• Archives of Plastic Surgery
• /
• v.47 no.2
• /
• pp.146-152
• /
• 2020

Background Preoperative computed tomography angiography (CTA) of the abdominal wall vessels is used when planning free flap breast reconstruction (FFBR) because it provides a surgical road map which facilitates flap harvest. However, there are few reports on the effect of abnormal findings on the operative plan. Methods We conducted a retrospective study of all FFBRs performed at a tertiary referral center over a 6-year period (November 2011 to June 2017). One consultant radiologist reported on the findings. Details on patient demographics, CTA reports, and intraoperative details were collected. Results Two hundred patients received preoperative CTAs. Fourteen percent of patients (n=28) had abnormal findings. Of these findings, 18% were vascular anomalies; 36% tumorrelated and 46% were "miscellaneous." In four patients, findings subsequently prevented surgery; they comprised a mesenteric artery aneurysm, absent deep inferior epigastric (DIE) vessels, bilateral occluded DIE arteries, and significant bone metastases. Another patient had no suitable vessels for a free flap and the surgical plan converted to a pedicled transverse rectus abdominis musculocutaneous flap. The remaining incidental findings had no impact on the surgical plan or appropriateness of FFBR. More than one in 10 of those with abnormal findings went on to have further imaging before their operation. Conclusions CTA in FFBR can have a wider impact than facilitating surgical planning and reducing operative times. Incidental findings can influence the surgical plan, and in some instances, avoid doomed-to-fail and unsafe surgery. It is therefore important that these scans are reported by an experienced radiologist.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.9
• /
• pp.31-36
• /
• 2020

This research study designed a location image collecting technology. It provides the exact location information of an image which is not given in the photo to the user. Deep learning technology analysis and collects the images. The purpose of this service system is to provide the exact place name, location and the various information of the place such as nearby recommended attractions when the user upload the image photo to the service system. Suggested system has a deep learning model that has a size of 25.3MB, and the model repeats the learning process 50 times with a total of 15,266 data, performing 93.75% of the final accuracy. This system can also be linked with various services potentially for further development.

• Geomechanics and Engineering
• /
• v.12 no.5
• /
• pp.771-783
• /
• 2017

Using a transparent fracture replica with aperture size and water-cement ratio (w/c), the factors affecting the penetration behavior of rock grouting were investigated through laboratory experiments. In addition, the waterproof efficiency was estimated by the reduction of water outflow through the fractures after the grout curing process. Penetration behavior shows that grout penetration patterns present similarly radial forms in all experimental cases; however, velocity of grout penetration showed clear differences according to the aperture sizes and water-cement ratio. It can be seen that the waterproof efficiency increased as the aperture size and w/c decreased. During grout injection or curing processes, air bubbles formed and bleeding occurred, both of which affected the waterproof ability of the grouting. These two phenomena can significantly prevent the successful performance of rock grouting in field-scale underground spaces, especially at deep depth conditions. Our research can provide a foundation for improving and optimizing the innovative techniques of rock grouting.

• Journal of Ecology and Environment
• /
• v.29 no.2
• /
• pp.175-183
• /
• 2006

The discovery of deep-sea hydrothermal vents and their ecosystems is a monumental landmark in the history of Ocean Sciences. Deep-sea hydrothermal vents are scattered along the global mid-ocean ridges and back-arc basins. Under sea volcanic phenomena related to underlying magma activities along mid-ocean ridges generate extreme habitats for highly specialized communities of animals. Multidisciplinary research efforts during past three decades since the first discovery of hydrothermal vents along the Galapagos Rift in 1977 revealed fundamental components of physiology, ecology, and evolution of specialized vent communities of micro and macro fauna. Heterogeneous regional geological settings and tectonic plate history have been considered as important geophysical and evolutionary factors for current patterns of taxonomic composition and distribution of vent faunas among venting sites in the World Ocean basins. It was found that these communities are based on primary production of chemosynthetic bacteria which directly utilize reduced compounds, mostly $H_2S$ and $CH_4$, mixed in vent fluids. Symbioses between these bacteria and their hosts, vent invertebrates, are foundation of the vent ecosystem. Gene flow and population genetic studies in parallel with larval biology began to unveil hidden dispersal barrier under deep sea as well as various dispersal characteristics cross taxa. Comparative molecular phylogenetics of vent animals revealed that vent faunas are closely related to those of cold-water seeps in general. In perspective additional interesting discoveries are anticipated particularly with further refined and expanded studies aided by new instrumental technologies.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2004.11a
• /
• pp.29-34
• /
• 2004

Numerical and experimental studies on pipeline laying for intake Deep Ocean Water are carried out. In the numerical study, an implicit finite difference algorithm is employed for three-dimensional pipe equations. Fluid non-linearity and bending stiffness are considered and solved by Newton-Raphson iteration. Seabed is modeled as elastic foundation with linear spring and damper. Top tension and general configuration of pipeline at a depth are predicted. It is found that control for tension to prevent being large curvature of pipeline is needed on th steep seabed and, it should be considered 23.5 ton of tension at a top of pipe on the process of pipeline laying at 400m of water depth The largest top tension of pipe on condition of the beam sea during pipe laying is shown from the experiment. The results of this study can be contributed to the design of pipeline laying for upwelling deep ocean water.

