• Journal of the Korean earth science society
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• v.41 no.2
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• pp.95-110
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• 2020

Comparisons between two different surface temperatures from high-resolution ECMWF ReAnalysis 5 (ERA5) and Automated Synoptic Observing System (ASOS) observations were performed to investigate the reliability of the new reanalysis data over South Korea. As ERA5 has been recently produced and provided to the public, it will be highly used in various research fields. The analysis period in this study is limited to 1999-2018 because regularly recorded hourly data have been provided for 61 ASOS stations since 1999. Topographic characteristics of the 61 ASOS locations are classified as inland, coastal, and mountain based on Digital Elevation Model (DEM) data. The spatial distributions of whole period time-averaged temperatures for ASOS and ERA5 were similar without significant differences in their values. Scatter plots between ASOS and ERA5 for three different periods of yearlong, summer, and winter confirmed the characteristics of seasonal variability, also shown in the time-series of monthly error probability density functions (PDFs). Statistical indices NMB, RMSE, R, and IOA were adopted to quantify the temperature differences, which showed no significant differences in all indices, as R and IOA were all close to 0.99. In particular, the daily mean temperature differences based on 1-hour-averaged temperature had a smaller error than the classical daily mean temperature differences, showing a higher correlation between the two data. To check if the complex topography inside one ERA5 grid cell is related to the temperature differences, the kurtosis and skewness values of 90-m DEM PDFs in a ERA5 grid cell were compared to the one-year period amplitude among those of the power spectrum in the time-series of monthly temperature error PDFs at each station, showing positive correlations. The results account for the topographic effect as one of the largest possible drivers of the difference between ASOS and ERA5.

• Journal of Plant Biotechnology
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• v.35 no.4
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• pp.307-316
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• 2008

Transposon-mediated insertional mutagenesis is one of powerful strategy for assessing functions of genes in higher plants. In this report, we have selected highly susceptible and tolerance plant by screening about high salt (3% NaCl) and cold stresses ($4^{\circ}C$) from F2 seeds of 30,000 Ac/Ds insertional mutagenesis lines in rice (Oryza sativa L. cv. Dongjin). In order to identify the gene tagging, insertion of Ds element was analyzed by Southern blot and these results revealed that 19 lines were matched genotype of selected lines with phenotype from the first selected 212 lines, and 13 lines have one copy of Ds elements. The Franking Sequence Tags (FSTs) of selected mutant lines showed high similarities with the following known function genes: signal transduction and regulation of gene expression (transpoter, protease family protein and apical meristem family protein), osmotic stress response (heat shock protein, O-methyltransferase, glyceraldehyde-3-phosphate dehydrogenase and drought stress induce protein), vesicle trafficking (SYP 5 family protein) and senescence associated protein. The expression pattern of 19 genes were analyzed using RT-PCR under the abiotic stresses of 9 class; 250mM NaCl, osmotic, drought, 3% $H_2O_2$, $100{\mu}M$ ABA, $100{\mu}M$ IAA, 0.1 ppm 2,4-D, $4^{\circ}C$ cold and $38^{\circ}C$ high temperature. Isolated knock-out genes showed the positive response about 250 mM NaCl, drought, $H_2O_2$, PEG, IAA, 2,4-D, ABA treatment and low ($4^{\circ}C$) and high temperature ($38^{\circ}C$). The results from this study indicate that function of selected knock-out genes could be useful in improving of tolerance to abiotic stresses as an important transcriptional activators in rice.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of Nutrition and Health
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• v.48 no.3
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• pp.258-268
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• 2015

Purpose: The aim of this study was to explore the relationship of ready-to-eat cereal (RTEC) consumption with nutrition and health status. Examination of health status for this project included obesity, abdominal obesity, hypertension, hypertriglyceridemia, hypercholesterolemia, low-HDL-cholesterolemia, diabetes, anemia, and metabolic syndrome. Methods: Two groups, RTEC consumers and those who did not consume RTEC, were identified using 24-hour dietary recall data from the 2012 Korea National Health and Nutrition Examination Survey (KNHANES). Nutritional intakes and risk factors of the two groups were compared using covariates-adjusted statistical procedures. Statistical analyses were performed using SAS survey procedures, and strata, cluster, and weight were considered. Subjects of analysis of nutritional intake were between the ages of 1 and 75, and those considered in the risk factor analysis were between the ages of 19 and 75. Results: Results showed that 3.8% of the Korean population was RTEC consumers. Compared to the subjects who did not intake RTEC, RTEC consumers exhibited significantly higher intakes of calcium, thiamin, riboflavin, and vitamin C. It was also discovered that the percentage of people whose intakes were less than EAR decreased with RTEC consumption. RTEC consumption showed significant association with decreased systolic blood pressure, diastolic blood pressure, serum triglyceride, and serum total cholesterol. Consequently, prevalence of hypertension among RTEC consumers was significantly lower than that among non-consumers, and the odds ratio for hypertension was 0.19 after adjusting the models for covariates. Conclusion: Results of this study clearly suggest an association of RTEC consumption with improved nutritional status and cardiometabolic risk profile in Korean adults. Conduct of additional studies will be necessary in order to determine the nature of these relationships.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Asian Journal of Turfgrass Science
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• v.26 no.1
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• pp.24-34
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• 2012

