• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.9
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• pp.749-757
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• 2021

In transportation missions using quadrotor, the payload may change the model parameters, such as mass, moment of inertia, and center of gravity. Moreover, if position of the payload is constantly changing during flight, the effect can adversely affect the control performances. To handle this issue, we suggest Model Reference Adaptive Control based on Linear Quadratic Regulator(LQR+MRAC) to compensate the uncertainty caused by payload. Firstly, the mathematical modeling with the fixed payload is derived. Second, Linear Quadratic Regulator (LQR) is used to design the reference model and baseline controller. Also, through the Stability method, Adaptive law is derived to estimate the model parameters. To verify the performance of proposed control scheme, we compared LQR and LQR+MRAC in situations where uncertainties exist. And, when the disturbance exist, the classic MRAC and proposed controller is compared to analyze the transient response and robustness.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.261-268
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• 2015

In spoken chatting systems, users'spoken queries are converted to text queries using automatic speech recognition (ASR) engines. If the top-1 results of the ASR engines are incorrect, these errors are propagated to the spoken chatting systems. To improve the top-1 accuracies of ASR engines, we propose a post-processing model to rearrange the top-n outputs of ASR engines using a ranking support vector machine (RankSVM). On the other hand, a number of chatting sentences are needed to train chatting systems. If new chatting sentences are not frequently added to training data, responses of the chatting systems will be old-fashioned soon. To resolve this problem, we propose a data collection model to automatically select chatting sentences from TV and movie scenarios using a support vector machine (SVM). In the experiments, the post-processing model showed a higher precision of 4.4% and a higher recall rate of 6.4% compared to the baseline model (without post-processing). Then, the data collection model showed the high precision of 98.95% and the recall rate of 57.14%.

• Journal of KIISE
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• v.44 no.7
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• pp.742-747
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• 2017

In this paper, we propose a novel approach to improve the performance of the Convolutional Neural Network(CNN) word embedding model on top of word2vec with the result of performing like doc2vec in conducting a document classification task. The Word Piece Model(WPM) is empirically proven to outperform other tokenization methods such as the phrase unit, a part-of-speech tagger with substantial experimental evidence (classification rate: 79.5%). Further, we conducted an experiment to classify ten categories of news articles written in Korean by feeding words and document vectors generated by an application of WPM to the baseline and the proposed model. From the results of the experiment, we report the model we proposed showed a higher classification rate (89.88%) than its counterpart model (86.89%), achieving a 22.80% improvement. Throughout this research, it is demonstrated that applying doc2vec in the document classification task yields more effective results because doc2vec generates similar document vector representation for documents belonging to the same category.

• Journal of Astronomy and Space Sciences
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• v.39 no.4
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• pp.159-167
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• 2022

Because of the small number of spacecraft available in the Earth's magnetosphere at any given time, it is not possible to obtain direct measurements of the fundamental quantities, such as the magnetic field and plasma density, with a spatial coverage necessary for studying, global magnetospheric phenomena. In such cases, empirical as well as physics-based models are proven to be extremely valuable. This requires not only having high fidelity and high accuracy models, but also knowing the weakness and strength of such models. In this study, we assess the accuracy of the widely used Tsyganenko magnetic field models, T96, T01, and T04, by comparing the calculated magnetic field with the ones measured in-situ by the GOES satellites during geomagnetically disturbed times. We first set the baseline accuracy of the models from a data-model comparison during the intervals of geomagnetically quiet times. During quiet times, we find that all three models exhibit a systematic error of about 10% in the magnetic field magnitude, while the error in the field vector direction is on average less than 1%. We then assess the model accuracy by a data-model comparison during twelve geomagnetic storm events. We find that the errors in both the magnitude and the direction are well maintained at the quiet-time level throughout the storm phase, except during the main phase of the storms in which the largest error can reach 15% on average, and exceed well over 70% in the worst case. Interestingly, the largest error occurs not at the Dst minimum but 2-3 hours before the minimum. Finally, the T96 model has consistently underperformed compared to the other models, likely due to the lack of computation for the effects of ring current. However, the T96 and T01 models are accurate enough for most of the time except for highly disturbed periods.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.131-142
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• 2015

In order to support the development of a cost-effective river bathymetric system, this research has focused on modeling GPS observables, which are obtained by array of five single-frequency receivers (i.e., two references and three rovers) to estimate the high accurate kinematic position, and the surveying vessel altitude. Also, by applying all GPS measurements as multiple-baselines with constraining rover baselines, we derived the socalled ‘kinematic network model.’ From the model, the integer-constrained least-squares (LS) for position estimation and the implicit LS for attitude determination were implemented, while a series of simulation tests with respect to the baseline lengths around 2km performed to demonstrate its accuracy analysis. The on-the-fly (OTF) ambiguity resolution tests revealed that ninety-nine percents of time-to-fix-first ambiguity (TTFF) can be decided in less than two seconds, when the positioning accuracy of ambiguity-fixed solutions was assessed as the greater than or equal to one and two centimeters in horizontal and vertical, respectively. Comparing to the GPS-derived attitudes, the achievable accuracy gradually descended in sequence of yaw, pitch and roll due to the antenna geometric configuration. Furthermore, the RMSE values for the baseline lengths of three to six meters were within ±1′for yaw, and less than ±10′and ±20′for pitch and roll, respectively, but those of between six to fifteen meters were less than ±1′for yaw, ±5′for pitch, and ±10′for roll.

