• Journal of Ocean Engineering and Technology
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• v.20 no.6 s.73
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• pp.75-81
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• 2006

In this study, the dynamic response of the umbilical cable in a deep-water unmanned underwater vehicle system was analyzed. In order to analyze the forces acting on the cable, the launcher and umbilical cable were modeled by the simple 1-D mass-spring system. Damping and dynamic analysis was carried out by a direct time integration scheme using the $Newmark-{\beta}$ method with inverse iteration procedure, considering the nonlinear drag forces acting on the launcher. The obtained results of the present study can be used for the design of connecting the structure of the launcher and cable of the UUV system.

• Journal of KIBIM
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• v.10 no.4
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• pp.60-69
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• 2020

Allowing on-site workers to access information of the construction process can enable task control, data integration, material and resource control. However, in the current practice of the construction industry, the existing methods and scope is quite limited, leading to inefficient management during the construction process. In this research, by adopting cutting edge technologies such as Augmented Reality(AR), digital twins, deep learning and computer vision with wearable AR devices, the authors proposed an AR visualization framework made of virtual components to help on-site workers to obtain information of the construction process with ease of use. Also, this paper investigates wearable AR devices and object detection algorithms, which are critical factors in the proposed framework, to test their suitability.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.1-43.1
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• 2019

In this study, we apply a deep learning model to denoising solar magnetograms. For this, we design a model based on conditional generative adversarial network, which is one of the deep learning algorithms, for the image-to-image translation from a single magnetogram to a denoised magnetogram. For the single magnetogram, we use SDO/HMI line-of-sight magnetograms at the center of solar disk. For the denoised magnetogram, we make 21-frame-stacked magnetograms at the center of solar disk considering solar rotation. We train a model using 7004 paris of the single and denoised magnetograms from 2013 January to 2013 October and test the model using 1432 pairs from 2013 November to 2013 December. Our results from this study are as follows. First, our model successfully denoise SDO/HMI magnetograms and the denoised magnetograms from our model are similar to the stacked magnetograms. Second, the average pixel-to-pixel correlation coefficient value between denoised magnetograms from our model and stacked magnetogrmas is larger than 0.93. Third, the average noise level of denoised magnetograms from our model is greatly reduced from 10.29 G to 3.89 G, and it is consistent with or smaller than that of stacked magnetograms 4.11 G. Our results can be applied to many scientific field in which the integration of many frames are used to improve the signal-to-noise ratio.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.10a
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• pp.76-79
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• 1997

In this study, an elasto-plastic finite element code, ESFORM, was developed to analyze sheet stamping processes. A formulation of 4-node degenerated shell element was implemented in the code. Workpiece materials were assumed to have planar anisotropy, and governed by associated flow rule. Explicit time integration method was employed to save computation time and reduce the required computer memory. Penalty method was used to describe interface behavior between workpiece and rigid die. Deep drawing of square cup and front finder stamping processes were simulated by ESFORM>

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.3
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• pp.159-167
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• 2015

In this paper, the errors found in the existed analytical solutions described the mechanism of residual pore-water pressure accumulation were examined and a new analytical was proposed. The new analytical solution was derived by using a Fourier series expansion and separation of variables was verified by comparison with the existed both analytical and numerical solutions and experimental result. The new analytical solution is very simple that there is no need for numerical integration for deep soil thickness. In addition, the solutions of the residual pore-water pressure for finite, deep, and shallow soil thickness reveled that it is possible to approach from finite to shallow soil thickness, but not possible to deep soil thickness because there was discontinues zone between finite and deep soil thickness.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Journal of Korean Society of Rural Planning
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• v.10 no.4 s.25
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• pp.19-28
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• 2004

In order to revitalize rural areas fundamentally through multifunctional utilization of their resources, it should be necessary to prepare the rational development plan to the areal characteristics and conditions, and the first priority of its planning works should be given to spatial planning. The space syntax method, a powerful objective and quantitative analysis tool on the relationship between social and spatial characteristics, was introduced in this study. Five Comprehensive Rural Clustered Villages Development Areas in the Jeonnam-province were selected as case study areas, of which total area's and included villages' spatial variables were measured and analyzed. Rural villages analyzed in this study have the spatial structure badly systematized and much complicated, which results from low integration and deep spatial depth of them. And, by virtue of relatively many axial lines, there should be few differences between villages in terms of local integration, connectivity and control, while being significant difference in terms of global integration showing the whole areal characteristics. Intelligibility, the correlation coefficient between connectivity(local variable) and integration(global one) is low, which means that the spatial structure of the study areas is difficult for visitors to understand the area or village well. Spatial configuration analysis results in the case study areas showed that each development area has a unique spatial structure and is differentiated in terms of not only local spatial variables but also global spatial variables. Therefore, global and local characteristics should be considered in spatial analysis of development areas.