• Journal of Korea Water Resources Association
• /
• v.56 no.2
• /
• pp.75-89
• /
• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.1
• /
• pp.67-76
• /
• 2024

In this paper, we present a DIP-MLS testing method that combines digital image processing with a rigid body-based MLS differencing approach to measure mechanical variables and analyze the impact of target location and image resolution. This method assesses the displacement of the target attached to the sample through digital image processing and allocates this displacement to the node displacement of the MLS differencing method, which solely employs nodes to calculate mechanical variables such as stress and strain of the studied object. We propose an effective method to measure the displacement of the target's center of gravity using digital image processing. The calculation of mechanical variables through the MLS differencing method, incorporating image-based target displacement, facilitates easy computation of mechanical variables at arbitrary positions without constraints from meshes or grids. This is achieved by acquiring the accurate displacement history of the test specimen and utilizing the displacement of tracking points with low rigidity. The developed testing method was validated by comparing the measurement results of the sensor with those of the DIP-MLS testing method in a three-point bending test of a rubber beam. Additionally, numerical analysis results simulated only by the MLS differencing method were compared, confirming that the developed method accurately reproduces the actual test and shows good agreement with numerical analysis results before significant deformation. Furthermore, we analyzed the effects of boundary points by applying 46 tracking points, including corner points, to the DIP-MLS testing method. This was compared with using only the internal points of the target, determining the optimal image resolution for this testing method. Through this, we demonstrated that the developed method efficiently addresses the limitations of direct experiments or existing mesh-based simulations. It also suggests that digitalization of the experimental-simulation process is achievable to a considerable extent.

• Korean Journal of Remote Sensing
• /
• v.32 no.3
• /
• pp.307-321
• /
• 2016

Planetary Boundary Layer Height (PBLH) is a major input parameter for weather forecasting and atmosphere diffusion models. In order to estimate the sub-grid scale variability of PBLH, we need to monitor PBLH data with high spatio-temporal resolution. Accordingly, we introduce a LIdar observation VEhicle (LIVE), and analyze PBLH derived from the lidar loaded in LIVE. PBLH estimated from LIVE shows high correlations with those estimated from both WRF model ($R^2=0.68$) and radiosonde ($R^2=0.72$). However, PBLH from lidar tend to be overestimated in comparison with those from both WRF and radiosonde because lidar appears to detect height of Residual Layer (RL) as PBLH which is overall below near the overlap height (< 300 m). PBLH from lidar with 10 min time resolution shows typical diurnal variation since it grows up after sunrise and reaches the maximum after 2 hours of sun culmination. The average growth rate of PBLH during the analysis period (2014/06/26 ~ 30) is 1.79 (-2.9 ~ 5.7) m $min^{-1}$. In addition, the lidar signal measured from moving LIVE shows that there is very low noise in comparison with that from the stationary observation. The PBLH from LIVE is 1065 m, similar to the value (1150 m) derived from the radiosonde launched at Sokcho. This study suggests that LIVE can observe continuous and reliable PBLH with high resolution in both stationary and mobile systems.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.40 no.3
• /
• pp.273-283
• /
• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Korean Society of Transportation
• /
• v.25 no.4
• /
• pp.123-133
• /
• 2007

This study analyzed whether a transit network with hierarchy is efficient or not, and if transit network hierarchy has cost efficiency, then which condition guarantees the efficiency of the transit network hierarchy. The authors modeled the total cost of the transit network and suggested the conditions in which the transit network hierarchy has cost efficiency through comparing the cost of the transit network with and without hierarchy. The efficiency of transit network hierarchy is guaranteed when the travel cost savings induced by using a higher hierarchy transit network is larger than the increasing non-travel cost, which is the sum of access cost, waiting cost, and operating cost, induced by the introduction of a higher hierarchy transit network. This result is consistent with common sense and with the concept of cost and benefit analysis. If a passenger traveling within the area divided by a higher hierarchy transit network uses only a lower hierarchy transit network and the passenger traveling out of the area divided by the higher hierarchy transit network uses both lower and higher hierarchy transit networks, the travel demand using the higher hierarchy transit network is inversely proportional to the square of the line spacing. This means that the transit network becomes more efficient and small increases of travel demand guarantee the efficiency of the transit network hierarchy as the connectivity of the network becomes higher. This result shows that transit networks have economies of aggregation. This study is the first analytical research on transit network hierarchy and is expected to be a basis for numerical research. However, numerical research should complement this study, since analytical research has some limitations for considering a real network.

