• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.5
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• pp.320-333
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• 2019

In this study, a physics-based 3D morphology model for the estimation of an erosion rate of nourished beach is newly proposed. As a hydrodynamic module, IHFOAM toolbox having its roots on the OpenFoam is used. On the other hand, the morphology model comprised a transport equation for suspended sediment, and Exner type equation derived from the viewpoint of sediment budget with the bed load being taken to accounted. In doing so, the incipient motion of sediment is determined based on the Shields Diagram, while the bottom suspended sediment concentration, the bed load transport rate is figured out using the bottom shearing stress directly calculated from the numerically simulated flow field rather than the conventional quadratic law and frictional coefficient. In order to verify the proposed morphology model, we numerically simulate the nonlinear shoaling, breaking over the uniform beach of 1/m slope, and its ensuing morphology change. Numerical results show that the partially skewed, and asymmetric bottom shearing stresses can be successfully simulated. It was shown that sediments suspended and eroded at the foreshore by wave breaking are gradually drifted toward a shore and accumulated in the process of up-rush, which eventually leads to the formation of swash bar. It is also worth mentioning that the breaker bar formed by the sediments dragged by the back-wash flow which commences at the pinnacle of up-rush as the back-wash flow gets weakened due to the increased depth was successfully duplicated in the numerical simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.141-148
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• 2021

In some intersections or busy traffic roads, there are more pedestrians in a specific period of time, and there are many traffic accidents caused by road congestion. Especially at the intersection where there are schools nearby, it is particularly important to protect the traffic safety of students in busy hours. In the past, when designing traffic lights, the safety of pedestrians was seldom taken into account, and the identification of motor vehicles and traffic optimization were mostly studied. How to keep the road smooth as far as possible under the premise of ensuring the safety of pedestrians, especially students, will be the key research direction of this paper. This paper will focus on person, motorcycle, bicycle, car and bus recognition research. Through investigation and comparison, this paper proposes to use YOLO v4 network to identify the location and quantity of objects. YOLO v4 has the characteristics of strong ability of small target recognition, high precision and fast processing speed, and sets the data acquisition object to train and test the image set. Using the statistics of the accuracy rate, error rate and omission rate of the target in the video, the network trained in this paper can accurately and effectively identify persons, motorcycles, bicycles, cars and buses in the moving images.

• Explosives and Blasting
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• v.38 no.4
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• pp.16-25
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• 2020

Multi-setting smart-investigation of the ground and large uncharged hole boring (MSP) method to reduce the blast-induced vibration in a tunnel excavation is carried out over 50m of long-distance boring in a horizontal direction and thus has been accompanied by deviations in boring alignment because of the heavy and one-directional rotation of the rod. Therefore, the deviation has been adjusted through the boring machine's variable setting rely on the previous construction records and expert's experience. However, the geological characteristics, machine conditions, and inexperienced workers have caused significant deviation from the target alignment. The excessive deviation from the boring target may cause a delay in the construction schedule and economic losses. A deep learning-based prediction model has been developed to discover an ideal initial setting of the MSP machine. Dropout, early stopping, pre-training techniques have been employed to prevent overfitting in the training phase and, significantly improved the prediction results. These results showed the high possibility of developing the model to suggest the boring machine's optimum initial setting. We expect that optimized setting guidelines can be further developed through the continuous addition of the data and the additional consideration of the other factors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.19-26
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• 2021

The lattice structure is attracting attention from industry because of its excellent strength and stiffness, ultra-lightweight, and energy absorption capability. Despite these advantages, widespread commercialization is limited by the difficult manufacturing processes for complex shapes. Additive manufacturing is attracting attention as an optimal technology for manufacturing lattice structures as a technology capable of fabricating complex geometric shapes. In this study, a unit cell was formed using a three-dimensional coordinate method. The relative density relational equation according to the boundary box size and strut radius of the unit cell was derived. Simple cubic (SC), body-centered cubic (BCC), and face-centered cubic (FCC) with a controlled relative density were designed using modeling software. The accuracy of the equations for calculating the relative density proposed in this study secured 98.3%, 98.6%, and 96.2% reliability in SC, BCC, and FCC, respectively. A simulation of the lattice structure revealed an increase in compressive yield load with increasing relative density under the same cell arrangement condition. The compressive yield load decreased in the order of SC, BCC, and FCC under the same arrangement conditions. Finally, structural optimization for the compressive load of a 20 mm × 20 mm × 20 mm structure was possible by configuring the SC unit cells in a 3 × 3 × 3 array.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.460-468
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• 2021

