• Journal of Navigation and Port Research
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• 제27권2호
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• pp.111-119
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• 2003

In order to improve navigational safety of ships, an ocean wave prediction model of high precision within a short time, dealing with multi-directional random waves from the information of the sea surface winds encountered at the planned ship's course, was introduced for construction of ocean wave forecasting system on the ship. In this paper, we investigated a sea disaster occurred by a stormy weather in the past. We analyzed the sea surface wind first and then carried out ocean wave hindercasting simulations according to the routes the sunken vessel. From the result of this study, we concluded that the sea disaster was caused by rapidly developed iou pressure system Okhotsk Sea and the predicted values by the third generation wave prediction model(WAM) was agreed well with the observed significant wave height, wave period, and directional wave spectrum. It gives a good applicability for construction of a practical on-board calculation system.

• Journal of the Korean Solar Energy Society
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• 제30권6호
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• pp.66-72
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• 2010

In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제36권3A호
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• pp.205-214
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• 2011

In 3rd generation project partnership long term evolution, wireless multicast techniques which send the same data to multiple users under single frequency networks have attracted much attention. In the multicast system, the transmission mode needs to be selected for efficient data transfer while satisfying the multicast coverage requirement. To achieve this, users' channel state information (CSI) should be available at the transmitter. However, it requires too much uplink feedback resource if all the users are allowed to transmit their CSI at all the time. To solve this problem, in this paper, the multicast coverage prediction is suggested. In the proposed algorithm, each user measures its transition probabilities between the success and the fail state of the decoding. Then, it periodically transmits its CSI to the basestation. Using these feedbacks, the basestation can predict the multicast coverage. From the simulation results, we demonstrate that the proposed scheme can predict the multicast system coverage.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 한국항해항만학회 2003년도 춘계공동학술대회논문집
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• pp.147-156
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• 2003

In order to improve navigational safety of ships, on ocean wave prediction model of high precision within a short time, dealing with multi-directional random waves from the information of the sea surface winds encountered at the planned ship's course, was introduced for construction of ocean wave forecasting system on the ship. In this paper, we investigated a sea disaster occurred by a stormy weather in the past. We analyzed the sea surface winds first and then carried out ocean wave hindercasting simulations according to the routes of the sunken vessel. From the result of this study, we concluded that the sea disaster was caused by rapidly developed low pressure system in Okhotsk Sea and the predicted values by the third generation wave prediction model(WAM) was agreed well with the observed significant wave height, was period, and directional wave spectrum. It gives a good applicability for construction of a practical on-board calculation system.

• KIISE Transactions on Computing Practices
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• 제20권12호
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• pp.694-699
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• 2014

In mobile ad-hoc networks (MANET), the network topology and neighboring nodes change frequently, since MANET is composed of nodes that have mobility without a fixed network infrastructure. The AODV routing protocol is advantageous for MANET, but AODV has a delay in the transmission of data packets because AODV can not transmit data during route recovery. This paper proposes solving the above problem of AODV by using a bypass generation mechanism for data transmission during route recovery. For further improvement, additional mechanisms that coordinate the reception threshold of a hello packet are proposed in order to improve the accuracy of the information obtained from the neighboring nodes when the bypass is generated due to a link failure and the immediacy of the route recovery. Simulation results show that the proposed technique improves the performance in terms of the delay in transmission compared to traditional AODV.

• KIPS Transactions on Software and Data Engineering
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• 제8권5호
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• pp.205-212
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• 2019

Scene graph is a kind of knowledge graph that represents both objects and their relationships found in a image. This paper proposes a 3D scene graph generation model for three-dimensional indoor environments. An 3D scene graph includes not only object types, their positions and attributes, but also three-dimensional spatial relationships between them, An 3D scene graph can be viewed as a prior knowledge base describing the given environment within that the agent will be deployed later. Therefore, 3D scene graphs can be used in many useful applications, such as visual question answering (VQA) and service robots. This proposed 3D scene graph generation model consists of four sub-networks: object detection network (ObjNet), attribute prediction network (AttNet), transfer network (TransNet), relationship prediction network (RelNet). Conducting several experiments with 3D simulated indoor environments provided by AI2-THOR, we confirmed that the proposed model shows high performance.

• The Journal of the Korea institute of electronic communication sciences
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• 제16권4호
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• pp.735-742
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• 2021

Recently, renewable energy-related power generation facilities have been surging due to the government's "Renewable Energy 3020", "Green New Deal", "2050 Carbon Neutrality" and "K-RE100" policies. Most renewable energy facilities are small and distributed, making it difficult to manage efficiently, and small distributed resources less than 1MW are having a hard time with participating in the market due to the limited sales and avoidance of trading. In particular, the intermittency of renewable energy has a significant impact on the stability of the power grid. The government is seeking to address volatility and intermittency issues through 'small distributed resource brokerage trading, and to expand the systematic resourceization and acceptability of heterogeneous large and small distributed resources. In this work, we intend to apply an AI-based power generation prediction model to a distributed resource brokerage trading system so that it can be utilized as a foundation platform for pioneering new energy business markets.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2011년 춘계학술대회논문집
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• pp.362-367
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• 2011

The aerodynamic performance of the pantograph of the next generation high sped train is analyzed. The calculation of the flow around pantograph is carried cut by FLUENT; by the steady state flow calculation with ${\kappa}-{\omega}$ SST turbulence model, the lift force of the pantograph is computed. For the verification of the numerical schemes am grid systems, flow calculations are performed with the pantograph shape which was used at the experiments performed at Railway Technical Research Institute (RTRI) in Japan. Then, the difference of lift force between numerical am experimental results is about 10%. Therefore, selected numerical schemes and the current grid system is adequate for the analysis am prediction of the aerodynamic performance of panthograph system. Based on these numerical schemes am grid system, the flow around pantograph of the next generation high sped train is calculated and the lift force of the pantograph is predicted; the lift force of the pantograph is about 146N.

• The Transactions of the Korean Institute of Electrical Engineers P
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• 제64권1호
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• pp.1-6
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• 2015

Factors that have influence on solar power generation are specified into three aspects such as meteorological, geographical factors as well as equipment installation. Meteorological factors influence the most among the three. Insolation, sunshine hours, and cloud directly influence on solar power generation, whereas temperature and wind speed have impacts on equipment installation. This paper provides explanation over temperature-wind speed equation by calculating influence of temperature and wind speed on equipment installation. In order to conduct a research, pyranometer, anemometer, air thermometer, module thermometer are installed in 2MWp solar power plant located in South Cholla province, so that real-time meteorological data and generating amount can be analyzed through monitoring system. Besides, if existing and new methods are applied together, accuracy of prediction for generating amount is improved.

• Journal of the Korean Society of Industry Convergence
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• 제26권4_2호
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.