• Korean Journal of Biological Psychiatry
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• v.6 no.2
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• pp.193-201
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• 1999

This study was designed to evaluate the effects of opioid receptor agonists on the spontaneous alternation behaviour in an animal model of obsessivecompulsive disorder in rats. According to the theory that dopamine is related to the biological etiology of obsessive-compulsive disorder, the effect of the nalbuphine(opioid kappa agonist) and the tramadol(opioid mu agonist), which act as manipulating agents on the inhibition or stimulation of dopamine release, in the spontaneous alternation behaviour were evaluated. 24 hours prior to the experiment, rats were food-deprived. These rats were put into the T-maze, in which white and black goal boxes were baited with small amounts of chocolate milk. Each rat was given 2 set of 7 trials during which it was placed in the start box and allowed to choose the one of the goal boxes for each time. After identifying the stable baseline of spontaneous alternation behaviour, nonselective 5-HT agonist 5-MeODMT(1.25mg/kg/IP) disrupted spontaneous alternation. Rats were stratified into fluoxetine(10mg/kg/IP), nalbuphine(10mg/kg/IP), tramadol(46.4mg/kg/IP), and saline(0.5cc/IP) injection group with experimental drug treatment for 21 days. The effects on the 5-MeODMT(1.25mg/kg/IP) induced disruption of spontaneous alternation behaviour were checked at the next day of discontinuation of drug treatment. The results were as follows ; 1) At the day after 21 days of the drug treatment, the nalbuphine treated group and the fluoxetine treated group showed significant difference from the tramadol treated group and the saline treated group in the 5-MeODMT(1.25mg/kg/IP) induced suppression of spontaneous alternation behaviour. 2) Within each drug treatment group, the fluoxetine treated group showed significant difference between before and after the treatment of fluoxetine in the 5-MeODMT(1.25mg/kg/IP) induced suppression of spontaneous alternation behaviour. And also, the nalbuphine treated group showed significant difference between before and after the treatment of nalbuphine in the 5-MeODMT(1.25mg/kg/IP) induced suppression of spontaneous alternation behaviour. There was no difference between the baseline and after the treatment of nalbuphine in the 5-MeODMT(1.25mg/kg/IP) induced suppression of spontaneous alternation behaviour. We indentified that the opioid kappa agonist that act as dopamine release inhibitor affect the spontaneous alternation behaviour which is an animal model of obsessive-compulsive disorder in rat.

• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.65-76
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• 2015

The Clean Development Mechanism (CDM) is the multi-lateral 'cap and trade' system endorsed by the Kyoto Protocol. CDM allows developed (Annex I) countries to buy CER credits from New and Renewable (NE) projects of non-Annex countries, to meet their carbon reduction requirements. This in effect subsidizes and promotes NE projects in developing countries, ultimately reducing global greenhouse gases (GHG). To be registered as a CDM project, the project must prove 'additionality,' which depends on numerous factors including the adopted technology, baseline methodology, emission reductions, and the project's internal rate of return. This makes it difficult to determine ex ante a project's acceptance as a CDM approved project, and entails sunk costs and even project cancellation to its project stakeholders. Focusing on hydro power projects and employing UNFCCC public data, this research developed a prediction model using logistic regression and CART to determine the likelihood of approval as a CDM project. The AUC for the logistic regression and CART model was 0.7674 and 0.7231 respectively, which proves the model's prediction accuracy. More importantly, results indicate that the emission reduction amount, MW per hour, investment/Emission as crucial variables, whereas the baseline methodology and technology types were insignificant. This demonstrates that at least for hydro power projects, the specific technology is not as important as the amount of emission reductions and relatively small scale projects and investment to carbon reduction ratios.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.5
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• pp.27-43
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• 2019

This study applied the LANDIS-II model to the forest vegetation of the study area in Yeongdong-gun, Korea to identify climate effects on ecosystems of forest vegetation. The main purpose of the study is to examine the long-term changes in forest aboveground biomass(AGB) under three different climate change scenarios; The baseline climate scenario is to maintain the current climate condition; the RCP 4.5 scenario is a stabilization scenario to employ of technologies and strategies for reducing greenhouse gas emissions; the RCP 8.5 scenario is increasing greenhouse gas emissions over time representative with 936ppm of $CO_2$ concentration by 2100. The vegetation survey and tree-ring analysis were conducted to work out the initial vegetation maps and data for operation of the LANDIS model. Six types of forest vegetation communities were found including Quercus mongolica - Pinus densiflora community, Quercus mongolica community, Pinus densiflora community, Quercus variabilis-Quercus acutissima community, Larix leptolepis afforestation and Pinus koraiensis afforestation. As for changes in total AGB under three climate change scenarios, it was found that RCP 4.5 scenario featured the highest rate of increase in AGB whereas RCP 8.5 scenario yielded the lowest rate of increase. These results suggest that moderately elevated temperatures and $CO_2$ concentrations helped the biomass flourish as photosynthesis and water use efficiency increased, but huge increase in temperature ($above+4.0^{\circ}C$) has resulted in the increased respiration with increasing temperature. Consequently, Species productivity(Biomass) of trees decrease as the temperature is elevated drastically. It has been confirmed that the dominant species in all scenarios was Quercus mongolica. Like the trends shown in the changes of total AGB, it revealed the biggest increase in the AGB of Quercus mongolica under the RCP 4.5 scenario. AGB of Quercus mongolica and Quercus variabilis decreased in the RCP 4.5 and RCP 8.5 scenarios after 2050 but have much higher growth rates of the AGB starting from 2050 under the baseline scenario. Under all scenarios, the AGB of coniferous species was eventually perished in 2100. In particular they were extinguished in early stages of the RCP 4.5 and RCP 8.5 scenarios. This is because of natural selection of communities by successions and the failure to adapt to climate change. The results of the study could be expected to be effectively utilized to predict changes of the forest ecosystems due to climate change and to be used as basic data for establishing strategies for adaptation climate changes and the management plans for forest vegetation restoration in ecological restoration fields.