• New & Renewable Energy
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• v.3 no.4
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• pp.47-53
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• 2007

Effluent ground water overflow in deep and broad ground space building. Temperature of effluent ground water is in $12{\sim}20^{\circ}C$ annually and the quality of that water is as good as living water. Therefore if the flow rate of effluent ground water is sufficient as source of heat pump, that is good heat source and heat sink of heat pump. Effluent ground water contain the thermal energy of surrounding ground. So this is a new application of ground source heat pump. In this study open type and close type heat pump system using effluent ground water was installed and tested for a church building with large and deep ground space. The effluent flow rate of this building is $800{\sim}1000ton/day$. The heat pump capacity is 5RT each. The heat pump cooling COP is $4.9{\sim}5.2$ for the open type and $4.9{\sim}5.7$ for close type system. The system cooling COP is $3.2{\sim}4.5$ for open type and $3.8{\sim}4.2$ for close type system. This performance is up to that of BHE type ground source heat pump.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.471-476
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• 2007

Effluent ground water overflow in deep and broad ground space building. Temperature of effluent ground water is in $12{\sim}20^{\circ}C$ annually and the quality of that water is as good as living water. Therefore if the flow rate of effluent ground water is sufficient as source of heat pump, that is good heat source and heat sink of heat pump. Effuent ground water contain the thermal energy of surrounding ground. So this is a new application of ground source heat pump. In this study open type and c lose type heat pump system using effluent ground water was installed and tested for it church building with large and deep ground space. The effluent flow rate of this building is $800{\sim}1000$ ton/day. The heat pump capacity is 5RT each. The heat pump cooling COP is $4.9{\sim}5.2$ for the open type and $4.9{\sim}5.7$ for close type system. The system cooling COP is $3.2{\sim}4.5$ for open type and $3.8{\sim}4.2$for close type system. This performance is up to that of BHE type ground source heat pump.

• New & Renewable Energy
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• v.3 no.2 s.10
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• pp.40-46
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• 2007

Effluent ground water overflow in deep and broad ground space building. Temperature of effluent ground water is in $12{\sim}20^{\circ}...$ annually and the quality of that water is as good as well water. Therefore if the flow rate of effluent ground water is sufficient as source of heat pump, that is good heat source and heat sink of heat pump. Effuent ground water contain the thermal energy of surrounding ground. So this is a new application of ground source heat pump. In this study open type and close type heat pump system using effluent ground water was installed and tested for a church building with large and deep ground space. The effluent flow rate of this building is $800{\sim}1000\;ton/day$. The heat pump capacity is 5RT. The heat pump heating COP was $3.85{\sim}4.68$ for the open type and $3.82{\sim}4.69$ for the close type system. The system heating COP including pump power is $3.0{\sim}3.32$ for the open type and $3.32{\sim}3.84$ for close type system. This performance is up to that of BHE type ground source heat pump.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2002.05a
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• pp.243-250
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• 2002

According to Ocean Korea 21, a basic plan established by the Ministry of Maritime Affairs and Fisheries (MOMAF) of Korea in May 2000, Korea Research Institute of Ships and Ocean Engineering (KRISO) proposed a program for the development of a deep-sea unmanned underwater vehicle (UUV) to explore deep sea for scientific purpose. KRISO has launched a project in May 2001 under the support of MOMAF. The deep-sea unmanned underwater vehicle will be applied to scientific researches in deep-sea as well as in shallow water. For operation of underwater vehicles in shallow water near the Korean Peninsula, a special design is required because of strong tidal current. In addition, MOMAF requires the vehicle to be designed for the purpose of long range survey, a long-term observation, and precise works in a specific area. Thus, KRISO has planned to design the system with the functional combination of both ROV and AUV. This paper presents the design of the deep-sea unmanned underwater vehicle.

• Journal of Ocean Engineering and Technology
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• v.26 no.6
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• pp.86-92
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• 2012

An exploration well is drilled where oil or gas potential is shown by a seismic survey and interpretation. With the advance of drilling technology, most of the easily accessible oil had been developed by the end of the 20th century. To satisfy the ever increasing demand for oil, and bolstered by high oil prices, the major oil companies started to drill in deep water, which requires a deep offshore drilling unit. Offshore drilling units are generally classified by their maximum operating water depth. Many semi-submersible rigs have been purpose-designed for the drilling industry as the allowable drilling water depth has become deeper by the developed technics since the first semi-submersible was launched in 1963. Semi-submersible rigs are commonly used for shallow to deep water up to 3,000 m. Drilling equipment such as a top drive, blowout preventer, drawworks and power system, mud circulation system, and subsea wellhead system are explained to help with an understanding of offshore drilling procedures in the oil and gas fields. The objective of this paper is to introduce the main components of a semi-submersible rig and, by doing so, to raise the awareness of offshore drilling, which accounts for over 30% of the total oil production and will continue to increase.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.206-214
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• 2022

The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Journal of Navigation and Port Research
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• v.42 no.1
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• pp.9-16
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• 2018

The development of limited petroleum resources for use with mankind inevitably explores and seeks to develop oil fields in the deep sea area, under the rise of the oil prices market situation. The use of Oceanic Thermal Energy Conversion (OTEC) technology, which operates the power generation facility using the temperature differences between the deep water and the surface water, is progressing actively as a trend to follow. In this study, the application of the Discharged Thermal Energy Conversion (DTEC) was designed and analyzed under the condition that the supply condition of seawater used in the FPSO installed in the deep sea area is changed up to 400m depth. In this case, it was confirmed that the design of the system that can generate more electric power according to the depth of water is confirmed, by thus applying the DTEC system by taking the cooling water at a deeper water depth than the existing design water depth. The FPSO considers the similarity of the OTEC power generation facilities, and will apply the DTEC system to FPSO in the deep sea area to accumulate technology and the conversion to further utilize the OTEC power generation facilities after the end of life cycle of oil production, which could be a solution to two important issues, namely, resource development and sustainable development.

• Journal of Korean Society on Water Environment
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• v.37 no.4
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• pp.275-285
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• 2021

Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.133-139
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• 2023

This paper proposes a two-stage Water Shield Automation System (WSAS) to predict the possibility of river overflow and vehicle flooding due to sudden rainfall. The WSAS uses a two-stage Deep Neural Network (DNN) model. First, a river overflow prediction module is designed with LSTM to decide whether the river is flooded by predicting the river's water level rise. Second, a vehicle flooding prediction module predicts flooding of underground parking lots by detecting flooded tires with YOLOv5 from CCTV images. Finally, the WSAS automatically installs the water barrier whenever the river overflow and vehicle flooding events happen in the underground parking lots. The only constraint to implementing is that collecting training data for flooded vehicle tires is challenging. This paper exploits the Image C&S data augmentation technique to synthesize flooded tire images. Experimental results validate the superiority of WSAS by showing that the river overflow prediction module can reduce RMSE by three times compared with the previous method, and the vehicle flooding detection module can increase mAP by 20% compared with the naive detection method, respectively.