• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.

• 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.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.77-91
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• 2008

In this study, we developed a technique of applying DRASTIC, which is the most widely used tool for estimation of groundwater vulnerability to the aqueous phase contaminant infiltrated from the surface, and a groundwater flow model jointly to assess groundwater contamination potential. The developed technique is then applied to Buyeo-eup area in Buyeo-gun, Chungcheongnam-do, Korea. The input thematic data of a depth to water required in DRASTIC model is known to be the most sensitive to the output while only a few observations at a few time schedules are generally available. To overcome this practical shortcoming, both steady-state and transient groundwater level distributions are simulated using a finite difference numerical model, MODFLOW. In the application for the assessment of groundwater vulnerability, it is found that the vulnerability results from the numerical simulation of a groundwater level is much more practical compared to cokriging methods. Those advantages are, first, the results from the simulation enable a practitioner to see the temporally comprehensive vulnerabilities. The second merit of the technique is that the method considers wide variety of engaging data such as field-observed hydrogeologic parameters as well as geographic relief. The depth to water generated through geostatistical methods in the conventional method is unable to incorporate temporally variable data, that is, the seasonal variation of a recharge rate. As a result, we found that the vulnerability out of both the geostatistical method and the steady-state groundwater flow simulation are in similar patterns. By applying the transient simulation results to DRASTIC model, we also found that the vulnerability shows sharp seasonal variation due to the change of groundwater recharge. The change of the vulnerability is found to be most peculiar during summer with the highest recharge rate and winter with the lowest. Our research indicates that numerical modeling can be a useful tool for temporal as well as spatial interpolation of the depth to water when the number of the observed data is inadequate for the vulnerability assessments through the conventional techniques.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.86-90
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• 2012

Purpose: Quantification of myocardial blood flow (MBF) using dynamic PET imaging has the potential to assess coronary artery disease. Rb-82 plays a key role in the clinical assessment of myocardial perfusion using PET. However, MBF could be overestimated due to the underestimation of left ventricular input function in the beginning of the acquisition when the scanner has non-linearity between count rate and activity concentration due to the scanner dead-time. Therefore, in this study, we evaluated the count rate linearity as a function of the activity concentration in PET data acquired in list mode. Materials & methods: A cylindrical phantom (diameter, 12 cm length, 10.5 cm) filled with 296 MBq F-18 solution and 800 mL of water was used to estimate the linearity of the Biograph 40 True Point PET/CT scanner. PET data was acquired with 10 min per frame of 1 bed duration in list mode for different activity concentration levels in 7 half-lives. The images were reconstructed by OSEM and FBP algorithms. Prompt, net true and random counts of PET data according to the activity concentration were measured. Total and background counts were measured by drawing ROI on the phantom images and linearity was measured using background correction. Results: The prompt count rates in list mode were linearly increased proportionally to the activity concentration. At a low activity concentration (<30 kBq/mL), the prompt net true and random count rates were increased with the activity concentration. At a high activity concentration (>30 kBq/mL), the increasing rate of the prompt net true rates was slightly decreased while the increasing rate of random counts was increased. There was no difference in the image intensity linearity between OSEM and FBP algorithms. Conclusion: The Biograph 40 True Point PET/CT scanner showed good linearity of count rate even at a high activity concentration (~370 kBq/mL).The result indicates that the scanner is useful for the quantitative analysis of data in heart dynamic studies using Rb-82, N-13, O-15 and F-18.

• Journal of Animal Science and Technology
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• v.44 no.4
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• pp.459-474
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• 2002

The purpose of this study was to establish the optimal operation condition of the natural evaporation system(NES) which was used for reducing swine slurry. Especially the main point of this study is to estimate the effect of climate condition(clear & rainy) and spray type(batch & flow) for the evaporation rate of swine slurry applying the NES in summer. Experiment was performed from June to August, which was generally regarded as summer in Korea, with the spray type of batch in 2000 and that of flow in 2001. As a result of experiment for batch and flow type, the averaged evaporation rate was measured into 2.71 and 3.59 l/ton . $m^2$ . day on clear days and 0.62 and 0.66 l/ton . $m^2$ . day on raint days, respectively. Based on the calculated evaporation rate by the climate condition and the spray type, it was proved that the averaged reduction rate for total input(1t/day) were 15.99% and 3.19% on clear and rainy days and the evaporation rate of the flow type was superior to that of the batch type by 5%, approximately. Therefore, it was concluded that the supplementary equipment, such as fan, should by operated in rainy days and the spray type of flow rather than that of batch should be recommended to increase the evaporation rate in the natural evaporation system(NES).

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.279-287
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• 2020

Recently, interest in radiation protection is increasing because of the occurrence of accidents related to exposure dose. So, the nuclear safety act provides to install the shields to avoid exceeding the dose limit. In particular, when the worker conducts the non-destructive testing (NDT) without the fixed shielding structure, we should monitor the access to the workplace based on a constant dose rate. However, when we apply for permits for NDT work in these work environments, the consideration factors to the estimation of the distance and exposure dose are not legally specified. Therefore, we developed the excel model that automatically calculates the distance, exposure dose, and cost if we input the factors. We applied the assumption data to this model. As a result of the application, the distance change rate was low when the thickness of the lead blanket and collimator is above 25 mm, 21.5 mm, respectively. However, we didn't consider the scattering and build-up factor. And, we assumed the shape of the lead blanket and collimator. Therefore, if we make up for these limitations and use the actual data, we expect that we can build a database on the distance and exposure dose.

