• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.283-291
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• 2002

Since GPS's SA cancellation, the interest is converged in correction of errors such as atmospheric delay and multipath that weight had been small relatively, which can improve the accuracy of positioning through modelling research. The aim of this study have an extensive comparison of the various tropospheric delay models (Goad&Goodman, A&K, Hopfield and Sasstamoinen) and mapping functions(Niell, Chao, and Marini). Expecially, the tropospheric delay amounts by change of the GPS satellite elevations, and the delay by various combination between zenith delay models and mapping functions, compared and examined. For this, programmed the total delay models and the combined models which can be described as a product of the delay at the zenith and a mapping function. The result of study, especially, as the minimum elevation of included data is reduced under $10^{\circ}$, it was considered to be reasonable that the prediction of tropospheric delay considering combination and mapping character of functions about the transition of the zenith delay to a delay with arbitrary zenith angle.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.11-19
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• 2022

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.

• Geotechnical Engineering
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• v.10 no.1
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• pp.47-62
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• 1994

It has been well known that the rainfall-triggered rise of groundwater levels is one of the most important factors resulting the instability of the hillside slopes. Thus, the prediction of porewater pressure is an essential step in the evaluation of landslide hazard. This study involves the development and verification of numerical groundwater flow model for the prediction of groundwater flow fluctuations accounting for both of unsatu나toed flow and saturated flow on steep hillside slopes. The first part of this study is to develop a nomerical groundwater flow model. The numerical technique chosen for this study is the finitro element method in combination with the finite difference method. The finite element method is used to transform the space derivatives and the finite difference method is used to discretize the time domain. The second part of this study is to estimate the unknown model parameters used in the proposed numerical model. There were three parameters to be estimated from input -output record $K_e$, $\psi_e$, b. The Maximum -A-Posteriori(MAP) optimization method is utilized for this purpose, . The developed model is applied to a site in Korea where two debris avalanches of large scale and many landslides of small scale were occurred. The results of example analysis show that the numerical groundwater flow model has a capacity of predicting the fluctuation of groundwater levels due to rainfall reasonably well.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.6 no.3
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• pp.43-49
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• 1992

Forecasting of electric power demand has been a basic element for electric power system operation and system development, and it's accuracy has very strong influence on reliability and economical efficience of power supply. So, in this paper, long―term maximum electric power demand has been forecasted by using the triple exponential smoothing method initiated R.G.Brown. It has been regarded this method as high accuracy and operational convenience. The smoothing function is a liner combination of all past observations and the weight given to previous observations decreases geometrically with age.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.23-29
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• 2017

Due to the success of Wealthfront, Betterment, etc., there is a growing interest in RoboAdvisor that is an automated asset allocation methodology globally. RoboAdvisor minimizes human involvement in managing assets, thereby reducing the costs of using services and eliminating human psychological factors. In this paper, we developed a predictive model for the KOSPI 200 Futures Index using deep learning, in order to replace the existing technical analysis technique. And the proposed model confirmed that When the KOSPI 200 Gift Index is small, it can be used to predict direction and price of index. In combination with the existing technical analysis, It is confirmed that the proposed models combining with existing technical analyses and can be applied to the RoboAdvisor Service in the future.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.487-494
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• 2019

Recently, interest in and use of drones is increasing. In this study, to provide accurate wind prediction at ultra low altitudes of 150 meters or below, the sensitivity of the physical process parameterization and initial conditions was assessed to select the optimal physical process and initial conditions. For this purpose, GFS and LDAPS data were used as initial and boundary conditions, and 7 experiments were constructed using a combination of PBL schemes such as YSU, RUC, ACM2, and LSM such as Noah, RUC, and Pleim. The experiment conducted for 1 month in April 2018. As a result, the RUC-YSU physical process combination using the GFS initial data showed the best performance. This study is meaningful in establishing an optimal modeling method for ultra low altitude wind prediction through experiments using different initial conditions and combination of physical processes.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.119-126
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• 2024

In building information model (BIM), it is difficult to train an artificial intelligence (AI) model due to the lack of sufficient data about individual projects in an architecture firm. In this paper, we present a methodology to correctly train an AI neural network model based on a large language model (LLM) to predict the steel structure product weight ratios in BIM. The proposed method, with the aid of the LLM, can overcome the inherent problem of limited data availability in BIM and handle a combination of natural language and numerical data. The experimental results showed that the proposed method demonstrated significantly higher accuracy than methods based on a smaller language model. The potential for effectively applying large language models in BIM is confirmed, leading to expectations of preventing building accidents and efficiently managing construction costs.

• The Journal of the Acoustical Society of Korea
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• v.31 no.6
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• pp.391-398
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• 2012

In this paper, prediction of centrifugal fan was conducted through combination the hybrid CAA method which was used to predict the fan noise with the FRPM technique which was used to generate the broadband noise source. Firstly, flow field surround the centrifugal fan was computed using the RANS equations and noise source region was deducted from the computed flow field. Then the FRPM technique was applied to the source region for generation of turbulence which satisfies the stochastic features. The noise source of the centrifugal fan was modeled by applying the acoustic analogy to the synthesized flow field from the computed and generated flow fields. Finally, the broadband noise of the centrifugal fan was predicted through combination the modeled noise source with the linear propagation which was realized using the boundary element method. It was confirmed that the proposed technique is efficient to predict the tonal and broadband noises of centrifugal fan through comparison with the measured data.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2291-2297
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• 2017

This paper describe to extract speech measure algorithm for evaluating a speech database, and presents generating method of a speech quality measure using DNN(Deep Neural Network). In our previous study, to produce an effective speech quality measure, we propose a combination of various speech measures which are highly correlated with WER(Word Error Rate). The new combination of various types of speech quality measures in this study is more effective to predict the speech recognition performance compared to each speech measure alone. In this paper, we describe the method of extracting measure using DNN, and we change one of the combined measure from GMM(Gaussican Mixture Model) score used in the previous study to DNN score. The combination with DNN score shows a higher correlation with WER compared to the combination with GMM score.