• Journal of the Society of Naval Architects of Korea
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• v.60 no.3
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• pp.165-174
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• 2023

Unmanned Surface Vehicle (USV)'s berthing and unberthing is the most difficult maneuvering tasks and have the highest risk of accidents. In this paper, we designed a berthing/unberthing control algorithm given human joystick command for an USV equipped with a waterjet and a bow thruster. The berthing and unberthing maneuvers are performed remotely by a joystick operator at the Ground Control Center (GCC) where the status of USV and environmental situation can be monitored. We interpret the human joystick commands into USV's desired speed, yaw rate, and heading angle commands. next, we developed a control algorithm for the desired target values of MIMO actuators (engine speed, bucket step, nozzle angle, and bow thruster state) to follow the interpreted commands. The validity of the control algorithm is confirmed through simulations and sea trials at Gwang Am port.

• Atmosphere
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• v.25 no.2
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• pp.221-233
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• 2015

Pollen is closely related to health issues such as allergenic rhinitis and asthma as well as intensifying atopic syndrome. Information on current and future spatio-temporal distribution of allergenic pollen is needed to address such issues. In this study, the Community Multiscale Air Quality Modeling (CMAQ) was utilized as a base modeling system to forecast pollen dispersal from oak trees. Pollen emission is one of the most important parts in the dispersal modeling system. Areal emission factor was determined from gridded areal fraction of oak trees, which was produced by the analysis of the tree type maps (1:5000) obtained from the Korea Forest Service. Daily total pollen production was estimated by a robust multiple regression model of weather conditions and pollen concentration. Hourly emission factor was determined from wind speed and friction velocity. Hourly pollen emission was then calculated by multiplying areal emission factor, daily total pollen production, and hourly emission factor. Forecast data from the KMA UM LDAPS (Korea Meteorological Administration Unified Model Local Data Assimilation and Prediction System) was utilized as input. For the verification of the model, daily observed pollen concentration from 12 sites in Korea during the pollen season of 2014. Although the model showed a tendency of over-estimation in terms of the seasonal and daily mean concentrations, overall concentration was similar to the observation. Comparison at the hourly output showed distinctive delay of the peak hours by the model at the 'Pocheon' site. It was speculated that the constant release of hourly number of pollen in the modeling framework caused the delay.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.697-703
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• 2011

Studies have shown that research and development has positively impacted the upbringing of construction industry. However, its economic effect has not yet been fully investigated. This study develops an economic effect analysis model for R&D performance. In-depth interview and review of the literature produced a total of 11 performance indicators that considered the public characteristics of construction industry. A case study which involved the Expressway & Transportation Research Institute was conducted in order to verify the proposed model. The result showed that the return on investment of the institute was 3.3 times of the R&D investment. The proposed model is expected to help researchers analyze an economic effect analysis model for R&D performance.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.137-145
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• 2012

According to the National Police Agency, the total number of traffic accidents which occurred in 2010 was 226,878. Intersection accidents accounts for 44.8%, the largest portion of the entire traffic accidents. An research on the signalized intersection is constantly made, while an research on the unsignalized intersection is yet insufficient. This study selected traffic volume, road width, and sight distance as the input variables which affect unsignalized intersection accidents, and number of accidents as the output variable to build a model using ANFIS(Adaptive Neuro-Fuzzy Inference System). The forecast performance of this model is evaluated by comparing the actual measurement value with the forecasted value. The compatibility is evaluated by R2, the coefficient of determination, along with Mean Absolute Error (MAE) and Mean Square Error (MSE), the indicators which represent the degree of error and distribution. The result shows that the $R^2$ is 0.9817, while MAE and MSE are 0.4773 and 0.3037 respectively, which means that the explanatory power of the model is quite decent. This study is expected to provide the basic data for establishment of safety measure for unsignalized intersection and the improvement of traffic accidents.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.2
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• pp.195-204
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• 2016

In this paper, the applicability and usefulness of windprofiler input data were investigated to generate three dimensional wind field. A logical diagnostic model CALMET with windprofiler data at ten sites and with weather forecasting model WRF output was evaluated by statistically comparing with the radiosonde data at eight sites. The horizontal wind speed from CALMET simulated with hourly windprofiler data is in good agreement with radiosonde observations within 1.5 m/s of the root mean square error, especially local circulation of wind such as sea breeze over the coastal region. The root mean square error of wind direction ranged $50^{\circ}{\sim}70^{\circ}$ is due to the wind direction error from the windprofiler polluted by ground clutters. Since the exact wind can be produced quickly and accurately in most of the altitude with windprofiler data on CALMET, we expect the method presented in this study to be useful for the monitoring of safe environment as well as weather in the coastal zone.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.833-854
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• 2007

Output prices tend to respond faster to input price increases than to decreases. The 'rockets and feathers' hypothesis of asymmetric price behavior in petroleum market is tested by a full adjustment error correction model. Using monthly data for the period January 1977 to June 2006, evidence is found that there is a significant degree of asymmetry in the adjustment of wholesale prices to increases and to decreases in crude oil price. A similar hypothesis in regard to the exchange rate is also rejected by the data. Using weekly data over the period examined, evidence of asymmetry for gasoline, diesel and heating oil is also found in the transmission of price changes from wholesale to retail: retail prices increase more quickly in response to the wholesale price increases than to wholesale price decreases.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.2
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• pp.96-108
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• 2011

Going abreast of high public concerns on the environment, the need of environmental modeling has been increased to assess the impact of space exploitation of environment. GIS offers potential solutions to the many problems encountered during water-quality modeling. But there are also many problems associated with the modeling. The preparation of necessary parameters for the modeling can be complicated. Also, the results from one model can be different from each other even the same area is analyzed. This paper aims to develop the data processing system to couple the Qual2E and HyGIS in which Qual2E input and output data files can be created, modified and processed using HyGIS and assess the performance of the system. A structural analysis and standardization of modeling are conducted to identify data flow and processing of Qual2E. Algorithms of the defined processors are designed and developed as component modules. The data model of HyGIS-Qual2E is designed, and GUI(Graphical User Interface) is developed using Visual Basic 6.0 and GDK.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.241-250
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• 2006

Although walking is an important transport mode which should be promoted, realistic studies about walking is not sufficient. Especially, due to the transportation planning oriented toward automobile, there is not realistic analysis method for walking in the Highway Capacity Manual. Therefore, in this study the fuzzy approximate reasoning was employed to build a model for the analysis of walkways service level. For the input variable the noise level and brightness as well as the pedestrian flow rate were employed and the output variable was the walking satisfaction degree. The fuzzy models were constructed for daytime and nighttime separately. The forecastability analysis for the models were conducted using $R^2$, MAE and MSE. The values of them for the daytime model are 0.802, 0.729 and 0.735 respectively and the values for nighttime are 0.893, 0.878 and 0.860 respectively, so it can be said that the models explain the real situation well. As a result of this study, it can be concluded that the noise level has stronger effects to walking satisfaction then the brightness in night.