• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.2032-2037
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• 2011

Marker genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect marker genes that are selected by ranking genes according to statistics after normalizing data with methods that are the most widely used among several normalization methods proposed the while, And it compare and analyze a performance of each of normalization methods with mult-perceptron neural network layer. The Result that apply Multi-Layer perceptron algorithm at Microarray data set including eight of marker gene that are selected using ANOVA method after Lowess normalization represent the highest classification accuracy of 99.32% and the lowest prediction error estimate.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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• pp.15-26
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• 2020

There are fifteen non-point pollution management areas in Korea and three of them (Doam lake, Daegi district and Golji-cheon) are located in the upstream of the Namhan river watershed. Many efforts to reduce non-point sources (NPS) pollution have been conducted, however, water quality pollution in the watershed is still serious. To solve these problems, it is a priority to grasp water quality using statistical techniques. In this study, a trend analysis was conducted to evaluate the effect of NPS management in the watershed. The long-term trends from 1996 to 2018 of water quality properties were analyzed using data collected from the water environment information system. Seventeen monitoring stations were selected along the main stream in Namhan river basin. Monthly water quality properties (BOD, COD, TN, TP, TN/TP ratio, Conductivity, SS and Chlorophyll-a) were collected and analyzed by Mann-Kendall test and LOWESS. The results showed that the Conductivity tended to increase in all regions and was the highest level in Jijangcheon. Organic pollution such as BOD and COD tended to increase in the Jungseon area. SS did not show a large tendency, but it showed high concentration in the Doam watershed. In all regions, 40% of water quality properties showed a tendency to 'UP', 15% of water quality properties tended to 'DOWN', and 46% indicated no tendency. In order to determine the cause of this, additional research and measures for improvement are necessary. This study will be used for the establishment of water quality policy in the future.

• Journal of Environmental Science International
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• v.27 no.2
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• pp.63-74
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• 2018

As part of the Four Major Rivers Restoration Project, multifunctional weirs have been constructed in the rivers and operated for river-level management. As the weirs play a role in draining water from tributaries, the aim of this study was to determine the influence of the weirs on the water level of the Nam River, which is one of the Nakdong River's tributaries. Self-organizing maps (SOMs) and a locally weighted scatterplot smoothing (LOWESS) technique were applied to analyze the patterns and trends of water level and quality of the Nakdong River, considering the operation of the Changnyeong-Haman weir, which is located where the Nam River flows into the Nakdong River. The software program HEC-RAS was used to find the boundary points where the water is well drained. Per the study results at the monitoring points ranging between the junction of the two rivers and 17.5 km upstream toward the Nam River, the multifunctional weir influenced the water level at the Geoyrong and Daesan observation stations on the Nam River and the water quality based on automatic monitoring at the Chilseo station on the Nakdong River was affected strongly by the Nakdong River and partly by the Nam River.

• Korean Journal of Agricultural Science
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• v.41 no.3
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• pp.169-175
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• 2014

Objective of this study was to investigate effects of pre-processing method and number of sampling leaves on stability of the reflectance measurement for Chinese cabbage and kale leaves. Chinese cabbage and kale were transplanted and cultivated in a plant factory. Leaf samples of the kale and cabbage were collected at 4 weeks after transplanting of the seedlings. Spectra data were collected with an UV/VIS/NIR spectrometer in the wavelength region from 190 to 1130 nm. All leaves (mature and young leaves) were measured on 9 and 12 points in the blade part in the upper area for kale and cabbage leaves, respectively. To reduce the spectral noise, the raw spectral data were preprocessed by different methods: i) moving average, ii) Savitzky-Golay filter, iii) local regression using weighted linear least squares and a $1^{st}$ degree polynomial model (lowess), iv) local regression using weighted linear least squares and a $2^{nd}$ degree polynomial model (loess), v) a robust version of 'lowess', vi) a robust version of 'loess', with 7, 11, 15 smoothing points. Effects of number of sampling leaves were investigated by reflectance difference (RD) and cross-correlation (CC) methods. Results indicated that the contribution of the spectral data collected at 4 sampling leaves were good for both of the crops for reflectance measurement that does not change stability of measurement much. Furthermore, moving average method with 11 smoothing points was believed to provide reliable pre-processed data for further analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2039-2044
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• 2006

