• 한국전산유체공학회:학술대회논문집
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• 2002.05a
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• pp.47-52
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• 2002

The supersonic flow around multi-stage rocket system is analyzed using 3-D compressible unsteady flow solver. A Chimera overset grid technique is used for the calculation of present configuration and grid around the core rocket is composed of 3 zones to represent fins in the core rocket. Flow solver is parallelized to reduce the computation time, and an efficient parallelization algorithm for Chimera grid technique is proposed. AUSMPW+ scheme is used for the spatial discretization and LU-SGS for the time integration. The flow field around multi-stage rocket was analyzed using this developed solver, and the results were compared with that of a sequential solver The speed-up ratio and the efficiency were measured in several processors. As a result, the computing speed with 12 processors was about 10 times faster than that of a sequential solver. Developed flow solver is used to predict the trajectory of booster in separation stage. From the analyses, booster collides against core rocket in free separation case. So, additional jettisoning forces and moments needed for a safe separation are examined.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.1997-2012
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• 1998

It is necessary to find out the parallelism in tlle sequential Java program to execute it on the parallel machine. The loop is a fundamental source to exploit parallelism as it process a large portion of total execution time in sequential Java program on the parallel machine. However, a complete parallel execution can hardly be achieved due to data dependence. This paper proposes the method of exploiting the implicit parallelism by structuring a dependence graph through the analysis of data dependence in the existing Java programming language having a nested loop structure. The parallel code generation method through the restructuring compiler and also the translation method of Java source program into multithread statement. which is supported by the Java programming language itself, are proposed here. The perforance evaluatlun of the program translaed into the thread statement is conducted using the trip cunt of loop and the trip Count of luop and the thread count as parameters The resttucturing compiler provides efficient way of exploiting parallelism by reducing manual overhead conveliing sequential Java program into parallel code. The execution time for the Java program as a result can be reduced un the parallel machine.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1051-1059
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• 1992

In this paper, two general methods using time-series analysis in the functional separation of the myoelectric signal of human arm movements are developed. Autocorrelation, covariance method and sequential least squares algorithm were used to determine the model parameters and the order of signal model to describe six arm movement patterns` the forearm flexion and extension, the wrist pronation and supination, rotation-in and rotation-out. The confidence interval to classify the functions of arm movement was defined by the mean and standard deviation of total squared error. With the error signals of autoregressive(AR) model, the result showed that the highest success rate was obtained in the case of 4th order, and success rate was decreased with increase of order. Autocorrelation was the method of choice for better success rate. This technique might be applied to biomedical and rehabilitation engineering.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.308-313
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• 2021

In this study, tweets about the 2019 Koreas-US DMZ Summit were collected along with a time sequence and analyzed by a sequential topic modeling method, Dynamic Topic Modeling(DTM). In microblogging services such as Twitter, unstructured data that mixes news and an opinion about a single event occurs at the same time on a large scale, and information and reactions are produced in the same message format. Therefore, to grasp a topic trend, the contextual meaning can be found only by performing pattern analysis reflecting the characteristics of sequential data. As a result of calculating the DTM after obtaining the topic coherence score and evaluating the Latent Dirichlet Allocation(LDA), 30 topics related to news reports and opinions were derived, and the probability of occurrence of each topic and keywords were dynamically evolving. In conclusion, the study found that DTM is a suitable model for analyzing the trend of integrated topics in a specific event over time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.4
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• pp.317-324
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• 2008

Optimum design for general or complex structures are required to the need of many numbers of structural analyses. However, current computational environment with single processor is not capable of generating a high-level efficiency in structural analysis and design process for complex structures. In this paper, a virtual parallel computing system communicated by an internet of personal computers and workstation is constructed. In addition, a routine executing Pro/E, ANSYS and optimization algorithm automatically are adopted in the distributed process technique of sequential approximate optimization for the purpose of enhancing the flexibility of application to general structures. By employing the distributed processing technique during structural analysis using commercial application, total calculation time could be reduced, which will enhance the applicability of the proposed technique to the general complex structures.

• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.37-43
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• 2003

The purpose of this thesis is to detect multiple vehicles using sequence image analysis at process that detect forward vehicles and lane from sequential color images. Detection of vehicles candidate area uses shadow characteristic and edge information in one frame. And, method to detect multiple vehicles area analyzes Estimation of Vehicle(EOV) and Accumulated Similarity Function(ASF) of vehicles candidate areas that exist in sequential images and examine possibility to be vehicles. Most researches detected a forward vehicles in road images but this research presented method to detect several vehicles and apply enough in havy traffic. To verify the effects of the proposed method, we capture the road images with notebook and CCD camera for PC and present the results such as processing time, accuracy and vehicles detection in the images.

• Journal of Wetlands Research
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• v.11 no.2
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• pp.107-121
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• 2009

Many rainfall-runoff models have been used for the flood forecasting. However, the determination of rainfall-runoff model parameters is very difficult. In this study, we investigated the efficiency of flood forecasting models by studying the optimization techniques for parameter calibration of SFM, Tank, and SSARR models. We analyzed the correlations between parameters in optimization techniques, then classified the parameters into parameter groups. For this we applied the sequential calibration method through the sensitivity analysis. As the results of the analysis, the parameter groups clibration method showed better result for peak flow and clibtation time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2021-2029
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• 2010

This paper suggested the method to design and analyze FMC robot's dispatching rule using the Simulation and Sequential Patterns. To do this, first of all, we built FMC using simulation and then, extracted signals that facilities call a robot, saved it as the log type. Secondly, we built robot's optimal path using the Sequential Pattern Mining with the results of analyzing the log and relationship between machine and robot actions. Lastly, we adapted it to the A corp.'s manufacturing line for verifying its performance. As a result of applying the new dispatching rule in FMC, total throughput and total flow time decrease because of decreasing material loss time and increasing robot utility. Furthermore, because this method can be applied for every manufacturing plant using simulation, it can contribute to advance total FMC efficiency as well.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.131-140
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• 2010

Biclustering is a method of finding meaningful subsets of objects and attributes simultaneously, which may not be detected by traditional clustering methods. It is popularly used for the analysis of microarray data representing the expression levels of genes by conditions. Usually, biclustering algorithms do not consider a sequential relation between attributes. For time series data, however, bicluster solutions should keep the time sequence. This paper proposes a new biclustering algorithm for time series data by modifying the plaid model. The proposed algorithm introduces a parameter controlling an interval between two selected time points. Also, the pruning step preventing an over-fitting problem is modified so as to eliminate only starting or ending points. Results from artificial data sets show that the proposed method is more suitable for the extraction of biclusters from time series data sets. Moreover, by using the proposed method, we find some interesting observations from real-world time-course microarray data sets and apartment price data sets in metropolitan areas.