• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.531-539
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• 2011

It has been widely acknowledged that climate system associated with extreme rainfall events was difficult to understand and extreme rainfall simulation in climate model was more difficult. This study developed a new model for extracting rainfall filed associated with extreme events as a way to characterize large scale climate system. Main interests are to derive location, size and direction of the rainfall field and this study developed an algorithm to extract the above characteristics from global climate data set. This study mainly utilized specific humidity and wind vectors driven by NCEP reanalysis data to define the rainfall field. Geometric first and second moments have been extensively employed in defining the rainfall field in selected zone, and an ellipsoid based model were finally introduced. The proposed geometric moments based ellipsoid model works equally well with regularly and irregularly distributed synthetic grid data. Finally, the proposed model was applied to space-time real rainfall filed. It was found that location, size and direction of the rainfall field was successfully extracted.

• 한국해양학회지
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• v.28 no.3
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• pp.241-258
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• 1993

To investigate the sedimentary environments on the coastal areas of Imja to Nakweol islands which show very complex shorelines and several zonal sand ridges in NE-SW direction, southwestern coast of Korea, a total of forty samples were taken by a grab sampler and sedimentological and micropaleontological studies on those samples were carried out. The present study area are classified into muddy sandy gravel, sand, city sand, sandy silt and silt facies. With statistical moment parameters such as mean, sorting, skewness and ketosis for the sediments in the study area, the sediments are generally categorized as shallow sedimentary facies. The characters that are observed in quartz grains among the sandy sediments of the study area imply the existence of high energy environments, temporal exposures in atmosphere and the mixing of clastic sediments of the several different origins. In the sediments of the study area, one genera belonging to six silicoflagellata species and five genera belonging to five nannoplankton species were detected. On the basis of the micro-organism assemblage, the study area seems to be influenced by active reworking dominantly under warm water masses. In addition, organic matter ad carbonate contents in the sediments did not show a definite relation with the occurrences of the micro-organisms in the study area.

• Geotechnical Engineering
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• v.3 no.4
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• pp.41-54
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• 1987

A stochastic numerical analysis for predicting the groundswater fluctuations in hillside slopes is performed in this paper to account for the uncertainties associated with the rainfall and site characteristics. The effect of spatial variabilities of aquifer parameters and the effect of temporal variability of recharge on the groundwater fluctuations are studied in depth. The Kriging is used to account for the spatial tariabilities of aquifer parameters. This technique prolevides the best linear unbiased estimator of a parameter and its minimum variance from a litsitem number of measured data. A stochastic one-dimensional numerical model is delreloped b) combining the groundwater flow model, the Kriging, and the first-order second-moment analysis. In addition, a two dimensional detelministic groundwater model is developed to study the change of ground water surfas in the transverse direction as well as in the downslope direction. It is revealed that the undulations of the impervious bedrock in addition to the permeability and the specific yield have an important influence on the fluctuations of the groundwater surface. It is also found that th'e groundwater changes significantly in the transverse direction as well as in the downslope direction. The results obtained in this analysis may be used for evaluation of landslide risks due to high porewater pressure.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.165-176
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• 2011

The femoral nerve innervates the quadriceps muscles and its dermatome supplies anteromedial thigh and medial foot. Paralysis of the quadriceps muscles due to the injury of the femoral nerve results in disability of the knee joint extension and loss of sensory of the thigh. A child could walk independently even though he had injured his femoral nerve severely due to the penetrating wound in the medial thigh. We measured and analyzed his gait performance in order to find the mechanisms that enabled him to walk independently. The child was eleven-year-old boy and he could not extend his knee voluntarily at all during a month after the injury. His gait analysis was performed five times (GA1~GA5) for sixteen months. His temporal-spatial parameters were not significantly different after the GA2 or GA3 test, and significant asymmetry was not observed except the single support time in GA1 results. The Lower limb joint angles in affected side had large differences in GA1 compared with the normal normative patterns. There were little knee joint flexion and extension motion during the stance phase in GA1 The maximum ankle plantar/dorsi flexion angles and the maximum knee extension angles were different from the normal values in the sound side. Asymmetries of the joint angles were analyzed by using the peak values. Significant asymmetries were found in GA1with seven parameters (ankle: peak planter flexion angle in stance phase, range of motion; ROM, knee: peak flexion angles during both stance and swing phase, ROM, hip: peak extension angle, ROM) while only two parameters (maximum hip extension angle and ROM of hip joint) had significant differences in GA5. The mid-stance valleys were not observed in both right and left sides of vertical ground reaction force (GRF) in the GA1, GA2. The loading response peak was far larger than the terminal stance peak of vertical ground reaction curve in the affected side of the GA3, GA4, GA5. The measured joint moment curves of the GA1, GA2, GA3 had large deviations and all of kinetic results had differences with the normal patterns. EMG signals described an absence of the rectus femoris muscle activity in the GA1 and GA2 (affected side). The EMG signals were detected in the GA3 and GA4 but their patterns were not normal yet, then their normal patterns were detected in the GA5. Through these following gait analysis of a child who had selective injuries on the knee extensor muscles, we could verify the actual functions of the knee extensor muscles during gait, and we also could observe his recovery and asymmetry with quantitative data during his rehabilitation.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.447-459
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• 2022

This study estimated the grid-type precipitation quantile for the Korean Peninsula using PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record), a satellite based re-analysis precipitation data. The period considered is a total of 38 years from 1983 to 2020. The spatial resolution of the data is 0.04° and the temporal resolution is 3 hours. For the probability distribution, the Gumbel distribution which is generally used for frequency analysis was used, and the probability weighted moment method was applied to estimate parameters. The duration ranged from 3 hours to 144 hours, and the return period from 2 years to 500 years was considered. The results were compared and reviewed with the estimated precipitation quantile using precipitation data from the Automated Synoptic Observing System (ASOS) weather station. As a result, the parameter estimates of the Gumbel distribution from the PERSIANN-CCS-CDR showed a similar pattern to the results of the ASOS as the duration increased, and the estimates of precipitation quantiles showed a rather large difference when the duration was short. However, when the duration was 18 h or longer, the difference decreased to less than about 20%. In addition, the difference between results of the South and North Korea was examined, it was confirmed that the location parameters among parameters of the Gumbel distribution was markedly different. As the duration increased, the precipitation quantile in North Korea was relatively smaller than those in South Korea, and it was 84% of that of South Korea for a duration of 3 h, and 70-75% of that of South Korea for a duration of 144 h.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.