The study was carried out to investigate the effects of polymer, calcium, perlite and chitosan on the growth of perennial ryegrass (Lolium perenne L., PR) and to provide a basic information needed for their practical application when establishing garden, parks, athletic field and golf courses with these materials. A total of 24 treatment combinations were applied in the study. Treatments were made of water-swelling polymer (WSP), calcium, perlite and chitosan mixed in soil organic amendment (SOA). Germination rate, turfgrass coverage, turfgrass density and top growth were evaluated in PR under greenhouse conditions. Significant differences were observed for these growth characteristics among the treatments. Turfgrass density and plant height, evaluated on a weekly basis, varied with time after seeding. A proper mixing rate of WSP was considered to be lower 3% for the growth of PR with an exception of being below 6% for turfgrass density. Germination rate and early survival capacity were greatly influenced by calcium and chitosan among the elements of calcium, perlite, and chitosan. But there was little effect by perlite. Calcium and chitosan were most effective one for turfgrass density and coverage, respectively. Top leaf-growth was influenced by all three elements, but the greatest effect was highly linked with calcium. Chitosan was very effective in early germination and vertical leaf growth, as compared with the others. Future studies are required for measuring the effect of WSP, calcium, perlite and chitosan on the turf growth characteristics in root zone mixtures of sand+SOA before a practical field use.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.9
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• pp.613-623
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• 2013

This study is purposed to evaluate the airborne asbestos concentrations in life environment surroundings in Seoul. In study, we investigated airborne asbestos concentrations in thirteen subway stations, four monitoring networks and each vicinity roadside, six stream surroundings, four tunnels quarterly and we also investigated relationship between the airborne asbestos concentrations and ambient temperature in monitoring networks and time-based airborne asbestos concentration variability for two typical monitoring networks, two subway stations transferred and used by lots of people through Phase Contrast Microscopy (PCM) and Transmission Electron Microscopy (TEM). The airborne asbestos concentrations by PCM for 4 objects of study were less than the detection limit (7 fiber/$mm^2$) in 111 (50%) out of 223 samples. The highest concentration was 0.0130 f/cc. But additional TEM analysis result for samples exceeding the guideline value for indoor air quality (0.01 f/cc) proposed by the Ministry of Environment (Korea), no asbestos was detected. Similarly TEM analysis result for 124 samples, no asbestos was detected. The average airborne asbestos concentrations by PCM in subway stations, monitoring networks, streams and tunnels were $0.0041{\pm}0.0027$ f/cc, $0.0015{\pm}0.0011$ f/cc, $0.0024{\pm}0.0012$ f/cc and $0.0016{\pm}0.0020$ f/cc. All objects of study were satisfied with the guideline value for indoor air quality. The relationship between the airborne asbestos concentrations and ambient temperature in monitoring networks was generally positive correlation (r = 0.660). The higher ambient temperature was and the more transient population was, the airborne asbestos concentrations by time for two subway stations were increased. While the airborne asbestos concentrations for two monitoring networks showed no variation pattern according to time.

• Journal of Life Science
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• v.18 no.12
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• pp.1733-1738
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• 2008

SLC6A18, one of the neurotransmitters, was reported the possible relationship to hypertension, and it contained eight blocks of minisatellites. In this study, SLC6A18-MS5 sequence which showed the highest heterozygosity among seven minisatellites was analyzed using the Transfac software, the putative binding sites for the transcription factor Pax4 and HNF4 were discovered as a result. The HNF4 is involved in the diabetes pathway and suggested the relationship to hypertension. Thus, we investigated the putative functional significance of allelic variation in this minisatellites with respect to susceptibility for hypertension. To address this possibility, we analyzed genomic DNA from the blood of 301 hypertension-free controls and 184 cases with hypertension. A statistically significant association was not identified between the allelic distribution of SLC6A18-MS5 and occurrence of hypertension. We then examined the meiotic segregation of SLC6A18-MS5 and it was transmitted following Mendelian inheritance. Therefore, this locus could be useful markers for paternity mapping and DNA fingerprinting. Moreover, we undertook a comprehensive analysis of the genomic sequence to address the evolutionary events of these variable repeats. SLC6A18 minisatellites regions are only conserved in human and primates. This result suggestedthat intronic minisatellites analysis is powerful evolution marker for the non-coding regions in primates and can provide a great insight to the molecular evolution of repeated region in primates.