This study investigated the enhanced combustion efficiency of an "air-cooled combustion system" with single F.D. fan, and performed a numerical analysis for the operation and design conditions to increase the combustion efficiency. The combustion efficiency in an actual combustor was compared before and after the structure modification. Numerical analysis for application of a single fan revealed the difficulty of forming a turbulence for circular combustion conditions. This is because the supply ratio of combustion air supplied into 2 flow paths becomes irregular in the combustion furnace due to a change in friction force and pressure in each flow path. Subsequently, two methods of supplying air into the combustion furnace were analyzed numerically to obtain the optimal combustion conditions of an air-cooled combustion system. The first method involved injecting the preheated combustion air after a 180~360 degree rotation from the outer wall, whereas in the second method, the combustion air was injected into the combustion furnace in a tangential direction after primary heat exchange outside the combustion furnace, by applying a rotatable vane structure in the combustion furnace. Results reveal that application of a single F.D. fan to the air injection into a rotatable combustion furnace is desirable for optimization of the combustion conditions for applying a duct structure having a dual cooling wall for the cooling of the outer wall of the combustion furnace, and for maintaining perfect mixing in the combustion furnace. We therefore confirmed enhanced combustion efficiency by comparing the actual combustion efficiency before and after structure modification.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.613-622
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• 2020

The South Korean government plans for a 37 % reduction in CO2 emissions against business as usual by 2030. Subsequently, the Ministry of Land, Infrastructure and Transport declared a 26.9 % reduction target in greenhouse gas emissions from buildings by 2020 and established the Green Standard for Energy and Environmental Design (G-SEED) to help improve the environmental performance of buildings. Construction companies often work with consulting firms to prepare for G-SEED certification. In the process, owing to inefficient data sharing and work connections, it is difficult to achieve economic efficiency and obtain certification. The objective of this study was to develop an economic model to assist contractors in achieving the required G-SEED scores for materials and resources. To do this, we automated the process for material comparison and selection on the basis of an analysis of actual consulting data, and developed a model that selects material alternatives that can meet the required scores at a minimum cost. Information on materials is input by applying a genetic algorithm to the optimization of alternatives. When the model was applied to actual data, the construction cost could be lowered by 79.3 % compared with existing methods. The economical material selection model is expected to not only reduce construction costs for owners desiring G-SEED certification but also shorten the project design time.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.931-941
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• 2020

There is a growing interest this paper for ocean sensing where autonomous vehicles can play an essential role in assisting engineers, researchers, and scientists with environmental monitoring and collecting oceanographic data. This study was conducted to develop a rigid sail for the autonomous sailing drone. Our study aims to numerically analyze the aerodynamic characteristics of curvy twin sail and compare it with wing sail. Because racing regulations limit the sail shape, only the two-dimensional geometry (2D) was open for an optimization. Therefore, the first objective was to identify the aerodynamic performance of such curvy twin sails. The secondary objective was to estimate the effect of the sail's spacing and shapes. A viscous Navier-Stokes flow solver was used for the numerical aerodynamic analysis. The 2D aerodynamic investigation is a preliminary evaluation. The results indicated that the curvy twin sail designs have improved lift, drag, and driving force coefficient compared to the wing sails. The spacing between the port and starboard sails of curvy twin sail was an important parameter. The spacing is 0.035 L, 0.07 L, and 0.14 L shows the lift coefficient reduction because of dramatically stall effect, while flow separation is improved with spacing is 0.21 L, 0.28 L, and 0.35 L. Significantly, the spacing 0.28 L shows the maximum high pressure at the lower area and the small low pressure area at leading edges. Therefore, the highest lift was generated.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.