• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.31-38
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• 2017

In this paper, we propose a system which estimates Manhattan coordinate systems for urban scene images using a convolutional neural network (CNN). Estimating the Manhattan coordinate system from an image under the Manhattan world assumption is the basis for solving computer graphics and vision problems such as image adjustment and 3D scene reconstruction. We construct a CNN that estimates Manhattan coordinate systems based on GoogLeNet [1]. To train the CNN, we collect about 155,000 images under the Manhattan world assumption by using the Google Street View APIs and calculate Manhattan coordinate systems using existing calibration methods to generate dataset. In contrast to PoseNet [2] that trains per-scene CNNs, our method learns from images under the Manhattan world assumption and thus estimates Manhattan coordinate systems for new images that have not been learned. Experimental results show that our method estimates Manhattan coordinate systems with the median error of $3.157^{\circ}$ for the Google Street View images of non-trained scenes, as test set. In addition, compared to an existing calibration method [3], the proposed method shows lower intermediate errors for the test set.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.485-496
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• 2023

Recently, South Korea has been making efforts to mitigate the risk of water scarcity during droughts by utilizing various drought response measures in dam operations. While various studies have been conducted on this topic, there is currently a lack of research on the economic effects of drought response measures. In this study, we evaluated the economic effects of drought response measures on nationwide multipurpose dams by using a long-term simulated inflow model based on ARIMA and Copula and a dam operation model that reflects drought response measures. The results showed that the expected benefits per unit flow rate were highest for coordinated operation and alternative water supply measures, at KRW 1,176 and KRW 1,139, respectively, while the benefits of emergency water supply utilization and water supply adjustment were estimated at KRW 956 and KRW 875, respectively. Additionally, when we examined the changes in the economic benefits of drought response measures based on the assumption of increased drought severity in the future, the changes in the drought risk resulting from reduced inflow increased the economic benefits of all drought response measures. The economic benefits of water supply adjustment increased by 2.6% compared to the baseline, while the economic benefits of coordinated operation and alternative water supply measures increased by 11.7% compared to the baseline. This suggests that dam-network-based measures, such as coordinated operation and alternative water supply measures, are crucial as drought risk increases. This study is expected to serve as a fundamental reference for selecting and utilizing drought response measures in the future.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.84-91
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• 2011

Recently, WBAN(Wireless Body Area Network) which has progressed standardization based on IEEE 802.15.6 standardization is a network for the purpose of the short-range wireless communications within around 3 meters from the inner or outer human body. Effective QoS control technique and data efficient management in limited bandwidth such as audio and video are important elements in terms of users and loads in short-range wireless networks. In this paper, for high-speed WBAN IEEE 802.15.6 standard, the dynamic allocation to give an efficient bandwidth management and weighted fair queueing algorithm have been proposed through the adjustment of the super-frame about limited data and Quality of Service (QoS) based on the queuing algorithm. Weighted Fair Queueing(WFQ) Algorithm represents the robust performance about elements to qualitative aspects as well as maintaining fairness and maximization of system performance. The performance results show that the dynamic allocation expanded transmission bandwidth five times and the weighted fair queueing increased maximum 24.3 % throughput and also resolved delay bound problem.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.1-12
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• 2020

Low-Orbit satellite mobile networks can provide services through miniaturized terminals with low transmission power, which can be used as reliable means of communication in the national public disaster network and defense sector. However, the high traffic environment in the emergency preparedness situation increases the new call blocking probability and the handover failure probability of the satellite network, and the increase of the handover failure probability affects the QoS because low orbit satellites move in orbit at a very high speed. Among the channel allocation methods of satellite communication, the FCA shows relatively better performance in a high traffic environment than DCA and is suitable for emergency preparedness situations, but in order to optimize QoS when traffic increases, the new call blocking and the handover failure must be minimized. In this paper, we propose LEO-DBC (LEO satellite dynamic beam width control) technique, which improves QoS by adaptive adjustment of beam width of low-orbit satellites and call time of terminals by improving FCA-QH method. Through the LEO-DBC technique, it is expected that the QoS of the mobile satellite communication network can be optimally maintained in high traffic environments in emergency preparedness situations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_2
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• pp.613-623
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• 2007

The main purpose of this study is to investigate the coordinate transformation based on two different systems between local geodetic datum(tokyo datum) and international geocentric datum(new Korea geodetic datum). For this purpose, three methods were used to determine seven parameters as follows: Bursa-Wolf model, Molodensky-Badekas model, and Veis model. Also, we adopted multiple regression equation method to convert from Tokyo datum to KTRF. We used 935 control points as a common points and applied gross error analysis for detecting the outlier among those control points. The coordinate transformation was carried out using similarity transformation applied the obtained seven parameters and the precision of transformed coordinate was evaluated about 9,917 third or forth order control points. From these results, it was found that Bursa-Wolf model and Molodensky-Badekas model are more suitable than other for the determination of transformation parameters in Korea. And, transforming accuracy using MRE is lower than other similarity transformation model.

