• The Journal of Korean Physical Therapy
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• v.22 no.5
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• pp.109-115
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• 2010

Purpose: This study was to delineate the structural change of neural pathway after sequential motor learning using diffusion tensor imaging (DTI). Methods: The participants were 16 healthy subjects, which were divided by training (n=8) and control (n=8) group. The task for the training was the Serial Reaction Time Task (SRTT) which was designed by Superlab program. When the 'asterisk' shows up in the 4 partition spaces on the monitor, the subject presses the correct response button as soon as possible. The training group participated in the training program of motor learning with SRTT composed of 24 digits pattern in one hour per daily through 10 days during 2 weeks. Results: In the behavioral results the training group showed significant changes in the increase of response number and the reduction of response time than those of the control group. There was significant difference in the left inferior frontal area in the fractional anisotropy (FA) map of the training group in DTI analysis. Conclusion: Motor sequential learning as like SRTT may be needed to the learning of language and visuospatial processing and may be induced for the experience-dependent structural plasticity during short period.

• Journal of Environmental Science International
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• v.15 no.1
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• pp.1-7
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• 2006

The purpose of this study is to illustrate the characteristics of soil organic matter (SOM) and partition coefficient $(K_{DOC})$. Humic substances (HS) from eight soils of varying properties were extracted by two different methods. The dissolved organic carbon (DOC) concentration was stabilized in 22hrs. The ratio of UV absorbance at 465nm and 665nm (E4/E6 ratio) for HS were similar pattern for 8 soils. The extraction with increasing pH increased dissolution of SON. The ratio of organic carbon (OC) associated with HA and FA (the HA:FA ratio) was varied widely in accordance with the soils and was highly correlated to OC $content(\%)$ of the soils. in modeling metal speciation in soils and soil solutions, assumptions that all DOC in soil solution is associated with FA and that HA:FA ratio in SOM is constant have been made. The results of this study indicate that the validity of these assumptions is questionable. By sequential pH extraction, the $K_{DOC}$ showed in a linear correlation with pH.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.187-196
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• 2017

It is urgent to prepare countermeasures for traffic congestion problems of Korea's metropolitan area where central functions such as economic, social, cultural, and education are excessively concentrated. Most users of public transportation in metropolitan areas including Seoul use the traffic cards. If various information is extracted from traffic big data produced by the traffic cards, they can provide basic data for transport policies, land usages, or facility plans. Therefore, in this study, we extract valuable information such as the subway passengers' frequent travel patterns from the big traffic data provided by the Seoul Metropolitan Government Big Data Campus. For this, we use a Hadoop (High-Availability Distributed Object-Oriented Platform) to preprocess the big data and store it into a Mongo database in order to analyze it by a sequential pattern data mining technique. Since we analysis the actual big data, that is, the traffic cards' data provided by the Seoul Metropolitan Government Big Data Campus, the analyzed results can be used as an important referenced data when the Seoul government makes a plan about the metropolitan traffic policies.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.8
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• pp.556-561
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• 2011

In this study, the theoretical maximum power of a heat engine was investigated by sequential Carnot cycle model, for a low-grade heat source of about $100^{\circ}C$. In contrast to conventional approaches, the pattern search algorithm was employed to optimize the two design variables to maximize power. Variations of the maximum power and the optimum values of design variables were investigated for a wide range of UA(overall heat transfer conductance) change. The results show that maximizing heat source utilization does not always maximize power.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.193-200
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• 2022

Unknown cryptographic communication protocols may have advantage of guaranteeing personal and data privacy, but when used for malicious purposes, it is almost impossible to identify and respond to using existing network security equipment. In particular, there is a limit to manually analyzing a huge amount of traffic in real time. Therefore, in this paper, we attempt to identify packets of unknown cryptographic communication protocols and separate fields comprising a packet by using machine learning techniques. Using sequential patterns analysis, hierarchical clustering, and Pearson's correlation coefficient, we found that the structure of packets can be automatically analyzed even for an unknown cryptographic communication protocol.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• Research in Plant Disease
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• v.21 no.4
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• pp.268-272
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• 2015

Bacterial leaf blight caused by Pseudomonas syringae pv. porri is one of the major bacterial diseases of garlic (Allium sativum). In South Korea, the disease has only been observed in garlic-growing regions of Jeju island. The spatial distribution pattern of the disease was analyzed by binary power law, in which the natural logarithm of the observed variance is regressed on the natural logarithm of the binomial variance. The estimated slope (b=1.361) of the regression was greater than 1 which meant that the diseased plants were aggregated. The sequential sampling plans were developed for estimating the mean incidence rate ($p_m$) and classifying the mean incidence as being below or above the critical incidence rate ($p_t$). These results could be used on more efficient and higher precisive sampling for bacterial blight of garlic compared to fixed sample sized sampling.

• Korean journal of applied entomology
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• v.40 no.2
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• pp.105-109
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• 2001

Dispersion pattern of the citrus red mite (CRM), Panonychus citri (McGregor) was determined to develop a monitoring method in the satsuma mandarin fields, Citrus unshiu L., in Jeju-do, during 1999 and 2000. CRM population was sampled by collecting leaves. Taylor's power law provided better description of mean-variance relationship for the dispersion indices compared to Iwao's patchiness regression. Slopes and intercepts of Taylor's power law from leaf samples did not differ among surveyed groves. Fixed-precision levels (D) of a sequential sampling plan were developed using Taylor's power law parameters generated from all motile stages of CRM in leaf sample. This sampling plan for leaf sample estimate was tested with resampling validation for sampling plan using 4 independent data sets. Resampling simulation analysis demonstrated that actual fixed-precision level values were better than desired D values of 0.20, 0.25 and 0.30. Required numbers for tree sampling at the density of more than 7 mites per tree were fewer than 18.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.11
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• pp.1397-1405
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• 2013

The optimal rib pattern design of B-pillar trim considering occupant head protection can be determined by two methods. One is the conventional approximate optimization method that uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by trial-and-error. The quality of results strongly depends on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, called the sequential design of experiments (SDOE), to reduce the trial-and-error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• Tunnel and Underground Space
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• v.15 no.4 s.57
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• pp.264-274
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• 2005

The optimum blasting pattern to excavate a quarry efficiently and economically can be determined based on the minimum production cost which is generally estimated according to rock fragmentation. Therefore it is a critical problem to predict fragment size distribution of blasted rocks over an entire quarry. By comparing various prediction models, it can be ascertained that the result obtained from Kuz-Ram model relatively coincides with that of field measurements. Kuz-Ram model uses the concept of rock factor to signify conditions of rock mass such as block size, rock jointing, strength and others. For the evaluation of total production cost, it is imperative to estimate 3-D spatial distribution of rock factor for the entire quarry. In this study, a sequential indicator simulation technique is adopted for estimation of spatial distribution of rock factor due to its higher reproducibility of spatial variability and distribution models than Kriging methods. Further, this can reduce the uncertainty of predictor using distribution information of sample data The entire quarry is classified into three types of rock mass and optimum blasting pattern is proposed for each type based on 3-D spatial distribution of rock factor. In addition, plane maps of rock factor distribution for each ground levels is provided to estimate production costs for each process and to make a plan for an optimum blasting pattern.