• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.45-51
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• 2013

Today, people who live in sea of information are strongly appearing desire about quicker and more accurate information. For a long time people wanted to know information about place that I am and where I must go out, and there are various methods to have a keen desire for position information. Equipment that is using most among the method is digital camera. In this study, the accuracy of external orientation, GCP and check point depending on array of GCP and regional feature are analyzed after AT(aerial triangulation) with UltraCamX in three selected study area with specific feature. As analysis result, we could get to know that area with a mountainous district rapidly decreased accuracy of external orientation according as number of GCP decreases, and area with high buildings became low in vertical accuracy of checkpoint. This study has performed the analysis of regional factors in aerial triangulation accuracy.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.375-383
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• 2018

In order to apply the Network RTK (real time kinematics) technology, which has been used for positioning of stationary points, to the navigation of vehicles, its infrastructure should provide correction data with a quality indicator that can show the expected accuracy in real time. In this paper, we analyzed various indicator generation algorithms such as I95 (ionospheric index 95) / G95 (geodetic index 95), SBI (semivariance based index) and RIU (residual interpolation uncertainty). We also applied them to the raw observables from the reference stations of National Geographic Information Institute and VRS (virtual reference station) users, and then examined its feasibility to be used as a real-time performance index of the Network RTK rover. 24 hour data analysis shows that the RIU index, which can represent the non-linearty of the correction, has the strongest correlation with the Network RTK rover accuracy. Therefore, RIU index is expected to be used as a real-time performance index of the Network RTK rover.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.3-10
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• 2015

The various studies and applications for UAVS(Unmaned Aerial Vehicle System) have been recently increased as a new technology to create 3D spatial information rapidly and accurately. UAV(Unmanned Aerial Vehicle) is economical when comparing with conventional technique, such as satellite and aerial survey, and can quickly obtain high resolution data under 5cm. This paper examined the utilizing possibility to creating 3D spatial information and analysis the compatibility the UAV data obtained by non-metric digital camera with conventional numerical photogrammetric system. The DEM and normal orthophoto is created by exclusive S/W and DPW(Digital Photogrammetry Workstation) then analysis the accuracy of created data. As a result, the accuracy of the created DEM and normal orthophoto, which is obtained by UAV then processed by DPW, is not satisfied;so it is estimated that the compatibility the UAV data with conventional numerical photogrammetric system is low.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.65-70
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• 2011

National Geographic Information Institute provides VRS service using permanent GPS networks. VRS-RTK(Virtual Reference System-Real Time Kinematic)GPS surveying which enable to accomplish the real time-based GPS surveying has been increasingly popular. However the positioning accuracy tends to deteriorate as the distance between the rover and base station increases in the VRS-RTK GPS surveying. To analysis this problem in this study, the accuracy of VRS-RTK data was analyzed with 2 different test sites of Geoje city, Gyeongnam province within and without the permanent GPS networks in order to accomplish the cadastral supplementary control surveying. As a result of surveying accuracy analysis at two test sites, positioning errors were ${\pm}0.03m$(RMSE) in both sites. The result was that within the tolerance specified in cadastral surveying law, and indicated the possibility of VRS-RTK GPS surveying in cadastral surveying.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Convergence Security Journal
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• v.20 no.3
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• pp.53-60
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• 2020

At the present stage of the fourth industrial revolution, machine learning and artificial intelligence technologies are rapidly developing, and there is a movement to apply machine learning technology in the security field. Malicious code, including new and transformed, generates an average of 390,000 a day worldwide. Statistics show that security companies ignore or miss 31 percent of alarms. As many malicious codes are generated, it is becoming difficult for humans to detect all malicious codes. As a result, research on the detection of malware and network intrusion events through machine learning is being actively conducted in academia and industry. In international conferences and journals, research on security data analysis using deep learning, a field of machine learning, is presented. have. However, these papers focus on detection accuracy and modify several parameters to improve detection accuracy but do not consider the ratio of dataset. Therefore, this paper aims to reduce the cost and resources of many machine learning research by finding the ratio of dataset that can derive the highest detection accuracy in CNN Mobile net-based malware detection model.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.31-38
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• 2021

In this paper, in order to obtain the optimization of the RNN model used for sentiment analysis, the correlation of each model was studied by observing the trend of loss and accuracy according to hyperparameter tuning. As a research method, after configuring the hidden layer with LSTM and the embedding layer that are most optimized to process sequential data, the loss and accuracy of each model were measured by tuning the unit, batch-size, and embedding size of the LSTM. As a result of the measurement, the loss was 41.9% and the accuracy was 11.4%, and the trend of the optimization model showed a consistently stable graph, confirming that the tuning of the hyperparameter had a profound effect on the model. In addition, it was confirmed that the decision of the embedding size among the three hyperparameters had the greatest influence on the model. In the future, this research will be continued, and research on an algorithm that allows the model to directly find the optimal hyperparameter will continue.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.2
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• pp.83-92
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• 2022

This paper analyzes the performance of the IRA(Interferometric Radar Altimeter) by terrain type for estimating reliability of TRN(Terrain Referenced Navigation). The accuracy of the altitude is one of the key parameters of TRN's accuracy. When the antenna of the IRA has wide beamwidth, its altitude accuracy is directly affected by the configuration of the earth's surface. Hence, the accuracy and reliability of TRN can also be affected and may cause ambiguity in positioning. We present analysis data for estimating the reliability of TRN by modeling several topographies and analyzing the performance of the IRA. The results of the analysis are verified by comparison with test data.

• Smart Structures and Systems
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• v.31 no.2
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• pp.101-111
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• 2023

Structural health monitoring (SHM) covers a range of damage detection strategies for buildings. In real-time, SHM provides a basis for rapid decision making to optimise the speed and economic efficiency of post-event response. Previous work introduced an SHM method based on identifying structural nonlinear hysteretic parameters and their evolution from structural force-deformation hysteresis loops in real-time. This research extends and generalises this method to investigate the impact of a wide range of flag-shaped or pinching shape nonlinear hysteretic response and its impact on the SHM accuracy. A particular focus is plastic stiffness (K_p), where accurate identification of this parameter enables accurate identification of net and total plastic deformation and plastic energy dissipated, all of which are directly related to damage and infrequently assessed in SHM. A sensitivity study using a realistic seismic case study with known ground truth values investigates the impact of hysteresis loop shape, as well as added noise, on SHM accuracy using a suite of 20 ground motions from the PEER database. Monte Carlo analysis over 22,000 simulations with different hysteresis loops and added noise resulted in absolute percentage identification error (median, (IQR)) in K_p of 1.88% (0.79, 4.94)%. Errors were larger where five events (Earthquakes #1, 6, 9, 14) have very large errors over 100% for resulted K_p as an almost entirely linear response yielded only negligible plastic response, increasing identification error. The sensitivity analysis shows accuracy is reduces to within 3% when plastic drift is induced. This method shows clear potential to provide accurate, real-time metrics of non-linear stiffness and deformation to assist rapid damage assessment and decision making, utilising algorithms significantly simpler than previous non-linear structural model-based parameter identification SHM methods.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.