• Journal of Korea Port Economic Association
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• v.33 no.3
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• pp.139-154
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• 2017

As competition in domestic and overseas ports intensify, new and small domestic ports are realizing certain limitations to independently secure competitiveness. This study collected data over 60 months with five modifications for container and general cargo volume around Gyeongin Port. Short-period (12 months) cargo volume was forecasted, which revealed the need for a middle-to-long-term development plan. First, the hinterland logistics complex of Gyeongin Port is expected to contribute to the coastal maritime facility through the quasi-public system for fishery product transportation and passenger services. It proposes to achieve this by opening channels to and from China for precision machinery, home network systems, LEDs, and machine industry items. second, specializing the ultra-light cargo transport route, and connecting the coastal island areas of the 5 West Sea Islands to Ara Waterway (Integrated Fishery Product Center of the 5 West Sea Islands). Third, on the basis of organic cooperation of government? local government ? port, the incentive and pre-circular support system would be required, and lastly, it shall carry out the adjustment of functions in nearby ports and specialization strategy simultaneously through the integrated operation of the ports in the capital areas.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.504-511
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• 2010

In this paper, we propose the face region recognition system for City-Security CCTV(Closed Circuit Television) using hippocampal neural network which is modelling of human brain's hippocampus. This system is composed of feature extraction, learning and recognition part. The feature extraction part is constructed using PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis). In the learning part, it can label the features of the image-data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in a dentate gyrus and remove the noise through the auto-associative memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are shape change and light change. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.337-340
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• 2004

The exactly reproduced prosody of a TTS system is one of the key factors that affect the naturalness of synthesized speech. In general, rules about prosody had been gathered either from linguistic knowledge or by analyzing the prosodic information from natural speech. But these could not be perfect and some of them could be incorrect. So we proposed artificial neural network(ANN)s that can be trained to team the prosody of natural speech and generate it. In learning phase, let ANNs learn the pitch and energy contour of center phoneme by applying a string of phonemes in a sentence to ANNs and comparing the output pattern with target pattern and making adjustment in weighting values to get the least mean square error between them. In test phase, the estimation rates were computed. We saw that ANNs could generate the prosody of a sentence.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2007.05a
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• pp.83-125
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• 2007

Increasing energy costs have caused profitability problems for paper suppliers. Therefore unprofitable lines are being closed down. The actions aiming for improved profits are focused either on cost savings or on increasing the capacity of the remaining machines. The runnability of a paper machine and its total efficiency have a significant effect on energy consumption. Producing one ton of waste paper consumes at least as much energy as producing the same amount of sellable end product. New automation solutions enable significant cost-effective improvements to the total efficiency of a line without large investment projects. The measures focus on minimizing changes, interruptions, interruption recovery times and grade change times. Newest actuators, online quality measurements and wet end analysators create an improvement potential, which can be optimally implemented with the latest machine direction control solutions, based on model predictive control concepts. Equally, drying management is significant to the energy consumption. The newest control strategies optimize the use of various drying actuators for different situations; either by responding to changes as efficiently as possible or by using only the cheapest energy sources in stable situations. An even steam supply, which is vital for paper machines, is achieved with control for the power plant steam network. This makes possible to avoid the delays upon starting the paper machine and assure an even steam supply for the drying section and the actuators. This document describes means which have brought significant energy and raw material savings for paper machines. Metso Automation has provided efficiency improvement packages, which are usually based on optimized control of dry weight and drying in all running conditions. The solutions are based on performance analysis, on which the estimations for improvement potential and the necessary actions are based on. Typically benefits on an annual level have been from hundreds of thousands of euros to over one million euro. For example, variations in dry weight have been decreased more than 50%. The results are presented with a few examples. Additionally, the analysis models, adjustment solutions and the changes in running methods with which the results were achieved, are presented.