant to an animal's specific behavioral needs and goals. Understanding the sensory mechanisms and information processing principles that biological systems utilize for efficient sensory data acquisition may provide useful guidance for the design of smart-sensing systems in engineering applications. Weakly electric fish, which use self-generated electrical energy to actively sense their environment, provide an excellent model system for studying biological principles of sensory data acquisition. The electrosensory system enables these fish to hunt and navigate at night without the use of visual cues. To achieve reliable, real-time task performance, the electrosensory system implements a number of smart sensing strategies, including efficient stimulus encoding, multi-scale virtual sensor arrays, task-dependent filtering and online subtraction of sensory expectation.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.315-319
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• 2000

400ppm의 pentachlorophenol(PCP) 오염토양을 5$m\ell$ scintillation vial의 multi reactor와 1L 크기의 one reactor를 써서 Hydrogen peroxide와 Hemoglobin 촉매반응에 의해 PCP 분해정도를 조사하였다. 대부분 초기에 반응이 빠르게 진행되므로 time scale을 8시간 이내와 한달여기간동안 살펴보았다. 8시간동안의 PCP 분해정도는 일차함수로 동력학 계수가 -0.0233으로 나타났고, 이때 제거효율은 60.8%이고 one reactor의 경우 30일동안 80%의 제거효율을 보였다. PCP 회수율은 multi reactor의 경우 96.5($\pm$6.7)이고 one reactor(fan scale)의 경우 90.1%였다.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.76-78
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• 1993

This paper describes an efficient method of computing any desired number of the most unstable eigenvalues and eigenvectors of a large scale multi-machine power system. Approximate eigenvalues obtained by Hessenberg process are refined using Rayleigh quotient iteration with cubic convergence property. If further eigenvalues and eigenvectors are needed, the procedure described above are repeated with deflation. The proposed algorithm can cover all the model types of synchronous machines, exciters, speed governing system and PSS defined in AESOPS. The proposed algorithm applied to New England test system with 10 machines and 39 buses produced the results same with AESOPS in faster computation time. Also eigenvectors computed in Rayleigh quotient iteration makes it possible to make eigen-analysis for improving unstable modes.

• Annual Conference of KIPS
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• 2018.05a
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.3
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• pp.233-242
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• 2015

This paper analyzes a multi-product inbound lot-sizing and outbound dispatching problem with multi-vehicle types in a third-party logistics distribution center. The product must be delivered to the customers within the delivery time window and backlogging is not allowed. Replenishing orders are shipped by several types of vehicles with two types of the freight costs, i.e., uniform and decreasing, are considered. The objective of this study is to determine the lot-size and dispatching schedules to minimize the total cost with the sum of inbound and outbound transportation and inventory costs over the entire time horizon. In this study, we mathematically derive a mixed-integer programming model and propose a genetic algorithm (GA1) based on a local search heuristic algorithm to solve large-scale problems. In addition, we suggest a new genetic algorithm (GA2) with an adjusting algorithm to improve the performance of GA1. The basic mechanism of the GA2 is to provide an unidirectional partial move of products to available containers in the previous period. Finally, we analyze the results of GA1 and GA2 by evaluate the relative performance using the gap between the objective values of CPLEX and the each algorithm.

• Journal of the Korean Society of Safety
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• v.31 no.5
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• pp.95-101
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• 2016

This study proposes a multi-scale dynamic system reliability analysis of control system as a method of quantitative evaluation of its performance in probabilistic terms. In this second paper, we discuss the control effect of the viscous damper on the seismic performance of the structure-level failure. Since the failure of one structural member does not necessarily cause the collapse of the structural system, we need to consider a set of failure scenarios of the structural system and compute the sum of the failure probabilities of the failure scenarios where the statistical dependence between the failure scenarios should be taken into account. Therefore, this computation requires additional system reliability analysis. As a result, the proposed approach takes a hierarchial framework where the failure probability of a structural member is computed using a lower-scale system reliability with the union set of time-sequential member failures and their statistical dependence, and the failure probability of the structural system is again computed using a higher-scale system reliability with the member failure probabilities obtained by the lower-scale system reliability and their statistical dependence. Numerical results demonstrate that the proposed approach can provide an accurate and stable reliability assessment of the control performance of the viscous damper system on the system failure. Also, the parametric study of damper capacity on the seismic performance has been performed to demonstrate the applicability of the proposed approach through the probabilistic assessment of the seismic performance improvement of the damper system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.127-135
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• 2017

The computing environment has changed rapidly to enable large-scale finite element models to be analyzed at the PC or workstation level, such as multi-core CPU, optimal math kernel library implementing BLAS and LAPACK, and popularization of direct sparse solvers. In this paper, the design considerations on a parallel finite element code for shared memory based multi-core CPU system are proposed; (1) the use of optimized numerical libraries, (2) the use of latest direct sparse solvers, (3) parallelism using OpenMP for computing element stiffness matrices, and (4) assembly techniques using triplets, which is a type of sparse matrix storage. In addition, the parallelization effect is examined on the time-consuming works through a large scale finite element model.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.48-57
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• 2007

Eye localization is necessary for face recognition and related application areas. Most of eye localization algorithms reported thus far still need to be improved about precision and computational time for successful applications. In this paper, we propose an improved eye localization method based on multi-scale Gator feature vector models. The proposed method first tries to locate eyes in the downscaled face image by utilizing Gabor Jet similarity between Gabor feature vector at an initial eye coordinates and the eye model bunch of the corresponding scale. The proposed method finally locates eyes in the original input face image after it processes in the same way recursively in each scaled face image by using the eye coordinates localized in the downscaled image as initial eye coordinates. Experiments verify that our proposed method improves the precision rate without causing much computational overhead compared with other eye localization methods reported in the previous researches.

• Smart Structures and Systems
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• v.14 no.4
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• pp.679-697
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• 2014

The strain data acquired from structural health monitoring (SHM) systems play an important role in the state monitoring and damage identification of bridges. Due to the environmental complexity of civil structures, a better understanding of the actual strain data will help filling the gap between theoretical/laboratorial results and practical application. In the study, the multi-scale features of strain response are first revealed after abundant investigations on the actual data from two typical long-span bridges. Results show that, strain types at the three typical temporal scales of $10^5$, $10^2$ and $10^0$ sec are caused by temperature change, trains and heavy trucks, and have their respective cut-off frequency in the order of $10^{-2}$, $10^{-1}$ and $10^0$ Hz. Multi-resolution analysis and wavelet shrinkage are applied for separating and extracting these strain types. During the above process, two methods for determining thresholds are introduced. The excellent ability of wavelet transform on simultaneously time-frequency analysis leads to an effective information extraction. After extraction, the strain data will be compressed at an attractive ratio. This research may contribute to a further understanding of actual strain data of long-span bridges; also, the proposed extracting methodology is applicable on actual SHM systems.