• Development and Reproduction
• /
• v.16 no.1
• /
• pp.53-58
• /
• 2012

Circadian rhythmicity (e.g. secretory pattern of hormones) plays an important role in the control of reproductive function. We hypothesized that the alteration of feeding pattern via meal time shift/restriction might disrupt circadian rhythms in energy balance, and induce changes in reproductive activities. To test this hypothesis, we employed simple animal model that not allowing $ad$ $libitum$ feeding but daytime only feeding. The animals of $ad$ $libitum$ feeding group (Control) have free access to food for 4 weeks. The day feeding (=reverse feeding, RF) animals (RF group) have restricted access to food during daytime (0900-1800) for 4 weeks. After completing the feeding schedules, body weights, testis and epididymis weights of animals from both group were not significantly different. However, the weights of seminal vesicle (control : RF group = $0.233{\pm}0.014g$ : $0.188{\pm}0.009g$, $p$<0.01) and prostate (control : RF group = $0.358{\pm}0.015g$ : $0.259{\pm}0.015g$, $p$<0.001) were significantly lower in RF group animals. The mRNA levels of pituitary common alpha subunit ($C{\alpha}$; control : RF group = $1.0{\pm}0.0699$ AU : $0.1923{\pm}0.0270$ AU, $p$<0.001) and $FSH{\beta}$ (control : RF group = $1.0{\pm}0.1489$ AU : $0.5237{\pm}0.1088$ AU, $p$<0.05) were significantly decreased in RF group. The mRNA levels of ACTH were not significantly different. We were unable to find any prominent difference in the microstructures of epididymis, and there were slight alterations in those of seminal vesicles after 4 weeks of reversed feeding when compared to control samples. The present study demonstrates that the shift and/or restriction of feeding time could alter the pituitary gonadotropin expression and the weights of seminal vesicle and prostate in rats. These data suggest the lowered gonadotropin inputs may decrease androgen secretion form testis, and consequently results in poor response of androgen-dependent tissues such as seminal vesicle and prostate.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.1D
• /
• pp.89-94
• /
• 2012

The RFM (Rational Function Model), as an alternative to physical sensor models has been widely used for photogrammetric processing of high resolution optical satellite imagery. However, the application of RF modeling to the SAR (Synthetic Aperture Radar) is very limited. In this paper, stereo radargrammetric processing of TerraSAR-X stereo pairs with RFM is implemented and analyzed. The investigation has shown that the accuracy of TerraSAR-X DSM is similar to that of the commercial S/W product. Finally, it is demonstrated that RFM is effective and feasible in the application to the radargrammetric SAR image processing.

• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.4
• /
• pp.259-275
• /
• 2021

This study explored the usefulness and implications of the Bayesian hyperparameter optimization in developing species distribution models (SDMs). A variety of machine learning (ML) algorithms, namely, support vector machine (SVM), random forest (RF), boosted regression tree (BRT), XGBoost (XGB), and Multilayer perceptron (MLP) were used for predicting the occurrence of four benthic macroinvertebrate species. The Bayesian optimization method successfully tuned model hyperparameters, with all ML models resulting an area under the curve (AUC) > 0.7. Also, hyperparameter search ranges that generally clustered around the optimal values suggest the efficiency of the Bayesian optimization in finding optimal sets of hyperparameters. Tree based ensemble algorithms (BRT, RF, and XGB) tended to show higher performances than SVM and MLP. Important hyperparameters and optimal values differed by species and ML model, indicating the necessity of hyperparameter tuning for improving individual model performances. The optimization results demonstrate that for all macroinvertebrate species SVM and RF required fewer numbers of trials until obtaining optimal hyperparameter sets, leading to reduced computational cost compared to other ML algorithms. The results of this study suggest that the Bayesian optimization is an efficient method for hyperparameter optimization of machine learning algorithms.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.134-134
• /
• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.156-156
• /
• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• Journal of the Society of Disaster Information
• /
• v.19 no.4
• /
• pp.924-935
• /
• 2023

Purpose: The central aim of this study is to leverage machine learning techniques for the classification of Intrusion Detection System (IDS) data, with a specific focus on identifying the variables responsible for enhancing overall performance. Method: First, we classified 'R2L(Remote to Local)' and 'U2R (User to Root)' attacks in the NSL-KDD dataset, which are difficult to detect due to class imbalance, using seven machine learning models, including Logistic Regression (LR) and K-Nearest Neighbor (KNN). Next, we use the SHapley Additive exPlanation (SHAP) for two classification models that showed high performance, Random Forest (RF) and Light Gradient-Boosting Machine (LGBM), to check the importance of variables that affect classification for each model. Result: In the case of RF, the 'service' variable and in the case of LGBM, the 'dst_host_srv_count' variable were confirmed to be the most important variables. These pivotal variables serve as key factors capable of enhancing performance in the context of classification for each respective model. Conclusion: In conclusion, this paper successfully identifies the optimal models, RF and LGBM, for classifying 'R2L' and 'U2R' attacks, while elucidating the crucial variables associated with each selected model.

• Proceedings of the IEEK Conference
• /
• 2004.06b
• /
• pp.347-350
• /
• 2004

The substrate parameters of Si MOSFET equivalent circuit model were directly extracted from measured S-Parameters in the GHz region by using simple 2-port parameter equations. Using the above extract ion method, bias and gate length dependent curves of substrate parameters in the RF region are obtained by varying drain voltage at several short channel devices with various gate lengths. These extract ion data will greatly contribute to scalable RF nonlinear substrate modeling.

• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.809-812
• /
• 2005

In this work, a new method for extracting substrate parameters of RF MOSFETs based on 3-port measurement is presented using device simulation. A T-type substrate resistance network is used. 3-port Y-parameter analyses were performed on the equivalent circuit of RF MOSFETs. All the components in the RF MOSFETs when the device is turned off were extracted directly from the 3-port device simulation data. The small-signal output admittance $Y_{22}$ can be well modeled up to 40 GHz. From the 3-port simulation and modeling results, it was verified that the proposed equivalent circuit and parameter extraction method was more accurate than the single substrate resistance model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.04a
• /
• pp.435-439
• /
• 2007

This paper deals with RLS algorithm using Newton-Raphson method based adaptive forgetting factor for a passive telemetry RF sensor system in order to estimate the time variant parameter to be included in RF sensor model. For this estimation with RLS algorithm, phasor typed RF sensor system modelled with inductive coupling principle is used. Instead of applying constant forgetting factor to estimate time variant parameter, the adaptive forgetting factor based on Newton-Raphson method is applied to RLS algorithm without constant forgetting factor to be determined intuitively. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

• Genomics & Informatics
• /
• v.5 no.4
• /
• pp.168-173
• /
• 2007

In previous nuclear genomic association studies, Random Forests (RF), one of several up-to-date machine learning methods, has been used successfully to generate evidence of association of genetic polymorphisms with diseases or other phenotypes. Compared with traditional statistical analytic methods, such as chi-square tests or logistic regression models, the RF method has advantages in handling large numbers of predictor variables and examining gene-gene interactions without a specific model. Here, we applied the RF method to find the association between mitochondrial single nucleotide polymorphisms (mtSNPs) and diabetes risk. The results from a chi-square test validated the usage of RF for association studies using mtDNA. Indexes of important variables such as the Gini index and mean decrease in accuracy index performed well compared with chi-square tests in favor of finding mtSNPs associated with a real disease example, type 2 diabetes.