• Journal of the Korean Geotechnical Society
• /
• v.15 no.2
• /
• pp.139-152
• /
• 1999

These days we broadly apply drilled shafts for deep foundations to build infrastructures. The defects of the deep foundations cause the decrease of their support load capacity and the increase of settlement, and the subsequent damage of the super-structures. In consequence, non-destructive testings techniques of concrete piles are important for their integrity evaluation. To improve understanding and reliable application of the impact echo method for the integrity evaluation of the drilled concrete piles, numerical studies of the impact response of concrete piles by using axi-symmetric three-dimensional finite element method are peformed for (a) sound piles: (b) piles containing necks, voids and layers of low-quality concrete: and (c) piles in soil and/or above rock. The results of these studies show that the finite element method is effective for evaluating the impact response of drilled concrete piles.

• Journal of KIBIM
• /
• v.9 no.4
• /
• pp.31-40
• /
• 2019

With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.

• Journal of Korea Society of Industrial Information Systems
• /
• v.23 no.4
• /
• pp.81-92
• /
• 2018

Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.

• Journal of Bio-Environment Control
• /
• v.30 no.4
• /
• pp.287-294
• /
• 2021

In order to expand facility agriculture and reduce greenhouse construction costs in reclaimed land, a greenhouse foundation method that satisfies economic feasibility and structural safety at the same time is required. As an alternative, the allowable bearing capacity and settlement were reviewed when the DCM(Deep cement mixing) method was applied among the soft ground reinforcement methods. To examine the applicability of the greenhouse foundation, the allowable bearing capacity and settlement were calculated by applying the theory of Terzaghi, Meyerhof, Hansen, and Schmertmann. In case of the diameter of 800mm and the width and length of the foundation of 4m, the allowable bearing capacity was 179kN/m² and the settlement was 7.25mm, which satisfies the required bearing capacity and settlement standards. The calculation results were verified through FEM(Finite element method) analysis using the Mohr-Coulomb material model. The allowable bearing capacity was 169kN/m² and the settlement was 2.52mm. The bearing capacity showed an error of 5.6% compared to calculated value, and the settlement showed and error of 65.4%. Through theoretical calculations and FEM analysis, it was confirmed that the allowable bearing capacity and settlement satisfies the design criteria as a greenhouse foundation when the width and length of the foundation were 4m. Based on the verified design values, it is expected to be able to present the foundation design criteria for greenhouses through empirical tests such as bearing capacity tests and long-term settlement monitoring.

• Journal of Gastric Cancer
• /
• v.18 no.2
• /
• pp.134-141
• /
• 2018

Purpose: Postoperative pancreatic fistula is a serious and fatal complication of gastrectomy for gastric cancer. Blunt trauma to the parenchyma of the pancreas can result from an assistant's forceps compressing and retracting the pancreas, which in turn may result in pancreatic juice leakage. However, no published studies have focused on blunt trauma to the pancreas during laparoscopic surgery. Our aim was to investigate the relationship between compression of the pancreas and pancreatic juice leakage in a swine model. Materials and Methods: Three female pigs were used in this study. The pancreas was gently compressed dorsally for 15 minutes laparoscopically with gauze grasped with forceps. Pancreatic juice leakage was visualized by fluorescence imaging after topical administration of chymotrypsin-activatable fluorophore in real time. Amylase concentrations in ascites collected at specified times was measured. In addition, pancreatic tissue was fixed with formalin, and the histology of the compressed sites was evaluated. Results: Fluorescence imaging enabled visualization of pancreatic juice leaking into ascites around the pancreas. Median concentrations of pancreatic amylase in ascites increased from 46 U/L preoperatively to 12,509 U/L 4 hours after compression. Histological examination of tissues obtained 4 hours after compression revealed necrotic pancreatic acinar cells extending from the surface to deep within the pancreas and infiltration of inflammatory cells. Conclusions: Pancreatic compression by the assistant's forceps can contribute to pancreatic juice leakage. These findings will help to improve the procedure for lymph node dissection around the pancreas during laparoscopic gastrectomy.