• Journal of Energy Engineering
• /
• v.23 no.2
• /
• pp.125-148
• /
• 2014

This paper performs a historical analysis on the various factors contributing to the current carbon lock-in of Korean electricity industry by using techo-institutional complex. The possibilities of the industry's carbon lock-out toward more sustainable development are also investigated. It turns out that market, firm, consumer, and government factors are all responsible for the development of the carbon lock-in of Korean power industry; the Korean government consistently favoring large power plants based on the economy of scale; below-cost electricity tariff; inflation policy to suppress increases in power price; rapid demand growth in summer and winter seasons; rigidities of electricity tariff; and expansion of gas-fired and imported coal-fired large power plants. On the other hand, except for nuclear power generation and smart grid, environment laws and new and renewable energy laws are the other remaining factors contributing to the carbon lock-out. Considering three key points that Korea is an export-oriented economy, the generation mix is the most critical factor to decide the amounts of carbon emission in the power industry, and the share of industry and commercial power consumption is over 85%, it is unlikely that Korea will achieve the carbon lock-out of power industry in the near future. Therefore, there are needs for more integrated approaches from market, firm, consumer, and government all together in order to achieve the carbon lock-out in the electricity industry. Firstly, from the market perspective, it is necessary to persue more active new and renewable energy penetration and to guarantee consumer choices by mitigating the incumbent's monopoly power as in the OECD countries. Secondly, from the firm perspective, the promotion of distributed energy system is urgent, which includes new and renewable resources and demand resources. Thirdly, from the consumer perspective, more green choices in the power tariff and customer awareness on the carbon lock-out are needed. Lastly, the government shall urgently improve power planning frameworks to include the various externalities that were not properly reflected in the past such as environmental and social conflict costs.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.45 no.2
• /
• pp.40-50
• /
• 2017

Compared to the traditional format, the revised IPA is an effective method for selecting a management strategy as compared to the traditional IPA. Comparison between the traditional IPA and revised IPA with a management strategy has been, however, limited. Therefore, the difference between the traditional IPA and revised IPA was compared to select an effective management strategy in the Suncheon Bay Wetland Reserve. First of all, related papers were reviewed to select an appropriate revised IPA. It was found that Deng (2007)'s revised IPA was appropriate for quantifying service quality and a management strategy that affects the measurable satisfaction of visitors in the space. Second, the results of the traditional IPA were compared with the revised IPA in the Suncheon Bay Wetland Reserve and the management strategy of the revised IPA and the changes of management factors were discussed. It was found that some management factors deviated from the order of the quadrant "low priority for managers", "Concentrate management here", "Keep up the good work" were moved to the order of the quadrants "Concentrate management here", "low priority for managers" and "Possible overkill" in the revised IPA grid. The complexity as a management factor resulted in higher demand management than the traditional IPA, which moved from "low priority for managers" to "Concentrate management here". Management factors resulted in lower demand management than the traditional IPA moved from "Concentrate management here" to "low priority for managers"; these consisted of shade trees, exhibition exteriors, programs, and a guided tour. Also, management factors moved from "Keep up the good work" to "Possible overkill" consisted of relaxation facilities, glow of the setting sun, a hedge, and an exhibition interior. Over all, the revised IPA responded properly to changes in the measurable satisfaction of visitors to the Suncheon Bay Wetland Reserve. Therefore, a revised IPA should be provided for accurate and reliable guidelines when decision makers establish management strategies.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.33-49
• /
• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Korean Society of Transportation
• /
• v.19 no.4
• /
• pp.35-47
• /
• 2001

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence. numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristic of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a methodology of motor vehicle emission calculation by using real-time traffic data was studied. A methodology for estimating emissions of CO at a test area in Seoul. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It was calculated speed-related mass of CO emission from traffic tail pipe of data from traffic system, and parameters are considered, volume, composition, average velocity, link length. And, the result was compared with that of a method of emission calculation by VKT(Vehicle Kilometer Travelled) of vehicles of category.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.43 no.11
• /
• pp.1757-1765
• /
• 2014

The objective of this study was to explore hygiene issues by analyzing the importance and performance of personal and school hygiene in school foodservices. Questionnaires were administered to 634 students (10 high schools) in the Busan area. Exactly 29% of respondents had received hygiene education. The average score of importance of students' personal foodservice hygiene was 3.81/5.00, and the performance score was 3.48/5.00. The same scores for school foodservice hygiene were 4.37/5.00 and 3.67/5.00, respectively. Regarding importance and performance of personal foodservice hygiene, students who had received hygiene education showed significantly (P<0.01) higher scores than those without prior education. In terms of importance of school foodservice hygiene, students who had received hygiene education showed significantly higher scores for environment hygiene (P<0.05) and equipment hygiene (P<0.05). Additionally, among grid analysis in personal foodservice hygiene, the areas of high importance and low performance included 'washing hands before the meal', 'using a designated cup for the water purifier', and 'keeping clean around the leftover container'. As for school foodservice hygiene, the same area was 'cleanliness of tray'. These findings suggest that hygiene education needs to be extended to more students, and for school foodservice hygiene, a cleaner environment should be created using equipment hygiene management, including emphasis on ensuring cleanliness of tray.