• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.181-186
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• 2003

As the approach close-range digital photogrammetry has a variety of merits, the application of precision requiting fields is in Increase for its scope expansion. In the meantime, in case of photographic surveying by use of films, a lot of studies on experiment analysis and theoretical forecast models about a change of the exactness as per photographing coordinates have been conducted, but experiments about approach close-range digital photogrammetry are not enough yet. In consequence, this study has made photographing respectively by changing the photographic distance, converging angle, picturing direction by use of Rollei d7 metric and d7 metric$\^$5/ that is a measurement digital camera. And also in order to minimize the errors happened at the relative orientation, we have sorted out the prototype target that the relative orientation is automatically on the programming and have calculated RMSE by carrying out the bundle adjustment. We think that such a study could be used as very important basic data necessary in deriving the optimal photographic conditions by the close-range digital photogrammetry and in judging such a degree.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1881-1888
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• 2011

Red tide is a natural phenomenon to bloom harmful algal, which fish and shellfish die en masse. Red tide damage with respect to sea farming has been occurred each year. Red tide damage can be minimized by means of prediction of red tide blooms. Red tide prediction using naive bayes classifier can be achieve good prediction results. The result of naive bayes method only determine red tide blooms, whereas the method can not know how increasing of red tide algae density. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning and naive bayes classifier. The proposed method can enhance the precision of red tide prediction and forecast the increasing density of red tide algae.

• Journal of the Korean Society of Safety
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• v.31 no.1
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• pp.105-110
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• 2016

Mountainous disasters such as landslides and debris flow are difficult to forecast. Debris flow in particular often flows along the valley until it reaches the road or residential area, causing casualties and huge damages. In this study, the researchers selected Seoraksan National Park area located at Inje County (Inje-gun), Gangwon Province-where many mountainous disasters occur due to localized torrential downpours-for the damage reduction and cause analysis of the area experiencing frequent mountainous disasters every year. Then, the researchers conducted the field study and constructed geospatial information data by GIS method to analyze the characteristics of the disaster-occurring area. Also, to extract more precise geographic parameters, the researchers scanned debris flow triggering area through terrestrial LiDAR and constructed 3D geographical data. LiDAR geographical data was then compared with the existing numerical map to evaluate its precision and made the comparative analysis with the geographic data before and after the disaster occurrence. In the future, it will be utilized as basic data for risk analysis of mountainous disaster or disaster reduction measures through a fine-grid topographical map.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.74-78
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• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.05a
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• pp.539-542
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• 2008

Recently, GMP(Glass Molding Press) process is mainly used to produce aspheric glass lenses. Because glass lens is heated at high temperature above Ty (yielding point) for forming glass, the quality of aspheric glass lens is deteriorated by residual stresses which are generated in a aspheric glass lens after forming. Before this study, as a fundamental study to develop forming conditions for progressive GMP process, compression, strain relaxation and thermal conductivity tests were carried out to obtain the visco-rigid plastic, the visco-elastic and thermal properties of K-PBK40 which is newly developed and applied for precision molding glass material, In this study, using the experimental results we obtained, a glass lens forming simulation in progressive GMP process was carried out and we could forecast the shape of deformed glass lenses and residual stresses contribution in the structure of deformed glass lenses after forming.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.220-228
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• 2022

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.547-559
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• 2022

For a chi-squared test, which is a statistical method used to test the independence of a contingency table of two factors, the expected frequency of each cell must be greater than 5. The percentage of cells with an expected frequency below 5 must be less than 20% of all cells. However, there are many cases in which the regional expected frequency is below 5 in general small area studies. Even in large-scale surveys, it is difficult to forecast the expected frequency to be greater than 5 when there is small area estimation with subgroup analysis. Another statistical method to test independence is to use the Bayes factor, but since there is a high ratio of data dependency due to the nature of the Bayesian approach, the low expected frequency tends to decrease the precision of the test results. To overcome these limitations, we will borrow information from areas with similar characteristics and pool the data statistically to propose a pooled Bayes test of independence in target areas. Jo et al. (2021) suggested hierarchical Bayesian pooling models for small area estimation of categorical data, and we will introduce the pooled Bayes factors calculated by expanding their restricted pooling model. We applied the pooled Bayes factors using bone mineral density and body mass index data from the Third National Health and Nutrition Examination Survey conducted in the United States and compared them with chi-squared tests often used in tests of independence.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.59-66
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• 2023

The Golden Cross is commonly seen as a buy signal in financial markets, but its reliability for predicting stock price movements is limited due to market volatility. This paper introduces a time-invariant approach that considers the Golden Cross as a singular event. Utilizing LSTM neural networks, we forecast significant stock price changes following a Golden Cross occurrence. By comparing our approach with traditional time series analysis and using a confusion matrix for classification, we demonstrate its effectiveness in predicting post-event stock price trends. To conclude, this study proposes a model with a precision of 83%. By utilizing the model, investors can alleviate potential losses, rather than making buy decisions under all circumstances following a Golden Cross event.

• Wind and Structures
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• v.38 no.2
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• pp.147-160
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• 2024

The aerodynamic force is a significant component that influences the stability and safety of structures. It has unstable properties and depends on computer precision, making its long-term prediction challenging. Accurately estimating the aerodynamic traits of structures is critical for structural design and vibration control. This paper establishes an unsteady aerodynamic time series prediction model using Long Short-Term Memory (LSTM) network. The unsteady aerodynamic force under varied Reynolds number and angles of attack is predicted by the LSTM model. The input of the model is the aerodynamic coefficients of the 1 to n sample points and output is the aerodynamic coefficients of the n+1 sample point. The model is predicted by interpolation and extrapolation utilizing Unsteady Reynolds-average Navier-Stokes (URANS) simulation data of flow around a circular cylinder, square cylinder and airfoil. The results illustrate that the trajectories of the LSTM prediction results and URANS outcomes are largely consistent with time. The mean relative error between the forecast results and the original results is less than 6%. Therefore, our technique has a prospective application in unsteady aerodynamic force prediction of structures and can give technical assistance for engineering applications.