• Journal of Korean Society of Forest Science
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• v.98 no.6
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• pp.791-798
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• 2009

The role of forest and soil carbon under global climate change is getting important as a carbon sink and it is necessary to research on applicable forest models as well as in the field for a study of these dynamics. On this study, historical annual litter dataset as a major input data for the forest soil carbon model, Yasso was established using a dendrochronological reconstruction method, and the soil carbon dynamics of a Pinus densiflora forest in Gwangneung, Korea was simulated using Yasso. The amount of litter (needle, branch, stem and fine root) production, which was estimated using the dendrochronological method, has increased continuously from 1971 to 2006. Furthermore, there was no significant error between estimated and measured values of litter production (needle and branch) in 2006. The average of simulated soil carbon stock up to 30 cm depth was $46.30{\pm}4.28tCha^{-1}$, which accounted for 53% of carbon stock in trees of the forest, and had no significant difference and error with measured soil carbon stock. Under the climate change trend in Korea according to IPCC A1B scenario, it was estimated that the simulated soil carbon stock in the region would increase continuously from 1971 to 2041 and then decreased until 2100. Compared to the result of the scenario that there is no climate change, the soil carbon stock could be decreased up to 7.58% at 2100. It was inferred the dendrochronological reconstruction method and simulation of Yasso model are useful to estimate soil carbon dynamics of the natural P. densiflora forest. Follow-up researches, such as improvement of the dendrochronological method and Yasso model and their application and validation in various environment, are needed to produce more reliable results.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.481-494
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• 2011

Investigation of the $CO_2$ exchange between biosphere and atmosphere at regional, continental, and global scales can be directed to combining remote sensing with carbon cycle process to estimate vegetation productivity. NASA Earth Observing System (EOS) currently produces a regular global estimate of gross primary productivity (GPP) and annual net primary productivity (NPP) of the entire terrestrial earth surface at 1 km spatial resolution. While the MODIS GPP algorithm uses meteorological data provided by the NASA Data Assimilation Office (DAO), the sub-pixel heterogeneity or complex terrain are generally reflected due to coarse spatial resolutions of the DAO data (a resolution of $1{\circ}\;{\times}\;1.25{\circ}$). In this study, we estimated inputs retrieved from MODIS products of the AQUA and TERRA satellites with 5 km spatial resolution for the purpose of finer GPP and/or NPP determinations. The derivatives included temperature, VPD, and solar radiation. Seven AmeriFlux data located in the Corn Belt region were obtained to use for evaluation of the input data from MODIS. MODIS-derived air temperature values showed a good agreement with ground-based observations. The mean error (ME) and coefficient of correlation (R) ranged from $-0.9^{\circ}C$ to $+5.2^{\circ}C$ and from 0.83 to 0.98, respectively. VPD somewhat coarsely agreed with tower observations (ME = -183.8 Pa ~ +382.1 Pa; R = 0.51 ~ 0.92). While MODIS-derived shortwave radiation showed a good correlation with observations, it was slightly overestimated (ME = -0.4 MJ $day^{-1}$ ~ +7.9 MJ $day^{-1}$; R = 0.67 ~ 0.97). Our results indicate that the use of inputs derived MODIS atmosphere and land products can provide a useful tool for estimating crop GPP.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.317-325
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• 2007

Purpose: Biological parameters can be quantified using dynamic PET data with compartment modeling and Nonlinear Least Square (NLS) estimation. However, the generation of parametric images using the NLS is not appropriate because of the initial value problem and excessive computation time. In irreversible model, Patlak graphical analysis (PGA) has been commonly used as an alternative to the NLS method. In PGA, however, the start time ($t^*$, time where linear phase starts) has to be determined. In this study, we suggest a new Multiple Linear Analysis for irreversible radiotracer (MLAIR) to estimate fluoride bone influx rate (Ki). Methods: $[^{18}F]Fluoride$ dynamic PET scans was acquired for 60 min in three normal mini-pigs. The plasma input curve was derived using blood sampling from the femoral artery. Tissue time-activity curves were measured by drawing region of interests (ROls) on the femur head, vertebra, and muscle. Parametric images of Ki were generated using MLAIR and PGA methods. Result: In ROI analysis, estimated Ki values using MLAIR and PGA method was slightly higher than those of NLS, but the results of MLAIR and PGA were equivalent. Patlak slopes (Ki) were changed with different $t^*$ in low uptake region. Compared with PGA, the quality of parametric image was considerably improved using new method. Conclusion: The results showed that the MLAIR was efficient and robust method for the generation of Ki parametric image from $[^{18}F]Fluoride$ PET. It will be also a good alternative to PGA for the radiotracers with irreversible three compartment model.