Since the bio-information now even exceeds the capability of human brain, the techniques of data mining and artificial intelligent are needed to deal with the information in this field. There are many researches about using DNA microarray technique which can obtain information from thousands of genes at once, for developing new methods of analyzing and predicting of diseases. Discovering the mechanisms of unknown genes by using these new method is expecting to develop the new drugs and new curing methods. In this Paper, We tested accuracy on classification of microarray in Bayesian method to compare normalization method's Performance after dividing data in two class that is a feature abstraction method through a normalization process which reduce or remove noise generating in microarray experiment by various factors. And We represented that it improve classification performance in 95.89% after Lowess normalization.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.2
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• pp.25-34
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• 2024

The long-term monitoring of the Soyang Lake's water quality, covering 25% of the North Han River watershed, is crucial for effective management of both lake water quality and pollution sources in the broader region. This study utilized continuous monitoring data from the front of the Soyang Dam spanning 2003 to 2022, aiming to analyze trends and provide foundational insights for water quality management. Results revealed a slightly poor grade (IV) for total nitrogen (T-N) in both surface and mid-depth layers, indicating a need for concentrated T-N management. Trend analyses using the Mann-Kendall test and Sen's Slope depicted a decreasing trend in total phosphorus (T-P) for both layers, attributed to non-point source pollution reduction projects initiated after the Soyang Lake's designation as a pollution control area in 2007. The LOWESS analysis showed a T-P increase until 2006, followed by a decrease, influenced by the impact of Typhoon Ewiniar in that year. This 20-year overview establishes a comprehensive understanding of the Soyang Lake's water quality and trends, allowing for a seasonal and periodical analysis of water quality changes. The findings underscore the importance of continued monitoring and management strategies to address evolving water quality issues in the Soyang Lake over time.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1005-1021
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• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.

• Molecules and Cells
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• v.22 no.3
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• pp.254-261
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• 2006

DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray experiments. Normalization is a critical step for obtaining data that are reliable and usable for subsequent analysis such as identification of differentially expressed genes and clustering. A variety of normalization methods have been proposed over the past few years, but no methods are still perfect. Various assumptions are often taken in the process of normalization. Therefore, the knowledge of underlying assumption and principle of normalization would be helpful for the correct analysis of microarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing on their principles and assumptions.

• Journal of Korea Water Resources Association
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• v.38 no.8 s.157
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• pp.617-629
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• 2005

In this paper, a hypothesis is tested that division of non-monotonic time series into monotonic parts will improve the estimation of trends through increased homogeneity in direction of time-variation using LOWESS smoothing and seasonal Kendall test. From the trend analysis of generated time series and water temperature, discharge, air temperature and solar radiation of Lake Daechung, it is shown that the hypothesis is supported by improved estimation of trends and slopes. Also, characteristics in homogeneity variation of seasonal changes seems to be more clearly manifested as homogeneity in direction of time-variation is increased. And this will help understand the effects of human intervention on natural processes and seems to warrant more in-depth study on this subject. The proposed method can be used for trend analysis to detect monotonic trends and it is expected to improve understanding of long-term changes in natural environment.

• Korean Journal of Ecology and Environment
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• v.42 no.1
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• pp.39-47
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• 2009

Spatial and temporal analysis of water quality was performed for eleven monitoring stations in Dongjin River watershed in order to determine the trends of monthly water quality. The monthly water quality data of biochemical oxygen demand (BOD), total nitrogen (TN) and total phosphorus (TP) during $1995{\sim}2004$, were analyzed utilizing Seasonal Mann-Kendall test, LOWESS and three-dimensional graphic approaches. The results indicated that BOD and TN concentrations had the downward trend, but TP showed the upward trend, especially in Gobucheon. This numerical and graphic analysis is the useful tool to analyze the long-term trend of water quality in a large river system.