• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• 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 Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.603-611
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• 2020

Potholes on the road directly affect drivers' safety, satisfaction, and vehicle damage. Thus, real-time detection and response are required. Increasing frequency of patrols allows for potholes to be detected and responded to quickly, but this takes much manpower, money, and time. In addition, potholes have different occurrence characteristics depending on the rain conditions, so it is necessary to consider the optimal frequency from an economic and road-service perspective. Therefore, a quantitative analysis was done on the effects of rainfall on the occurrence characteristics of potholes. Information on the persistence, impact of rainfall intensity, and weather information was collected over a long period. Based on the results, a risk-based, optimized, and changeable road-patrol strategy is presented. The analysis results show that the probability of pothole occurrence increases by 2.4 times in rainy weather. Furthermore, the impact continues for 3 days even after the rain stops. The probability of pothole occurrence increases by 0.46% per 1 mm of rainfall, and the occurrence characteristics react sensitively to even a small amount of rain of around 1 mm. It was concluded that road patrol is required at least once every three days for an effect-free period, while twice a day is needed for the "sphere of influence" period to achieve a 95% reliability level.ys for effect-free period, while twice a day for sphere of influence period to satisfy 95% reliability level.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.955-968
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• 2021

A water resource plan is routinely based on a natural flow and can be estimated using observed streamflow data or a long-term continuous rainfall-runoff model. However, the watershed with the natural flow is very limited to the upstream area of the dam. In particular, for the ungauged watershed, a rainfall-runoff model is established for the gauged watershed, and the model is then applied to the ungauged watershed by transferring the associated parameters. In this study, the GR4J rainfall-runoff model is mainly used to regionalize the parameters that are estimated from the 14 dam watershed via an optimization process. In terms of optimizing the parameters, the Bayesian approach was applied to consider the uncertainty of parameters quantitatively, and a number of parameter samples obtained from the posterior distribution were used for the regionalization. Here, the relationship between the estimated parameters and the topographical factors was first identified, and the dependencies between them are effectively modeled by a Copula function approach to obtain the regionalized parameters. The predicted streamflow with the use of regionalized parameters showed a good agreement with that of the observed with a correlation of about 0.8. It was found that the proposed regionalized framework is able to effectively simulate streamflow for the ungauged watersheds by the use of the regionalized parameters, along with the associated uncertainty, informed by the basin characteristics.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.483-490
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• 2022

Floating LiDAR is a system that provides a new paradigm for wind condition observation, which is essential when creating an offshore wind farm. As it can save time and money, minimize environmental impact, and even reduce backlash from local communities, it is emerging as the industry standard. However, the design and verification of a stable platform is very important, as disturbance factors caused by fluctuations of the buoy affect the reliability of observation data. In Korea, due to the nation's late entry into the technology, a number of foreign equipment manufacturers are dominating the domestic market. The west coast of Korea is a shallow sea environment with a very large tidal difference, so strong currents repeatedly appear depending on the region, and waves of strong energy that differ by season are formed. This paper conducted a study examining buoys suitable for LiDAR operation in the waters of Korea, which have such complex environmental characteristics. In this paper, we will introduce examples of optimized design and verification of ship-type buoys, which were applied first, and derive important concepts that will serve as the basis for the development of various platforms in the future.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.50-60
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• 2010

For the last half a century, the personal computer and software industries have been prosperous due to the incessant evolution of computer systems. In the 21st century, the embedded system market has greatly increased as the market shifted to the mobile gadget field. While a lot of multimedia gadgets such as mobile phone, navigation system, PMP, etc. are pouring into the market, most industrial control systems still rely on 8-bit micro-controllers and simple application software techniques. Unfortunately, the technological barrier which requires additional investment and higher quality manpower to overcome, and the business risks which come from the uncertainty of the market growth and the competitiveness of the resulting products have prevented the companies in the industry from taking advantage of such fancy technologies. However, high performance, low-power and low-cost hardware and software platforms will enable their high-technology products to be developed and recognized by potential clients in the future. This paper presents such a platform for industrial embedded systems. The platform was designed based on Telechips TCC8300 multimedia processor which embedded a variety of parallel hardware for the implementation of multimedia functions. And open-source Embedded Linux, TinyX and GTK+ are used for implementation of GUI to minimize technology costs. In order to estimate the expected performance and power consumption, the performance improvement and the power consumption due to each of enabled hardware sub-systems including YUV2RGB frame converter are measured. An analytic model was devised to check the feasibility of a new application and trade off its performance and power consumption. The validity of the model has been confirmed by implementing a real target system. The cost can be further mitigated by using the hardware parts which are being used for mass production products mostly in the cell-phone market.

