• 한국멀티미디어학회논문지
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• 제25권11호
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

• 한국산업융합학회 논문집
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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.

• 한국정밀공학회지
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• 제23권5호
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• pp.143-148
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• 2006

It is still very difficult to generate a geometry model and finite element model, which has complex and many free surface, even though 3D CAD solutions are applied. Furthermore, in the medical field, which is a big growth area of recent years, there is no drawing. For these reasons, making a geometry model, which is used in finite element analysis, is very difficult. To resolve these problems and satisfy the requests of the need to create a 3D digital file for an object where none had existed before, new technologies are appeared recently. Among the recent technologies, there is a growing interest in the availability of fast, affordable optical range laser scanning. The development of 3D laser scan technology to obtain 3D point cloud data, made it possible to generate 3D model of complex object. To generate CAD and finite element model using point cloud data from 3D scanning, surface reconstruction applications have widely used. In the early stage, these applications have many difficulties, such as data handling, model creation time and so on. Recently developed point-based surface generation applications partly resolve these difficulties. However there are still many problems. In case of large and complex object scanning, generation of CAD and finite element model has a significant amount of working time and effort. Hence, we concerned developing a good direct finite element model generation method using point cloud's location coordinate value to save working time and obtain accurate finite element model.

• Smart Structures and Systems
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• 제30권5호
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• 전기학회논문지
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• 제61권5호
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• pp.683-689
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• 2012

Photovoltaic(PV) system is one of power generation systems. Solar light in PV system is like the fuel of the car. The quantity of electricity generation, therefore, is fully dependent on the available quantity of solar light on the system of each site. If a utility can predict the solar power generation on a planned site, it may be possible to set up an appropriate PV system there. It may be also possible to objectively evaluate the performances of existing solar systems. Based on the theories of astronomy and meteorology, in this paper, Perez model is simulated to estimate the available quantity of solar lights on the prevailed photovoltaic systems. Consequently the conditions for optimal power generation of each PV system can be analyzed. And the maximum quantity of power generation of each system can be also estimated by applying assumed efficiency of PV system. Perez model is simulated in this paper, and the result is compared with the data of the same model of Meteonorm. Simulated site is Daejeon, Korea with typical meteorological year(TMY) data of 1991~2010.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.893-903
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• 2022

Recently, computer vision (CV)-based safety monitoring (i.e., object detection) system has been widely researched in the construction industry. Sufficient and high-quality data collection is required to detect objects accurately. Such data collection is significant for detecting small objects or images from different camera angles. Although several previous studies proposed novel data augmentation and synthetic data generation approaches, it is still not thoroughly addressed (i.e., limited accuracy) in the dynamic construction work environment. In this study, we proposed a game engine-driven synthetic data generation model to enhance the accuracy of the CV-based object detection model, mainly targeting small objects. In the virtual 3D environment, we generated synthetic data to complement training images by altering the virtual camera angles. The main contribution of this paper is to confirm whether synthetic data generated in the game engine can improve the accuracy of the CV-based object detection model.

• 물과 미래
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• 제12권2호
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• pp.63-69
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• 1979

The sequences of monthly streamflows constitute a non-statonary time series. The purely stochastic model has been applied to data generation of non-stationary time series. Tow different mothods--single site and multisite generation--have been used on the hydrologic time series. In this study the synthetic generation method by bivariate analysis, studied by Thomas Fiering, one of multi-site models, has been applied to the historical data on monthly streamflows at two sites in Nakdong River, and also for validity of this model the single site Thomas Fiering model applied. Through statistical analysis it has been shown that the performance of bivariate Thomas Fiering model was better than that of the other. By comparison of mean and standard deviaion between the historical and the generated, and cross correlogram interpretation, it has been known that the model used herein has good performance to simultaneously generate the monthly streamflows at two sites in a river hasin.

• 한국응용과학기술학회지
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• 제33권1호
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• pp.213-223
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• 2016

The validity of landfill gas models is an important problem considering that they are frequently used for landfill-site-related policy making and energy recovery planning. In this study, the Monte Carlo method was applied to an landfill gas generation model in order to enhance conformity. Results show that the relative mean deviation between measured data and modeled results (MD) decreased from 19.8% to 11.7% after applying the uncertainty range of Intergovernmental Panel on Climate Change (IPCC) to the methane-generation potential and reaction constants. Additionally, when let reaction constant adjust derived errors from all other modeling components, such as model logic, gauging waste, and measured methane data, MD decreased to 6.6% and the disparity in total methane generation quantity to 2.1%.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1234-1237
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• 1993

This paper discusses the hull form generation from fuzzy model constructed with actual ship data using fuzzy concept. SAC, which is the most important factor in the hull form generation, is expressed by a fuzzy model describing the relationships among design parameters, which have a great influence on SAC, through model identification process with the actual ship data and design parameters. Then, we can infer the SAC of an aimed ship through the process of fuzzy inference and decide the offset of a front view by making the fuzzy model between SAC and offset as well. In conclusion, this paper makes a step forward from the geometrical definition, which has been used for hull form generation so far, to direct mathematical formulae about the relationship between design parameters and offset. So, if the design parameters are given, we can generate the hull form taking such properties into account.

• 대한토목학회논문집
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• 제32권6B호
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• pp.399-406
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• 2012

본 연구의 목적은 폐기물매립지에서의 온실가스 발생량 예측모델 및 모델에 적용된 변수들의 산정방법을 개발하는 것이다. 본 연구에서는 온실가스 발생예측 모델 중 1차 반응모델의 변수인 메탄잠재발생량과 메탄발생속도상수를 평가하기 위하여 수정 Gompertz 식과 Logistic 식을 미분한 2개의 식을 적용하였다. 변수들은 실제 폐기물매립지에서의 매립가스 발생량에 대한 실측값과 예측값과의 통계학적 비교를 통해 산정하였다. 매립가스 발생량에 대한 실측값과 수정 Gompertz 식 및 Logistic 식을 미분하여 나타낸 2개의 식을 이용한 매립가스 발생량 예측값에 대한 회귀분석결과 결정계수는 각각 0.92와 0.94로 나타나, 폐기물매립지에서의 매립가스 발생량에 대한 측정값이 있을 경우 회귀분석을 통해 변수를 산정할 수 있는 것으로 나타났다. 또한 실측값이 없는 폐기물매립지에서의 온실가스 발생량을 예측할 수 있도록 하기 위하여 수정 Gompertz 식과 Logistic 식을 미분한 2개의 식을 기초로 하여 예측모델을 개발하였으며, 이 모델들의 정확성을 평가하기 위하여 Qcs(실측값):Q(예측값)의 비에 대한 빈도분포를 평가한 결과 LandGEM 모델보다 높은 정확성을 나타내었다. 따라서 본 연구에서 개발한 모델들은 폐기물매립지에서의 온실가스 발생량 예측에 적합한 것으로 사료된다.