• Journal of Korea Water Resources Association
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• v.45 no.3
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• pp.301-316
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• 2012

Two models, TANK and SWAT (Soil and Water Assessment Tool) were compared for simulating natural flows in the Paldang Dam upstream areas of the Han River basin in order to understand the limitations of TANK and to review the applicability and capability of SWAT. For comparison, simulation results from the previous research work were used. In the results for the calibrated watersheds (Chungju Dam and Soyanggang Dam), two models provided promising results for forecasting of daily flows with the Nash-Sutcliffe model efficiency of around 0.8. TANK simulated observations during some peak flood seasons better than SWAT, while it showed poor results during dry seasons, especially its simulations did not fall down under a certain value. It can be explained that TANK was calibrated for relatively larger flows than smaller ones. SWAT results showed a relatively good agreement with observed flows except some flood flows, and simulated inflows at the Paldang Dam considering discharges from upper dams coincided with observations with the model efficiency of around 0.9. This accounts for SWAT applicability with higher accuracy in predicting natural flows without dam operation or artificial water uses, and in assessing flow variations before and after dam development. Also, two model results were compared for other watersheds such as Pyeongchang-A, Dalcheon-B, Seomgang-B, Inbuk-A, Hangang-D, and Hongcheon-A to which calibrated TANK parameters were applied. The results were similar to the case of calibrated watersheds, that TANK simulated poor smaller flows except some flood flows and had same problem of keeping on over a certain value in dry seasons. This indicates that TANK application may have fatal uncertainties in estimating low flows used as an important index in water resources planning and management. Therefore, in order to reflect actually complex and complicated physical characteristics of Korean watersheds, and to manage efficiently water resources according to the land use and water use changes with urbanization or climate change in the future, it is necessary to utilize a physically based watershed model like SWAT rather than an existing conceptual lumped model like TANK.

• Journal of Life Science
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• v.29 no.6
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• pp.653-661
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• 2019

The first small interfering RNA (siRNA) therapeutics have recently been approved by the Food and Drug Administration in the U.S., and the demand for a new RNA therapeutics bioanalysis method-which is essential for pharmacokinetics, including the absorption, distribution, metabolism, and excretion of siRNA therapeutics-is rapidly increasing. The stem-loop real-time qPCR (RT-qPCR) assay is a useful molecular technique for the identification and quantification of small RNA (e.g., micro RNA and siRNA) and can be applied for the bioanalysis of siRNA therapeutics. When the anti-HPV E6/E7 siRNA therapeutic was used in preclinical trials, the established stem-loop RT-qPCR assay was validated. The limit of detection was sensitive up to 10 fM and the lower limit of quantification up to 100 fM. In fact, the reliability of the established test method was further validated in three intra assays. Here, the correlation coefficient of $R^2$>0.99, the slope of -3.10 ~ -3.40, and the recovery rate within ${\pm}20%$ of the siRNA standard curve confirm its excellent robustness. Finally, the circulation profiles of siRNAs were demonstrated in rat serum, and the pharmacokinetic properties of the anti-HPV E6/E7 siRNA therapeutic were characterized using a stem-loop RT-qPCR assay. Therefore, the stemloop RT-qPCR assay enables accurate, precise, and sensitive siRNA duplex quantification and is suitable for the quantification of small RNA therapeutics using small volumes of biological samples.

• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.701-715
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• 2020

This study compared several reference evapotranspiration estimated using eight methods such as FAO-56 Penman-Monteith (FAO PM), Hamon, Hansen, Hargreaves-Samani, Jensen-Haise, Makkink, Priestley-Taylor, and Thornthwaite. In addition, by analyzing the monthly deviations of the results by the FAO PM and the remaining seven methods, monthly optimized correction coefficients were derived and the improvement effect was evaluated. These methods were applied to 73 automated synoptic observation system (ASOS) stations of the Korea Meteorological Administration, where the climatological data are available at least 20 years. As a result of evaluating the reference evapotranspiration by applying the default coefficients of each method, a large fluctuation happened depending on the method, and the Hansen method was relatively similar to FAO PM. However, the Hamon and Jensen-Haise methods showed more large values than other methods in summer, and the deviation from FAO PM method was also large significantly. When comparing based on the region, the comparison with FAO PM method provided that the reference evapotranspiration estimated by other methods was overestimated in most regions except for eastern coastal areas. Based on the deviation from the FAO PM method, the monthly correction coefficients were derived for each station. The monthly deviation average that ranged from -46 mm to +88 mm before correction was improved to -11 mm to +1 mm after correction, and the annual average deviation was also significantly reduced by correction from -393 mm to +354 mm (before correction) to -33 mm to +9 mm (after correction). In particular, Hamon, Hargreaves-Samani, and Thornthwaite methods using only temperature data also produced results that were not significantly different from FAO PM after correction. It can be also useful for forecasting long-term reference evapotranspiration using temperature data in climate change scenarios or predicting evapotranspiration using monthly or seasonal temperature forecasted values.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.